DESIGNING AN INTELLIGENT TUTORING SYSTEM MODEL FOR AFAAN
OROMOO
MASTER’S THESIS
BY:
ABDISA KEDIRO
Feb, 2021
ARBA MINCH, ETHIOPIA
DESIGNING AN INTELLIGENT TUTORING SYSTEM MODEL FOR AFAAN
OROMOO
BY:
ABDISA KEDIRO
A THESIS SUBMITTED TO THE FACULTY OF COMPUTING AND SOFTWARE
ENGINEERING
ARBA MINCH INSTITUTE OF TECHNOLOGY
SCHOOL OF GRADUATE STUDIES
ARBA MINCH UNIVERSITY
ARBA MINCH, ETHIOPIA
IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF
MASTER OF SCIENCE IN COMPUTER SCIENCE
DECLARATION
I, the undersigned, declare that this thesis entitled with “designing an intelligent tutoring system model for
Afaan Oromoo ” is my original work and has not been presented /submitted to any of the university as a
partial requirement for a degree of Computer Science or related discipline and all source of materials used
in the thesis have been duly cited/ acknowledged.
ABDISA KEDIRO
Signature ______________
Date __________________
I
ARBA MINCH UNIVERSITY
SCHOOL OF GRADUATE STUDIES
ADVISORS’ APPROVAL SHEET
This is to certify that the thesis entitled that “Designing an Intelligent Tutoring System Model Using Afaan
Oromoo” submitted in partial fulfilment of the requirement for the Degree of Master in Computer Science,
at faculty of Computing and Software Engineering, Arba Minch Institute of Technology, Arba Minch
University has been carried out by Abdisa Kediro Dedefo, under my supervision.
I, therefore, recommend that the student has fulfilled all the necessary requirements, and hence the thesis
can be submitted to the school of Graduate Studies for defense.
Prof. Durga Prasad Sharma _______
Name of Principal Advisor
Bikila Alemu
Name of Co-Advisor
________________
Signature
________________
Signature
II
____________
Date
____________
Date
ARBA MINCH UNIVERSITY
SCHOOL OF GRADUATE STUDIES
EXAMINERS’ APPROVAL SHEET
We, the undersigned, members of the board of examiners of the final open defense by Abdisa Kediro
Dedefo have read and evaluated his/her thesis entitled “Designing an Intelligent Tutoring System Model
Using Afaan Oromoo”, and examined the candidate. This is therefore to certify that the thesis has been
accepted in partial fulfillment of the requirement for the degree of Master of Science in Computer Science.
Approved by:
__________________________
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__________
Chairperson
Signature
Date
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External Examiner
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Internal Examiner
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Principal Advisor
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III
Dedication
I dedicate this project to Allah Almighty my creator, my strong pillar, my source of inspiration, wisdom,
knowledge and understanding. He has been the source of my strength throughout this program and on His
wings, only have I soared. I also dedicate this work to my family, who has encouraged me all the way and
whose encouragement has made sure that I give it all it takes to finish that which I have started.
IV
Acknowledgement
First and foremost, I would like to express my deepest and sincere gratitude to Allah for the grace,
blessings, and help given during my research and throughout my life. I also extend my sincere gratitude
and respect to my advisor Prof. Durga Prasad Sharma and Co-Advisor Bikila to support and
encourage my study. His patience, motivation, enthusiasm, and immense knowledge turned my dream
into reality. His guidance helped me in all the time of my research journey and writing this research thesis.
I would like to forward my gratefulness to my family members, especially my lovely mother for their
love, support, and encouragement during the writing of this research thesis. I express my deep-hearted
love for you all of them. I wish to express my love and gratitude to all my friends and colleagues working
at Arba Minch University for their cooperation, encouragement, and support they provided for the
accomplishment of this task in time.
Finally, I would like to thank all the people who helped me directly or indirectly during data collection,
especially those who gave me their precious time during the interview and survey and those who helped
me collect the questionnaire. Once again, I express my heartfelt thanks to all.
V
Abstract
Intelligent Tutoring System (ITS) is an intelligent computer system design aimed to provide tutoring
services and support the learner’s community on a specific subject through customized instructions and
support guidelines for interacting with learners through localized language-based support and feedback.
In Ethiopia, the existing state of art systems of teaching and learning are still lagging behind in terms of
adaptability, ease of guidance, interaction, instant support, anywhere, anytime over any device access,
digitalization, and personalized learning. Also, the applications of intelligent systems with localized
customization and contextualization are not widely adopted. Hence, the learner’s community is lacking
behind for such technology-enabled solutions towards improving their learning capabilities. The
intelligent language tutoring system is also one of the applications for providing tutoring support to
language learners. Afaan Oromo is one of the major languages that is widely spoken and used in Ethiopia.
As a primary observation, the traditional way of teaching and learning Afaan Oromoo has several
challenges and limitations. This research study proposed an intelligent tutoring system model (ITS) for
providing tutoring support and services while learning Afaan Oromoo using text-based interaction for the
localized application context. The designed ITS model has three domains with different components: the
student domain, tutoring domain, and knowledge domain. The student domain uses a Model tracing
algorithm to follow and track students’ behavior while they use the system, and Bayesian knowledge
tracing is used to estimate student knowledge status and used to update student status in the student domain
in the proposed model. The tutoring domain uses a simple Bayesian network with a direct acyclic graph
to model topic dependence in Afaan Oromo. The production rule is used to represent the knowledge
domain of the proposed model. The proposed model can provide a tremendous instrumental for supporting
the Afaan Oromoo. The proposed ITS model provides individualized feedback and supports the
individual’s linguistic mistakes while learning in the text interaction formats. The prototype s developed
for the newly proposed ITS model using Cognitive Tutor Authoring Tool (CTAT). The user acceptance
test has also been done to check the acceptance status of the research outcomes. The acceptance test clearly
indicated a high level of acceptance, i.e., 85.7 % of the model upon the demo of the prototype. This implies
that the ITS model can be a new knowledge contribution to the domain towards the betterment of
educational systems in Ethiopia, especially learning Afaan Oromo. The study concludes that ITS solution
can be a great instrumental for supporting education and the Afaan Oromo through the intervention and
application of Artificial Intelligence.
Keyword: Intelligent Tutoring System, Model, Student domain, Tutoring domain, Artificial Intelligence
VI
Contents
Dedication ................................................................................................................................................... IV
Acknowledgment ......................................................................................................................................... V
Abstract ....................................................................................................................................................... VI
List of Figures .............................................................................................................................................. X
List of Tables .............................................................................................................................................. XI
List of Acronyms ....................................................................................................................................... XII
CHAPTER 1 ................................................................................................................................................. 1
1.
INTRODUCTION ................................................................................................................................ 1
1.1 Background ......................................................................................................................................... 1
1.1.1 E-Learning ................................................................................................................................................ 3
2.1.2 Adaptive Learning .................................................................................................................................... 4
1.2 Statement of the Problem & Motivation ................................................................................................. 4
1.3 Research Motivation ............................................................................................................................... 6
1.4 Research Question .................................................................................................................................. 7
1.5. Objective ................................................................................................................................................ 7
1.5.1 General Objective ............................................................................................................................ 7
1.5.2 Specific Objectives .......................................................................................................................... 7
1.6 The significance of the study .................................................................................................................. 7
1.7 Scope and Limitation of the study .......................................................................................................... 8
1.8. Beneficiaries .......................................................................................................................................... 8
1.9 Research/Thesis Document Organization ............................................................................................... 8
CHAPTER 2 ............................................................................................................................................... 10
LITERATURE REVIEW ........................................................................................................................... 10
2. Literature Review ................................................................................................................................... 10
2.1 Review of Concept literature ............................................................................................................ 10
2.1.1 Artificial Intelligence in Education......................................................................................................... 10
2.1.2 An overview Adaptive Learning............................................................................................................. 12
2.2 An overview of Intelligent Tutoring System (ITS)........................................................................... 13
2.2.1 Architecture of Intelligent Tutoring System (ITS) ................................................................................. 15
2.3 Type of ITS Tutor ............................................................................................................................. 16
2.3.1 Cognitive tutoring ................................................................................................................................... 16
2.3.2 ACT-R (Adaptive Control of Thought-Rational) ................................................................................... 17
2.3.3 Constraints based tutoring (CBM) .......................................................................................................... 17
2.4 Intelligent Tutoring System Current scenarios ................................................................................. 18
2.5 Review of Related research works/literature .................................................................................... 19
VII
CHAPTER 3 ............................................................................................................................................... 26
3. RESEARCH DESIGN AND METHODOLOGY .................................................................................. 26
3.1 Research Design, Approach, and Methodology ................................................................................... 26
3.1.1 Research Design and Approach ..................................................................................................... 26
3.1.2.2 Research Approach Adopted ............................................................................................................... 27
3.1.3 Research Processes ........................................................................................................................ 27
3.2 Sampling Design ............................................................................................................................... 30
3.2.1 Target Populations .................................................................................................................................. 30
3.2.2 Sampling Techniques.............................................................................................................................. 30
3.2.3 Sample Size ............................................................................................................................................ 31
3.3 Data Collection Methods .................................................................................................................. 31
3.3.1 Data Collection from Primary Data Sources .......................................................................................... 31
3.3.2 Data Collection from Secondary Data Source ........................................................................................ 33
3.4 Tools Selection Methods .................................................................................................................. 34
3.4.1 Data Analysis methods and Tool Selection ............................................................................................ 34
3.5 Method selected for Tutoring and student domain ........................................................................... 36
3.5.1 Bayesian Network ................................................................................................................................... 36
3.5.2 Bayesian Knowledge Tracing ................................................................................................................. 37
CHAPTER 4 ............................................................................................................................................... 39
4 DATA COLLECTION AND ANALYSIS .............................................................................................. 39
4.1 Primary and Secondary Data Analysis ............................................................................................. 39
4.1.1 Primary Data Collection and Analysis........................................................................................... 39
4.2 Secondary Data Analysis .................................................................................................................. 51
4.2.1 Salient Computer based Tutoring System vs Intelligent Tutoring System ............................................. 51
4.3 Critical Analysis of ITS .................................................................................................................... 53
4.4 Summary ........................................................................................................................................... 55
CHAPTER 5 ............................................................................................................................................... 57
DESIGNING AN INTELLIGENT TUTORING SYSTEM MODEL AND PROTOTYPE ...................... 57
5.1 Background issues on Intelligent Tutoring system ........................................................................... 57
5.2 Designing an Intelligent Tutoring System Model............................................................................. 57
5.2.1 Suitability assessment for selection of Intelligent Tutoring System category ........................................ 57
5.2.2 Designing the Proposed Intelligent Tutoring System (ITS) Model ........................................................ 59
5.2.3 The Tutoring Domain ............................................................................................................................. 62
5.2.4 Student Domain ...................................................................................................................................... 65
5.2.5 Knowledge domain ................................................................................................................................. 67
5.3 Process in Proposed Intelligent Tutoring System Model .................................................................. 68
VIII
5.4 Prototype and Demonstration for the proposed Intelligent Tutoring system model. ........................ 71
5.5 Performance evaluation and End User acceptance ........................................................................... 74
5.5.1 Performance Evaluation .......................................................................................................................... 74
5.5.2 User Acceptance Test ............................................................................................................................. 78
CHAPTER 6 ............................................................................................................................................... 80
6.1 Conclusion ........................................................................................................................................ 80
6.2 Contribution ...................................................................................................................................... 82
6.3 Recommendation .............................................................................................................................. 82
References................................................................................................................................................... 84
APPINDEX A Survey questionnaire for learners communities ................................................................. 88
APPINDEX B Interview questionnaire for Instructor ................................................................................ 91
APPINDEX C Interview Question for pedagogical Expert ........................................................................ 92
APPINDEX D Acceptance and Validation model and prototype assessment ............................................ 93
IX
List of Figures
Figure 2. 1 Adaptive tutor learning process followed.............................................................................................. 12
Figure 2. 2 iterative stage of ITS [23] ...................................................................................................................... 14
Figure 2. 3 Architecture of an intelligent tutoring system ....................................................................................... 15
Figure 2. 4 Type of intelligent tutoring system ....................................................................................................... 18
Figure 3. 1 Research Process Steps ......................................................................................................................... 30
Figure 3. 2 Data Collection Methods ....................................................................................................................... 34
Figure 3. 3 Tool selection Method ........................................................................................................................... 36
Figure 3. 4 Bayesian Network example ................................................................................................................... 37
Figure 4. 1 Response summary to the Availability of computer Application for learning Afaan Oromo ............... 41
Figure 4. 2 Response summary to the most learning way of Afaan Oromo ............................................................ 42
Figure 4.3 Response summary to the type of computer system they use before ...................................................... 42
Figure 4.4 Response summary to the assistance of ITS to learning Afaan Oromo ................................................. 43
Figure 4. 5 Response summary to the ITS enables reduce the cost of learning and improve skill .......................... 44
Figure 4. 6 Response summary to the enables to acquire and maintain the competitive advantage of learning and
respond efficiently and quickly................................................................................................................................ 45
Figure 4. 7 Response summary to improve the satisfaction level and improve personalized learning ................... 46
Figure 4 8 Response summary to the availability of the learning environment of Afaan Oromo existing system . 47
Figure 4. 9 Response summary to the need for Alternative System ....................................................................... 48
Figure 4 10 Response summary to the awareness of ITS ........................................................................................ 49
Figure 4 11 Response summary to Stakeholders Interest towards ITS ................................................................... 49
Figure 4 12 Response summary to ITS adaption by Education institute ................................................................. 50
Figure 4. 13 Response summary to ITS Adoption Recommendation in Firms ....................................................... 50
Figure 4 14 Response summary to ITS Solution for learning Afaan Oromo .......................................................... 51
Figure 5. 1 The Proposed Intelligent Tutoring System Model ................................................................................ 60
Figure 5. 2 Sample topic Dependencies................................................................................................................... 62
Figure 5.3 Example of Afaan Oromo Topic Dependencies ..................................................................................... 64
Figure 5. 4 Directed acyclic graph for representation of topic ................................................................................ 65
Figure 5. 5 representation of topic and Bayesian knowledge tracing ...................................................................... 66
Figure 5. 6 course follow of Afaan Oromo for knowledge representation .............................................................. 68
Figure 5. 7 Flowchart of proposed ITS model ......................................................................................................... 69
Figure 5. 8 inner process of proposed ITS model .................................................................................................... 70
Figure 5. 9 topic contents page ................................................................................................................................ 71
Figure 5 10 Topic practice page .............................................................................................................................. 73
Figure 5. 11 behavioral recorder graph for letter practice ....................................................................................... 74
Figure 5. 12 User acceptance of the Model ............................................................................................................. 79
X
List of Tables
Table 2. 1 Summary of Review of related Work ..................................................................................................... 20
Table 3. 1 comparison of student domain model method ........................................................................................ 38
Table 4. 1 Comparative Analysis of Computer assisting system and Intelligent Tutoring system ......................... 52
Table 4. 2 critical analysis of intelligent tutoring System student model ................................................................ 54
Table 5. 1 Suitability assessment for selection of intelligent tutoring system category .......................................... 57
Table 5. 2 student model topic question sample ...................................................................................................... 65
Table 5. 3 Bayesian Knowledge Tracing decision making table ............................................................................. 67
Table 5. 4 Performance evaluation .......................................................................................................................... 75
XI
List of Acronyms
ACT-R ……………………………………...Adaptive Control Of thought-Rational.
AEHS …………………………………….....Adaptive Educational Hypermedia System
AHS …………………………………………Adaptive Hypermedia System
AI…………………………………………….Artificial Intelligent
ASAT ………………………………………..Auto Tutor Script Authoring Tool (ASAT)
ATI …………………………………………..Aptitude-treatment Interaction System
BKT ……………………………………….....Bayesian Knowledge Tracing
CAI……………………………………………Computer Assist Instruction
CBM ………………………………….............Constraints Based tutoring
CBT…………………………………………...Computer Based Training
CTAT …………………………………………Cognitive Tutor Authoring tool
DAG ………………………………………......Directed Acyclic Graph
EDAC………………………………………….English Dictation Automatic Correction
GIFT………………………………………….. Generalized Intelligent Framework for Tutoring
GUI ……………………………………………Graphical User Interface
ICAI……………………………………............Intelligent Computer Assist Instruction
ICT……………………………………… …….Information communication Technology
IT ………………………………………………Information Technology
ITS ……………………………………………..Intelligent Tutoring System
ITSB ……………………………………………Intelligent tutoring System builder
LMS …………………………………………….Learning Management System
PDF ……………………………………………..Portable Document Format
SLR ……………………………………………..Systematic Literature Review
SPA……………………………………………....Simplified Process for Automated Assessment
XII
CHAPTER 1
1. INTRODUCTION
1.1 Background
Computer and computer technology usage in education is connected to information technology
(IT) development began at an early age when the machine is considered primitive nowadays
constructed for programmed training. Such technologies are currently used very widely, for
example, e-format information literature, virtual training systems and environments, self-appraisal
tests, technology enchanted learning, and animated tutorials. Improvement of IT technologies,
expansion of the internet, and popularization of web technologies have enabled technologyenhanced learning to adopt general matters, and acquaintance of specialized problems as the
introduction of Artificial intelligence adaptive learning environment began[1].
Artificial Intelligence (AI) is a computational model of human behavior and processes human
thought. AI is an area of computer science that emphasizes the creation of intelligent machines
that work and reacts like a human being. It is able to perform tasks, usually requiring human
intelligence. It has various characteristics, which are used for solving complex problems by acting
like a human being, like simulating higher functions of the human brain, programming a computer
to use general language. The teaching-learning process of students can also be supported and
enhanced by using technology like artificial intelligence. It can increase the efficiency and
effectiveness of education that allow students to learn at their own pace using computer tutors
[2][3]. Considering any form of technology and technology-based education regardless of subject
matter is necessary to consider student variability dealing with a heterogeneous learner
community; learners differ in the different number of characteristic and individual traits, which
makes each learner’s approach to learning unique. A personalized learning approach is particularly
important in education when taking care of learner differences, especially when these differences
are rather prominent and require a significant amount of planning time and effort from the
teacher[4].
Before the invention of the Intelligent Tutoring Systems (ITS), Computer-Based Training (CBT)
and Computer-Aided Instruction (CAI) was the first systems used to teach learners using
1
computers. The teaching was not individualized to the learner's needs in these types of programs,
and transitioning a student through the material was not formularized and inflexible[5]. Computer
Assisted
Learning
(CAL)
has
been
implemented
in
the
classrooms
as
technologies allow learners to apply them in their learning process by making them
free from time and space boundaries. Intelligent Computer Assisted Instruction (ICAI) programs
simulate understanding of the domain they teach and respond specifically to the student based on
the subject problem with solving strategies
The low quality of CAI software and teachers' displeasure using such educational devices led to
the integration of artificial intelligence (AI) techniques into the development of ICAI [6]. One of
the human tutor's essential duties is to extend student knowledge in a given subject and advance
student knowledge step by step that the human tutor decides, i.e., what to teach next? And the
teacher provides the student with the sequence of learning steps to refine and mastering the
student’s knowledge [7].
ITS is a computer system that seeks to provide learners with immediate and personalized guidance
or input, typically without the need for human teacher involvement. The popular objective of ITSs
is to allow learning in a meaningful and efficient way through various technologies of computing.
And professional educations. In these systems, they have demonstrated their capabilities and
limitations as well [8].ITS has the capability to perform reasoning, planning, problem-solving, and
learning also. It determines “what the learner knows? Or does not know” and guide and assist the
learners in a personalized manner. Intelligent tutoring systems have a proven method for
promoting the active creation of knowledge beyond the textbook and traditional classroom
environment to mimic one-on-one human tutoring [9].
An intelligent tutoring system was also implemented with an expert system component. Domain
knowledge in artificial intelligence is knowledge about the domain in which the target system
operates. The domain model organizes the course structure, its various components, and the
relationship among the components. This domain model mainly deals with “what-to-assist,” which
is a part of an ITS. The domain models are created in order to represent the vocabulary and key
concepts of the problem domains. It also identifies the relationships among all the entities within
the problem domain's scope and commonly identifies their attributes. An important advantage of
a domain model is that it describes the problem domain's scope [7].
2
Intelligent tutoring systems (ITS) are specially designed for language learning and attempt to
develop a tutor that focuses on processing and assessing the learner’s free-text inputs. The use of
ITSs in education makes it easy to diagnose the learner’s error and provide convenient feedback
or instant support and the source of the errors [7]. Designing ITS Model for Afaan Oromoo is an
attempt to provide feedback for both learner’s community speakers of Afaan Oromoo. Ethiopia is
one of the diverse countries that more than 80 languages is spoken and nation and nationalities
have interaction with each other socially economically and culturally. One way to increase their
interaction is by speaking each other language[10].
1.1.1 E-Learning
Traditional learning with direct interaction with student/learner and teacher have great contribution
and achieve much success. However, nowadays, it raises the demand for personalized learning
when the computer and the internet are booming. Learning is mostly associated with computers
and interactive networks simultaneously, and users require that learning material/activities be
suitably provided to them [11]. E-learning is a computer-based educational tool or system that
enables teaching-learning anywhere and at any time. E-learning is often carried and distributed via
the internet today, but it was delivered using a combination of computer-based techniques such as
CD-ROM in the past. Computer technology enables us, without geographical limitation, the use
tools that make you feel as if you are inside the classroom. A phenomenon that enables individuals
and organizations to keep up with rapid changes through transparency, accessibility, and
opportunity. E-Learning is limited to share material in all kinds of formats such as videos,
slideshows, word documents, and PDFs and conducting webinars, communicating to professor or
advisor via chat and message forums[12].
Pros of E-learning: Learner-centered student has access to many resources and tools, convenient
and portable not bound by place or time Study at own pace; a range of tools have already been
learned for flexible skip content to suit the learner's style. E-learning promotes active and
independent delivery of education and offers an alternative that is faster, cheaper, and potentially
better. It considers the individual learner's differences in their working time and helps compensate
for scarcities of academic staff, including instructors or teachers and facilitators, lab
technicians[13].
3
Limitation of E-learning: E-learning has a lack of instant feedback from instructors, increased
preparation time for the instructor, lack of direct interaction with instructors, and lack of required
skills has an impact on learning. Many show E-learning more effective in giving training for an
employee.
2.1.2 Adaptive Learning
Learning method and student interaction to learning are not definable and describable, learning
and successful learning results may not be defined as a particular thing, the order of matters, the
sequence of events and thus guarantee a successful outcome, adaptation capacity of the student,
collaboration with a learning environment as well as the preferable result of system action plays
tremendous importance. Learning material great in an e-learning environment is hard for student
and teacher seekers to; therefore, it would be appropriate to build adaptive learning systems to
select the right materials. Adaptive learning is generating a unique learning experience for each
learner/ student based on the student’s personality, interests, and performance to achieve goals
such as student academic improvement, student satisfaction, and effective learning process[1].
1.1.2.1 Characteristic of Adaptive Learning:
Adaptive learning characterized by providing a technique for personalized learning which provides
an efficient, effective, and customized learning environment to engage each student and the ability
to alter its activities to provide learning content and pedagogic environment/method for every
student in accordance with her/his individual characteristics such as knowledge, goal, experience,
interest, background when these characteristics vary from student to student[11].
1.2 Statement of the Problem & Motivation
Computer applications have been widely used in different domains to provide proper facilities for
their users. In order to support or assist the students/learners in reading, writing, and learning,
computer applications are playing vital roles. In order to solve the computational problems of
societies, engineering, technology, and education, people use modernized computing and
communication systems[14].
4
Afaan Oromoo is one of Ethiopia's most widely spoken languages, the official working language,
and primary education language in the Oromia region of Ethiopia [15]. As a matter of fact, the
traditional way of teaching the Afaan Oromo language is time-consuming and needs human labor
intensively. The linguistic facilities for language learners for Afaan Oromoo are limited in the
countryside, especially in the Oromia region. Most Afaan Oromoo language speakers have salient
types of linguistic / grammar deficiencies while they speak or write the language. Even non-Afaan
Oromoo speakers cannot learn themselves because of the unavailability of technology-enabled
language tutoring systems. These systems can be greatly instrumental to learn the Afaan Oromoo
with little efforts and instant tutoring support without the intervention of human/teacher. It is well
said that “the development of a nation is also dependent on technology adoption in wider areas
like culture, economic, social and their linguistics”. In Ethiopia, also most of the subject have lack
of a sophisticated supporting system for education, learning, and writing, listening and
communicating in localized languages to support the school and college student countryside.
Afaan Oromo is not only a spoken language, but a subject taught in the schools and
colleges/universities, and hundreds or thousands of students take this course in their regular
studies, mainly in the Oromia region. As a primary observation, the students suffer a lot while they
try to understand the Afaan Oromo language. They have a lack of instant language support systems
in 24/365 manners which may guide them towards the subject and keeps track of student’s learning
behavior to solve their learning problems with ease of guidance. The students who get help and
instant support can perform better than those who do not get it, and the subject learning becomes
difficult and harder for them. Ethiopia is a developing country where there is a lack of educational
materials and support systems in schools and other educational institutions. This is just because of
limited or lack of technical research efforts to design and development support system resources
that can serve and help learner’s communities and students. Also, the existing learning materials
are not adaptive by their nature and need the teacher’s guidance instantly to fully understand the
topic at anytime and anywhere. A modern educational system environment needs instant support
and adaptive environments, which should not only be dependent on the human teacher but can
autonomic to support both learners and teachers.
During corona pandemic, all schools and educational institutions were closed around the world as
well as in Ethiopia, and most of the students in developing countries, including Ethiopia, were
5
challenged in teaching and learning processes because of the lack of technology-enabled support
systems for teaching-learning environments. In Ethiopia, most of schools try to design and deliver
course materials using social media like telegram, and most of higher educational institutions used
their E-learning systems to deliver their courses. While designing and deliberations, these systems
faced challenges like lack of adaptability, measurability of student’s performance, dynamicity for
interaction, ease of guidance and topic-based deliverability.
It was observed that the regular students learning Afaan Oromo do not have any technologyenabled instant support system with better precision and accuracy in schools and colleges. These
issues and challenges of the educational environment are required to be resolved with priority as
education is the key element in the development of a nation, and learning a language like Afaan
Oromo can also increase interaction among the diverse population.
1.3 Research Motivation
Nowadays, in Ethiopia, people speak more than two languages in different parts of the country.
As we know, language is one of the communication media to increase social-economic and cultural
exchange among people and play its own essential role. Learning a language is important to
increase the interaction of people and language learning activities. Ethiopia is a developing
country, and technology-enabled support to the different languages has still not been fully
harnessed/explored. Afaan Oromo language has been widely spoken in the country, but the
learner’s community needs to learn language grammar, vocabulary, and communication in
traditional classroom manners. As an alternative, there is a strong need for technological
interventions to develop the new and advanced mechanism or systems for supporting linguistic
learning, i.e. how the language can be learnt in an automated environment through the help of
automated interaction while speaking or writing. This tutoring support needs to be taken out from
the traditional language classroom teaching to anytime, anywhere over any device tutoring system
over technological platforms.
Such aforementioned issues and challenges motivated this study to initiate and resolve such
problems through an alternative solution. This solution strives to develop an intelligent tutoring
system model for Afaan Oromo for the learner’s community. It can provide exercises and
personalized feedback to the individuals in text-based interaction formats while they learn the
6
Afaan Oromo. This solution can be made easily available in cost-effective manners for the Afaan
Oromo learner community.
1.4 Research Question
The questions this study proposes to answer are as follows:
RQ1.What are the issues (Flaws) and challenges in the current state art of the systems in
teaching and learning Afaan Oromo in Ethiopia
RQ2. What are the important parameters to be considered for designing a localized
intelligent tutoring system Model for Afaan Oromo Language?
RQ3. How to design an Afaan Oromo tutoring system model with intelligent text-based
support features to improve the learnability efficiency of students?
1.5. Objective
1.5.1 General Objective
The general objective of this study is to design an Intelligent Tutoring System (ITS) Model for
Afaan Oromo using Text-based Dialogue to help and support the learners’ community.
1.5.2 Specific Objectives
The Specific objectives of this study are: 
To investigate and analyze the existing issues and challenges in the current state of art
systems in teaching and learning Afaan Oromo in Ethiopia.

To Identify and enlist the important parameters that can be used to design a
localized ITS for the Afaan Oromo language

To design an Afaan Oromo tutoring system model with intelligent text-based support
features to improve the learnability efficiency of students?

To demonstrate a functional prototype for Validation and Evaluation of the
localized ITS for Afaan Oromo
1.6 The significance of the study
The proposed intelligent language tutoring system model can be made available for anyone at any
time and anywhere to learn Afaan Oromo using text-based dialogue. More specifically, the
significance of the study will be to:
7

Assist learners/student to learn Afaan Oromo language and increase social interaction
between people through teaching language

Support for developing efficient and permanent ITS for student /learners for exploring the
technological interventions in linguistic localization supports

The attention of the world communities to attract and learn the Afaan Oromo in the most
convenient and effective manners
1.7 Scope and Limitation of the study
This proposed research aims to Design an Intelligent Language Tutoring System Model for Afaan
Oromoo Using Text-based interaction. The research study aims to assess the feasible ways to
design an intelligent tutoring system for Afaan Oromoo language teaching support. This study
focuses only on developing an intelligent tutoring system model and limited to a well-defined
knowledge domain. The proposed research study covers only text-based tutoring of the Afaan
Oromoo language and also to develop a functional prototype
1.8. Beneficiaries
The beneficiaries of the proposed research study are:  Learner’s community
 Government of Ethiopia
 Researchers
 Ethiopian education ministry
 Global Communities and agencies
1.9 Research/Thesis Document Organization
This research study has grouped every chapter, topic, and sub-topics upon considering the
activities' technicalities and links. The research thesis is compiled in six chapters starting from the
first chapter (i.e. Introduction) to the last chapter (Conclusion and Recommendation). The
description for every chapter is as follows:
Chapter 1: Introduction: In this section of the thesis; an overview or introduction of the existing
and proposed research domain, Problem Statement, Motivation of the study, research questions,
8
Objectives of the study, the Significance of the study, Scope, benefits, and Target beneficiaries of
the study and Deliverables of the study are covered.
Chapter 2: Systematic Review of Literatures: This chapter presents some of the Related Concepts
Review from the Literature such as Computer-assisted instruction, Artificial intelligence in
education, an overview of Intelligent tutoring system, type of intelligent tutor system, an overview
of ITS technology along with Related Research Works Review from Literature to find out the
researchable gapes in the existing studies done before the proposed research.
Chapter 3: Research Design and Methodology: In this section of the thesis, the research design
used, the approach followed, the sampling technique and sample size, qualitative and quantitative
data gathering techniques along with personal and technical observation and the selection of
development or implementation tools and technologies are clearly specified and covered using
suitability assessment.
Chapter 4: Data Collection and Analysis: This section of the study clearly describes the analysis
of primary data and facts about the existing practices, end-users awareness towards ITS,
ideological recommendations from technical experts within the research domain, and finally,
answers the interview questions, technical observations of researcher and responses of respondents
gathered in research questionnaire with analysis and presentation.
Chapter 5: Analysis & designing an intelligent tutoring system model and prototype: In this
section, different ITS types, categories are assessed and analyzed for suitability/ best technology
and finally selected. The proposed model is developed and implemented as proof of the ITS system
concept and then validated using function demonstration with selected features.
Chapter 6: Conclusion, Research Contribution, and Recommendations: In this section, the main
points of this research study, along with research Contributions and recommendations, are
described.
9
CHAPTER 2
LITERATURE REVIEW
2. Literature Review
This section presents a review of literature which are related to the proposed domain of the study.
These include the conceptual review of the literature and the review of related works from the
literature done by different researchers before this study to find out a clear-cut research gap
between the existing knowledge and the knowledge proposed to be acquired through this research.
2.1 Review of Concept literature
2.1.1 Artificial Intelligence in Education
Artificial intelligence is a leading technology capable of altering any aspect of human life and
social interaction in our day-to-day life in different application areas, including Education at
different levels, producing different teaching and learning solution. Nowadays, human-artificial
intelligence interaction is considered a kind of collaboration that assists in education in different
subjects and areas. This type of human-machine interaction is a turning point for human beings to
learn and memorize each subject. [16]. Artificial intelligence has a great contribution to education
by automating the education environment like an automated grading system for an education
institution. Artificial intelligence is also applied in the adaptive learning environment to make elearning more intelligent, individualized learning that satisfies students' needs more. AI relies on
the student side and on the teacher side to understand the students' ability to understand their
lectures and encourage them to provide students with the necessary tips; it acts as a tutor and helps
them learn. Artificial intelligence-driven programs changed the way students interact with
integrated systems that have capability feedback for both teacher and student. Artificial
intelligence systems acquired data that change how the schools find, teach, and support students
and even replace teachers in certain instances [17]. Giving adequate and target feedback for the
student is a primary objective of the teacher or lecture us. The number of student increase became
difficult to handle without the assistance of technology. Many schools, universities, and
institutions start implementing an iterative learning environment to improve education quality
through feedback and monitoring students [18].
10
The intervention of artificial intelligence applications in education can increase efficiency and
effectiveness through an adaptive learning environment without human intervention. It can also be
achieved with the help of intelligent tutoring systems. Intelligent tutoring systems can act as a
tutoring agent for computers that can fit into their purposes for tutoring support in lessons learning,
lab experimentation, etc. The agents can also assist in language learning, grammar testing, and
error diagnosing. Through intermediate agent-based systems, this linguistic support can also help
support documents archival on specific subject matters. Here, problem-specific feedbacks to the
learners and one-to-one human tutoring based knowledge delivery or interaction can help to shift
one step towards modernization from the traditional learning environment [3]
2.1.2 Traditional computer assisting instruction (CAI)
Computer assist instruction is typical computer-based learning where students allow to enter their
answers for CAI get feedback at the end of the lecture. Computer-Assisted Instruction (CAI) has
been implemented in the classrooms since technologies enable learners to apply them in their
learning process free from time and space boundaries. Most of CAI tutor evaluates students’
responses and gives feedback. This computer system checks simply whether student answer is
right or wrong responses in the student input, CAI is type fill-in-the-blanks, and multiple-choice
tasks are frequently used. In CAI, student’s responses are compared to pre-stored answers on the
system, letter by letter, to see if learners’ answers are right or wrong. When accurate responses are
predictable, learners do not make any grammatical mistakes, or imagined errors correspond
directly to intended feedback. Simple pattern matching can work well to detect most CAI errors
[7]. CAI focus on the passive role of student and has prompted student and instructional designers
to focus more on effective uses of computers for education. Coaching is most effective in tasks
where the student’s performance tends to reach the top. The CAI system also assists through the
game because gaming environments combine the characteristics of both coaching and informal
discovery learning for the student by trial. Students learn these skills and knowledge by applying
the context of the game to discover them through their positive influence on their game position.
CAI is limited only on one-sided nature student should not be allowed to ask questions of the tutor
and, in general, affect the tutoring session's directions [19].
11
2.1.2 An overview of Adaptive Learning
An adaptive learning tool is based on technology artefact that interacts with student and different
in presentation style based on their interaction, in learning context-adaptive learning system can
alter its action when providing a subject content in education method for each student in their
accordance of experience, knowledge status, goal and characteristic of interaction. Adaptive
learning system has five main trend macro and micro adaptive system, Aptitude-treatment
interactions system (ATI), Intelligent tutoring system (ITS), and Adaptive Hypermedia System
(AHS) or Adaptive Educational Hypermedia System (AEHS)[11].
Figure 2. 1 Adaptive tutor learning process followed [20]
To enhance the adaptability of tutor strategies is needed to correctly classify the learner/student
state and select optimal instructional strategies given to the learner's current state. A more
comprehensive learner model of ITS to adapt more appropriately to address learner need by
changing instructional strategies, i.e. Content, flow, or feedback. An adaptive tutor instructional
strategies better aligned with learner need to influence student/learner learning gain[19] more
positively.
Macro adaptive learning system: adaptive learning intends to adapt the instructional
performances to students on the macro level. Students are classified into groups by grades from
tests. Students in the same group have similar adaptive instruction
12
Micro adaptive learning system: micro-adaptive performs adaptively on the micro-level since it
discovers and analyzes individuals' needs to provide the user with the appropriate instructions.
When the student is in an ongoing learning process, the system observes and diagnoses
continuously his/her activities, the system’s efficiency is evaluated on how much the adaptive
procedures are tailored to the user’s needs
Aptitude treatment interaction system (ATI): is a system that adapts specific instructional
strategies to specific student’s characteristics (aptitudes) such as knowledge, learning styles,
intellectual abilities, and cognitive styles. ATI also permits the user to control partially or the
learning process, and the teacher can control learning instruction or content presentation in the
course
Intelligent Tutoring System (ITS): is implemented by artificial intelligence methods. It aims to
resemble the situation in which teacher and student sit down one-on-one and attempt to teach and
learn together. Its hybrid approach, which coordinate micro-adaptive system and ATI, can perform
prominently adaptive strategies
Adaptive hypermedia system (AHS): Hypertext is defined as a set of text nodes connected by
links; each node contains some amount of information (text) and several links to other nodes.
Hypermedia is an extension of hypertext, which uses multiple forms of media, such as text, video,
audio, and graphics. AHS uses the user model containing personal information about her/his goals,
interests, and knowledge to adapt the content and navigation in hypermedia space, i.e. adaptive
presentation and adaptive navigation
2.2 An overview of Intelligent Tutoring System (ITS)
The traditional computer assisting instruction (CAI) system cannot provide active information
service. Artificial intelligent technology in the education community is highly increasing in
assisting learners and providing resources, and providing a time-oriented learning environment for
the learner community. Complex computer programs that deal with different heterogeneous forms
of information, ranging from domain to pedagogical knowledge, are intelligent tutoring systems
(ITSs). An ITS is an adaptive system that implements learner personalization learning based on
individual student characteristics such as subject knowledge, mood, emotion, and learning styles.
ITS is an automatic tutoring environment based on various well- established cognitive principles
13
and algorithms to suit users. An ITS is a computer system that can provide students with
immediate, adaptive guidance and personalized feedback without human intervention[21]. ITS
authors need to be well equipped to face multiple issues related to their building process. The
resources required to build an ITS come from multiple research fields, including artificial
intelligence, the cognitive sciences, education, human-computer interaction, and software
engineering [9]. An intelligent tutor system (ITS) is a kind of CAl with Artificial intelligence.
However, current ITS development focuses mainly on a knowledge database, inference engine,
and construction of both teacher model and student model[22]. Intelligent tutoring system (ITS)
implementation has provided high-level gain for education and learner community which have
different knowledge domain based on the understanding of one to one tutoring are more effective
than traditional classroom instruction mainly on language tutoring. ITS usually undertake a
problem-solving context in which tutoring occurs through iterative tutoring to improve student
solving ability and knowledge domain. Educational researchers have investigated intelligent
tutoring systems as a means of providing cost-effective yet personalized tuition[23].
Intelligent tutoring systems must also have an expert module unique to the domain that can
generate and resolve domain problems and provide access to such knowledge to facilitate
disseminating and acquiring this knowledge by learners. An ITS's expert's module can provide the
basis for analyzing learner acts and developing an explicit domain knowledge model for reasoning
mechanisms. Therefore, it is essential to consider the nature and value of the domain knowledge
and the formalisms used to represent and apply it[5].
ITS design and development process consists of four iterative stages, needs assessment, cognitive
task analysis, implementation, and evaluation.
Needs assesment
Cognitive task analysis
Learners (students)
analysis and about
subject matter,
development of valid
computational model,
Implementation
initial tutor
implementation
Figure 2. 2 iterative stage of ITS [24]
14
Evaluation
Effectiveness
and Efficiency
2.2.1 Architecture of Intelligent Tutoring System (ITS)
Intelligent tutoring system architecture talks of the three-component architecture (domain, student,
tutoring) where knowledge and reasoning are needed. The ITS system generally emphasizes both
computational and control term on one computer over the other.
Figure 2. 3 Architecture of an intelligent tutoring system [25]
2.1.4.1 Domain Model: To effectively provide such tutoring services, these systems must be fitted
with an explicit description of the domain information that the learning activity is subject to. The
domain-specific expert module that can generate and resolve domain problems and provide access
to such knowledge to facilitate the dissemination and acquisition of this knowledge by learners
and The ITS expert module should form the basis for the analysis of learner behavior [5]. The
Expert Module does the study of the input of the user. [26]. The domain model contains the
collection of the subject, being the tutor’s abilities, skills, strategies/tactics, the bugs, mal -rules,
and misconceptions that students periodically.
2.1.4.2 Tutoring Module: handles the exercises' processes based on the tutor's assessment of the
student’s answer. The tutoring module takes input from the domain and learner models and
chooses tutoring methods, steps and actions on what the tutor should do next in the exchange. The
model of the learner consists of the intellectual, adaptive, motivational and other psychological
states that emerge during the learning process. Since learner performance is primarily tracked in
the domain model, the learner model is often viewed as an overlay (subset) of the domain model,
which changes throughout tutoring [27]. The tutoring module receives input from the student
15
model to decide on strategies and action taken during tutoring, and tutoring decisions would be
reflected in different interactions with students [28].
2.1.4.3 Student model: Ideally, the central aspect of an ITS should include as much information
as possible about the cognitive and affective states of the student and their growth as the learning
process progresses. The student model is commonly seen as a complex model that has several
functions. [25]. This model is viewed as a dynamic implementation that implements many
functions, has three main functions. Gather both explicit and implicit data from and about the
learner, use this data to create a representation of student state. i.e., The student knowledge and
learning process, and finally based on student state select pedagogical strategies and what student
thought, i.e., activities corrective, elaborative, strategy, diagnosis, predict and evaluate about
student[28].
2.1.4.4 Interface: this component is used to present the tutorial way student/learners are
interacting with the whole ITS system in multiple forms.
2.3 Type of ITS Tutor
2.3.1 Cognitive tutoring
The cognitive tutor produces detailed and precise knowledge involved in student performance in
a given domain, including strategies, problem-solving methods, and how to apply the problemsolving principle in the context of the specific problem. The cognitive tutor provides step by step
learning for student as learning a complex problem-solving skill. This rich problem-solving
environment makes thinking visible, feedback correctness of each step with multiple solution
approaches, error-specific feedback messages triggered by commonly occurring errors, contextsensitive next-step hints, and individualized problem selection[25]. Cognitive tutoring based on
the model tracing algorithm based on Carnegie learning Adaptive Control of Thought-Rational
(ACT-R) represents a knowledge base using production rule. The tutor assesses and interprets
students’ solution steps by comparing what the student does in any given situation against what
the model might do in the same situation. An error detects either when the student step matches
any rule or matches one or more buggy rules, representing a mistake. When a student step/action
is correct, production rules that generate the matching action serve as an interpretation of the
thinking process by which the student arrived at this step; this helps the ITS system to keep track
of individual students toward the skill they master[29].
16
2.3.2 ACT-R (Adaptive Control of Thought-Rational)
Cognitive Tutors are some of the most successful ITS today; they have been developed for several
domains, including algebra, geometry is developed and modelled based on ACT-R theory. ACTR theory defines a basic and irreducible cognitive and perceptual operation that enables the human
mind; each task a human can perform should consist of a series of discrete operations. ACT-R
claim that human knowledge can divide into two irreducible kinds of representation in declarative
and procedural knowledge. Human learning goes through many phases. According to the ACT-R
theory, the student learns first declarative knowledge, which is factual knowledge represented by
chunks later declarative knowledge converted into procedural knowledge, representing the
production rule. The assumption behind ACT-R is that cognitive skills are realized and learned by
production rule, i.e., in order to support a student to learn specific skill or knowledge is equal to a
learn a particular set of production rule which enables students/learners to perform the task
correctly, cognitive tutors organize instruction for students/ learners around underlying production
rule that represent both declarative and procedural knowledge[30].
2.3.3 Constraints based tutoring (CBM)
Constraint-based tutoring is based on all correct solutions to any problem that shares the same
feature any solution that violates the domain principle is incorrect. Constraint-based tutoring
systems interact by advising the student on the mistake they made. CBM represents solution space
in form abstraction, i.e., all solutions that need the same feedback grouped under one class that
corresponds to one constraint. Therefore, an equivalence class represents all solutions that warrant
the same instructional action[25]. CBM represents domain knowledge in declarative form and
follows the theory of learning from performance error. The process of learning from error has twostep error recognition and error correction. It must have a declarative knowledge to detect an error
if a student cannot detect error ITS system informs about a mistake and give a hint and a sequence
of feedback message which reflect the action of teacher help student to overcome give problem
student follow neither impose nor support in any particular strategies since it evaluates the current
state of the student in problem-solving with on demands feedback[29].
17
Figure 2. 4 Type of intelligent tutoring system [31]
2.4 Intelligent Tutoring System Current scenarios
The current education system worldwide is undergoing extraordinary development, us a growing
number of learner communities gaining formal and informal education. The curriculum is
diverting. The educational institute is experimenting with a new and innovative way of delivering
a course, maintaining education quality, improving curricula' relevance, improving the expenditure
of financial resources, and balancing expansion with more significant equity. The Intelligent
Tutoring System is a technical solution that can deliver quality training to many students (ITS).
ITSs are seen to have the ability to increase or encourage over-expanded education systems. Most
ITS research is limited to a universal subject like math and physics; there is also a need for Subject
areas with a more localized interest ripe for technology interventions. It can be used to protect and
foster local culture. [32].
The ITS system is currently implemented in many developed countries with artificial intelligence
by providing one-to-one learning. These systems have an impact on student learning, especially
when it comes to digital environments. ITS shows significant growth currently due to its ease of
supportiveness for the learner community in a different domain. Nowadays, many ITS researchers
focus on the ITS system's quality, making ITS's design and development easy, efficient, and useful.
More research focuses on the development of the student model to highlight individual skill
differences and interests. The ITS system's status is significantly low in developing countries than
in developed countries; these ITS systems aim to assist teachers through a pedagogical function
18
such as problem generation, problem selection, and feedback generation[33]. Experts widely hold
this as the most effective teaching method, and many nations are moving towards it. In the near
future, learning environments, either in a classroom or digital, will undoubtedly consist of
transformative new tools based on AI technology like ITS s. It improves our educational system by
technology like smart robots modelled by the ITS system on an intended subject or specific area.
2.5 Review of Related research works/literature
Chandhya Thirugnanasambantham is a researcher who tries to assess intelligent language tutoring
systems to develop a framework that integrates the basic architecture of an intelligent tutoring
system that uses string searching algorithms to extract vital words from the corpus or word bank.
Further, they implement a system which holds item banks of the sentence with its correct response
and alternative response from language expert that can be used as domain knowledge. He uses
Knuth-Morris Pratt algorithm, a string search algorithm used to match students' responses to
available patterns in the items bank and use a top-down parser. Another Tool for Language
Recognition to generate a parse tree based on the grammatical structure of response to handle
semantic, syntactic, and contextual errors on response [5].
Patil Deepti Reddy and Dr. Sasikumar M are researchers who try to develop a student model for
an intelligent language tutoring system that instructs to teach noun/ verb inflection grammar for
Telugu language. This student model gives the tutor by generating an exercise and personalized
feedback to the Telugu language learner and uses template dialogue to generate a question for
Telugu language tutoring by following rules to generate exercise. To proceed and evaluate student/
learner confidence level, it uses student rule confidence that applies in each rule that generates
problem to problem [27].
He Xuechen is a researcher that designed and developed a web-based intelligent tutor system for
English dictation (EDAC). EDAC allows automatic speech for a student while the student writes
and checks what students write and correct them if their answer is incorrect. EDAC was designed
based on a speech synthesizer to produce artificial human speech and an automatic correction tool
that many students answer with speech [5].
Mona Hafez Mahmoud is a researcher whom asses multi-agent intelligent language tutoring
systems for Arabic language grammar. Intelligent agent autonomous entity observes through
sensors and acts upon an environment using actuators and directs its activity towards achieving
19
goals and learning or using knowledge to achieve their goals. This researcher integrates intelligent
agent and ITS to tutor Arabic language grammar [34].
Arthur C. Graesser, Patrick C, Brian C. Haynes, and Andrew Olney are researchers who study auto
tutor with mixed imitative dialogue and use text-based and 3 D simulation to answer student
questions. In this paper, the system cannot provide exercise for student/learner in the study student
ask a question. The system answers their question based on their answer, not knowing how to
interact with the student and giving personalized feedback for each student [35].
Ralph Vincent Regalado, Michael Louie Boñon, Nadine Chua, Rene Rose Piñera, Shannen Rose
Dela Cruz are a researcher who asses intelligent language tutoring system to develop the ITS for
the Filipino language. Their focus is on teaching higher-level lessons focusing on Filipino
grammar and sentence construction. Their research work assumes students can know or understand
the Filipino language and vocabulary basics only suitable for students above 14 years [36].
Mohammed I. Alhabbash, Ali O. Mahdi, Samy S. Abu Naser are researchers who try to design An
Intelligent Tutoring System for Teaching Grammar English Tenses based on the tense of English
language, present tense, past tense and future tense. They build grammar English tense using
Intelligent Tutoring System Builder (ITSB) tool in building intelligent tutoring system for learning
grammar English tense. They consider both interface student and teacher interface in the research.
The student interface presents education data (lesson) followed by a question for student, and the
Teacher interface consisting of three essential parts. The first part will add lessons and examples
in different multimedia formats, the second part interface to and questions and answer with a hint,
and third interface is to edit the interface into a suitable form. The researcher evaluates their
research in a different group based on their specialization like English language teacher and among
different student, and they got a 90% benefit from their work[37].
Table 2. 1 Summary of Review of Related Work
N Authors
o Year
& Title
&
Journal Major
Name/Conference Name
Findings Critical Remarks to find
/Contributions
out research ability gap for
&conclusion
the proposed research
20
Amer An Intelligent Tutoring
1 Randa,
Khella
System
for
Teaching
Samy S., AbuGrammar English Tenses
Naser
European
Academic
2018
Research
This
research
designed
intelligent
paper
is This research paper focuses
on
the on developing an intelligent
based
tutor
system tutoring system for English
concept to give tutorials for grammar
present, past, and future architecture
tense of English grammar.
and
ITS's
with
both
interfaces for students and
lecturers to present the tutor
to
the student
and add
knowledge to the domain.
This system has a lack of
adaptability and a way to
select topic while tutoring
2 Chandhya
An
Intelligent
Thirugnanasamb Framework
antham, 2011
System This research paper designed This research paper is related
for
Automated
an an
Language tutoring
Tutoring Tool.
First
intelligent
language to the proposed research.
framework
that However, it focuses only on
cooperates with the basic developing the framework of
International architecture of ITS
ITSs and focusing only on a
Conference on Informatics
searching algorithm to find
and
the word from the item bank.
Computational
Intelligence
This study did not use the AI
technique to design the ITS
framework. It superficially
designed based only on the
SEARCH algorithm, which is
a very poor technique for
intelligence integration
tutoring
21
in
3 Patil
Deepti Student
Model
Reddy and Dr. Intelligent
an This research study focuses This research focuses on the
Language on
Sasikumar M,
Tutoring System.
2014
IEEE
14th
for
the
design
and template of dialogue built
development of a student based on noun/verb inflection
International model
for
the
Telugu of Telugu language only. It
Conference on Advanced language. The student model does not include the grammar
Learning Technologies
generates
exercise
learner
and
for and vocabulary of the Telugu
gives language. In the context of
personalized feedback
the proposed research, this
research is lacking. It only
models one part of the ITS
system and does not represent
ITS's full functionality and
low
in
knowledge
tutoring
and
domain,
and
Intelligence Integration.
4 He Xuechen,
2009
A
Web-based
Intelligent This research paper focuses This research paper mainly
Tutoring System for English on
Dictation.
the
design
and focuses on checking the
development of intelligent student's answers while they
International Conference on tutoring
for
English write
and
submit
their
Artificial Intelligence and language dictation for grade answers. It did not give
Computational Intelligence
seven and eight students. personalized feedback for the
EDAC read exercise for student if their answers are
students, and students write incorrectly written.
by hearing what EDAC read context
of
the
In the
proposed
for them. Finally, EDAC research, this research is
checks for student answer again flawed in terms of
only
adaptability, interaction, and
Intelligence Integration.
5 Mona
Hafez A Multi agent is based This research study is on the This research study focuses
Mahmoud,
2018
Intelligent Tutoring System development
intelligent
22
of
an on
a
multi-agent-based
agent-based intelligent language tutoring
for
teaching
Arabic Intelligent tutoring system system. This research is
Grammar.
for teaching Arabic language somehow good and related to
International
Education
Journal
and
of grammar
the proposed research for
Learning
reference because it includes
Systems
intelligence.
It
dependent
is
highly
on
error
identification
and
personalized feedback to the
learner. It only depends on
the grammar aspect of the
Arabic language.
In the context of the proposed
research, this research has a
poor
student
and
tutor
domain model
6 Arthur
C. Auto Tutor: An Intelligent This research study mainly This research paper is related
Graesser,
Tutoring
Patrick
Mixed-Initiative Dialogue.
Chipman, Brian IEEE
System
transactions
With focuses on giving tutorials to the proposed study in only
by using dialogue with a one
on combination
of
aspect,
3-D dialogue.
The
i.e.,
Text
research
C. Haynes, and Education
simulation interaction for the focuses on mixed-initiative
Andrew Olney,
student answers.
2005
dialogue
that
combines
simulation and text dialogue.
However, it depends on the
student’s question and does
not provide any method for
student rate to assess their
level of understanding. It’s
just like student ask question,
and then auto tutoring will
provide an answer for their
23
question means a questionanswer session
7 Ralph
Vincent Salinlahi III: An Intelligent The researcher developed This research study focuses
Regalado
, Tutoring System for Filipino the third version of an on only the development of a
Michael
Louie Language Learning.
intelligent Filipino language tutoring system that gives
Boñon, Nadine Proceedings of The 2nd tutoring system that gives teaching support only to the
Chua,
Rose
Rene Workshop
Piñera, Language
Shannen
Dela Cruz,
on
Natural Filipino language grammar grammar of the Filipino
Processing tutoring for grade seven and language. This study does not
Rose Techniques for Educational eight
Applications
2015
students.
This assess the students that who is
intelligent language tutoring the first language is Filipino.
system assumes that the It is only for students but not
student complete the first all students’ native speakers
level of Filipino language, of Filipino. It is a second
i.e.
vocabulary’s
and language, or these learners
primary language
grew up overseas but did not
learn Filipino formally, but
may be exposed to it through
their Filipino parents, and
does not have a way to follow
the topic and keep track of
students in this research
work.
8 Sh Bakeer, Hani An Intelligent Tutoring This research study mainly
M
System
for
Learning focuses on the development
Abu-Naser,
Introduction to Computer of an intelligent tutoring
Samy S
Science
system
to
teach
an
2018
International
Journal
of introduction of computer
Academic
Research
Pedagogical science
at
University of Gaza
This
research
paper
is
focused on developing an
intelligent tutoring system
based on the architecture of
ITS. It has both interfaces for
Al-Azhar student and lecture to present
the tutor to the student. This
research focuses only on the
interaction of system, and
24
less
adaptability
have
difficult in guiding student.
9 Mones
hanjori
Samy
Naser
2020
Al- Learning
S,Abu-
Computer This research study mainly This research study focuses
Network Using Intelligent focuses on the development on design and development
tutoring system
International
advanced
of an intelligent tutoring of ITS for computer network
Journal
research
Of system
for
and computer network.
development
learning course, the tutor present topic
to the students without any
selection method which make
this
research
adaptability
lack
and
of
doesn’t
follow student knowledge
status which less support
personalized learning
After observation and review of salient related research, reports studies, it is mainly indicated that
the most in-depth study and analysis of the current state of intelligent tutoring system are required
to be designed and developed in a localized manner. Since it has been observed, there is a critical
knowledge gap in intelligent tutoring systems, i.e., haves and not developed vs developing
countries. In this specific area in a localized context, there is a clear lack of research studies. This
motivates the study to conduct an analytical investigation and design an intelligent language
tutoring system model for Afaan Oromoo using text-based dialogue with a prototype
implementation to get tutoring services of Afaan Oromoo anytime, anywhere. Also, a clear gap
and opportunity have been observed that there is no intelligent tutoring system available for
providing tutoring support to the students. This motivates the proposed study to present the
appropriate design of an intelligent language tutoring system model for Afaan Oromoo using textbased interaction.
25
CHAPTER 3
3. RESEARCH DESIGN AND METHODOLOGY
The research methodology is an important and essential plan for guiding the research clearly
according to the drafted objectives. The research methodology's primary purpose is to give a clear
idea of what methods or processes are proposed to be used to answer the research questions and
achieve the research goals. Therefore, the study methodology is an essential element in the research
studies to explain all the steps required to achieve the research goals. [38]. This chapter aims to
explain the data collection, analysis, and model design procedures, tools, and techniques. The
decision about prototype designing tools and techniques are also decided in this chapter. To
achieve the objective of the study, and to answer the research questions, the following specific
research design and methodology is followed.
3.1 Research Design, Approach, and Methodology
3.1.1 Research Design and Approach
By nature, this study is a mixed version of exploratory and constructive research. Initially, this
study starts with an effort to explore the possible design artefacts of the Afaan Oromoo intelligent
language tutoring system using text-based interaction. Further, the study constructs an Afaan
Oromoo intelligent language tutoring system model. Hence the study is exploratory and
constructive research.
In order to achieve the objectives of the proposed study, a qualitative approach is proposed to be
used for data collection and analysis. The basic advantage of qualitative research is that it offers a
complete description and analysis of a research subject without limiting the inputs of the research
and the nature of participant’s responses. The qualitative research approach is used because the
gathered data is based on open-ended questions.
In order to achieve the objectives of the study, a Systematic Literature Review (SLR) is again
considered and incorporated for the detailed document analysis to validate the primary data
analysis outcomes. The primary data is collected using technical observation, interviews of domain
professionals/experts (i.e. lecturers, teachers, and pedagogy experts).
26
In modern research practices, in addition to the detailed review of literature, an SLR is considered
as an additional tool for summarizing quantitative or qualitative evidence collected accurately and
reliably. The SLR also helps to provide a tentative framework or background with systematic
approximation [37].
In this research study, the SLR is mostly derived from relevant researches conducted in the fields
of “Artificial intelligence in education and an overview of the intelligent tutoring systems” to
understand precision and accuracy gaps along with lagging in solutions. The comparative analysis,
impact, and effectiveness of ITS for delivering tutorials are also made. The architecture of ITS,
current trends of ITS in education, and specified features and their characteristics are also analyzed
for selecting the most suitable tools and techniques in the research.
3.1.2.2 Research Approach Adopted
The basic reason for selecting a mixed approach (mix of both qualitative and quantitative) is that
the study requires to gather relevant facts through structured (close-ended) questionnaires
(surveys) from numerous stakeholders so that the analysis result can be presented in a statistical
manner. Also, in-depth data collection through an interview about the study problem domain
through semi-structured (open-ended) questions is done using domain professionals/experts for
better research inputs and insights. Furthermore, qualitative analysis of research inputs without
limiting the scope of the research and the nature of participant’s responses provides better and indepth knowledge of the domain.
3.1.3 Research Processes
The research process consists of a series of acts or measures required to carry out research activities
effectively. [39]. To answer the research questions and achieve the objectives of the research, the
study rigorously followed a well-structured research process flow. Figure 3.1 shows the detailed
research process flow, which is adopted during the research progress.
Step 1: Problem Identification and Definition: At this stage, the researcher identified and
defined the problem intended to be researched and decided on the generalized area of interest; this
is an initial point where the study begins.
27
Step 2: Literature Survey (Review): The researcher rigorously reviewed the existing literature
(previously published research papers, thesis, dissertations, research reports, and books) to study
and analyze what has been discovered about the topic selected and what needs to do for finding
and filling the research gaps from the proposed research.
As a result of SLR, it was clearly observed that designing an intelligent tutoring system is typically
lagging behind in developing countries like Ethiopia, where the computers or computing resources
are limited for assisting students statistically. Although this step is a never-ending task, it is used
and followed continuously from the beginning of problem formulation to completing the research
study.
Step 3: Problem Statement Formulation: the researcher formulated the problem to be
investigated unambiguously and concisely, as described in section 1.2.
Step 4: Research Questions and Objectives Formulation: The researcher formulated the
research questions to be answered by the study as listed in section 1.4, and the research objectives
to be achieved by the study are identified and set in section 1.5.
Step 5: Preparing the Research Design and Methodology: A research design is the arrangement
of data collection and analysis conditions in a way that seeks to integrate significance with the
procedure for the purpose of the research., the conceptual structure within which research is
conducted (the means of obtaining data and the way of organizing obtained data and transformed
into information), research approach, research processes used and methodology followed in this
study to answer the research questions and to achieve research objective is defined in section 3 of
this chapter.
Step 6: Determining Sample Design: the researcher identified the target populations from which
the sample is drawn and decided the way of selecting a sample. The sample size is also determined
in this step using the purposive sampling technique.
Step 7: Collecting the Data: the researcher identified the methods and procedures used to collect
data. The data was collected from different sources (primary and secondary) using different data
collection methods like questionnaires, interviews, technical observation, and book and dictionary
analysis, finally discussed in chapter 4.
28
Step 8: Analyzing/Processing the Data: After the data have been collected, the researcher
analyzed using different analyzing tools and the result of the analysis was interpreted and presented
in statistical diagrams in chapter 4.
Step 9: Model & its prototype Design and Validation of the Proposed Model: After data is
collected and analyzed, the proposed Model designing, simulation, and evaluation were performed
in chapter 5.
Step 10: Generalization and interpretation: at this step, the findings of the study are explained
and interpreted so that the researcher could generalize the study outcomes/results.
Step 11: Preparation of the Thesis: Finally, the researcher compiled the study steps and prepared
the THESIS of what has been done so far and submitted the thesis for presentation.
29
Figure 3. 1 Research Process Steps
3.2 Sampling Design
Sampling design is defined as “the technique or the procedure the researcher would adopt in
selecting some sampling units (items) from which inferences about the population is drawn”. It
deals with determining the size of the sample used for the study and the technique used for
selecting these sampling from the target population[39].
3.2.1 Target Populations
The target population of this study is the language learner community and educational
organizations.
Afaan Oromo Language Learners, Educational institutions, and Afaan Oromo language
professionals are the population elements considered in this research.
3.2.2 Sampling Techniques
This study proposes to use the purposive/deliberate sampling method. Purposive sampling is used
in this study, a form of non-probability sampling in which researchers rely on their judgment when
choosing elements/members of the population to participate in their study. It is also known as
judgment sampling.
This sampling method involves the purposive or deliberate selection of particular units of the
universe for constituting a sample that represents the universe. When population elements are
selected based on the ease of access for inclusion in the survey, in purposive sampling, items for
the sample are selected deliberately by the researcher; his/her choice concerning the items remains
supreme. In other words, The investigation organizers deliberately choose the individual units of
the universe to form a sample on the assumption that the small mass they choose from an immense
one would typically be representative of the whole.[39].
Why purposive sampling? It is the most popular and hence used in this research due to its costeffectiveness and less time required when compared to other sampling techniques. This research
selected the purposive sampling because it allows selecting respondent from a different domain,
i.e. learner, language teacher, and pedagogic experts/professional
30
3.2.3 Sample Size
The sample size is the optimum number of samples from which the required information is
obtained so that inference to the study population is possible. It plays a significant role in the
quantitative research approach than the qualitative approach because the qualitative study is not
about the magnitude instead of a detailed description of the cases from one or a few people[40].
The sample size in this research study was selected 98, which was decided based on different
conditions and criteria set by this research for selecting respondents using Open-ended interviews
and close-ended questionnaires. Moreover, the research study is scientifically applied and
constructive, and therefore 98 sample size is assumed as sufficient representative for
generalization.
3.3 Data Collection Methods
It is a strategy used to collect relevant data from different sources and reveal all the necessary
details relevant to the study so that the objective of the study could be achieved.
This research study collected data from both primary and secondary data sources using a semistructured interview, technical observations, and document analysis like books, encyclopedia and
dictionary. These data collection techniques re-applied, and a review of relevant literature is done
to get the actual status of ground reality of the research's progress in the focused domain. These
data collection methods are summarized in figure 3.2
3.3.1 Data Collection from Primary Data Sources
Identified key informant interviews are proposed to be conducted mainly using rigorous interview
techniques. This primary data was gathered through a semi-structured interview, close-ended
questionnaire, and technical observation of the researcher.
3.3.1.1 Interview
The interview is a primary data collection instrument that is used to gather in-depth detailed data
about the research problem domain through open-ended questions. In this study, the researcher
prepared open-ended questions and did an in-depth interview with selected Afaan Oromo
language instructors at different school (Arsi Nagele elementary and preparatory school
31
instructor, Arba Minch university pedagogic expert) and another instructor (works at different
school) to find out the need of supporting system while they deliver the course.
In this study, open-ended questions were designed for different Stakeholders, and they were
different participants depending on their profession domain and knowledge levels. This means that
the interview questions for the Afaan Oromo instructors and pedagogic experts/professionals are
quite different.
The instructor was interviewed for facts like how to track students, challenges and flaws of lack of
supporting system, how to interact with the student during problem-solving, availability and
accessibility of the system, guidance and personalized learning, and alternative system needs.
The pedagogic experts were interviewed for the facts like challenges and flaws in the existing
learning environment, Adaptability, interaction like feedback and hint, availability of the
supporting education system, categorizing student status, and presentation for ease guidance, and
need for an alternative tutoring system.
These interview questions for pedagogic experts and attached are attached in Appendix B and C,
respectively.
3.3.1.2 Questionnaire
The questionnaire is a flexible and widely used primary data collection method containing a set of
close-ended questions. It allows the researcher to gather facts from respondents without talking
with each one separately and used for statistical analysis. In this study, open-ended questionnaires
were prepared and distributed to the stakeholders/ users, mostly the learner community.
This questionnaire or survey is distributed in two ways.
1. The first is through an online cloud-based surveying tool called Google Form. The
survey questions are created on the Google Form platform, and the link to the survey is
distributed to the respondents through their E-mail so that they can fill the form and submit
their responses online over the cloud.
2. The second is through paper-based questionnaires, which were printed and
distributed to the respondents, and the responses were collected by the researcher.
32
The printed questionnaires were used to reach out to the respondents who don’t have
internet access and address respondents from a different domain and knowledge level. In
this survey, the respondent’s experience and observations of how the learner community
needs to learn Afaan Oromo, challenges in the existing education supporting system were
collected and analyzed. In addition, the survey tried to find out the awareness of the users
towards the ITS solution in learning Afaan Oromo.
These survey questionnaires are attached in appendix C
3.3.1.3 Technical Observation
Technical observation is a purposeful, systematic, and particular way of personally observing in
terms of watching and listening to an interaction or phenomenon as it takes place by the researcher
self.
In this data collection method, the researcher self was involved in the area as a non-participant
observation and performed detailed technical observation on the facts, an existing system like
android application and dictionary, challenges in learning and supporting mechanisms. The
parameters considered in this researcher’s observation phase were related to adaptability of the
system, availability of service delivery, ease of access and guidance, interaction, supportiveness,
anywhere and over any type of device learning.
3.3.2 Data Collection from Secondary Data Source
Secondary data are the facts collected from existing research studies collected and compiled by
other researchers or authors such as books, dictionary, journal articles, conference papers, reports,
web articles, white papers, etc.
In this study, the researcher performed a rigorous assessment of various secondary data sources,
i.e. IEEE and Elsevier Papers, Text Books, Encyclopedia and Dictionary that are relevant to the
study to gain deep insights and facts related to Afaan Oromo linguistic greetings, vocabulary and
grammar, Intelligent Tutoring System, and its application to a specific topic and language
learning.
These data are used as scientific and technical inputs for the design and
development of the proposed ITS model. The summary of data collection methods from both
primary and secondary data sources are illustrated in figure 3.3
33
Figure 3. 2 Data Collection Methods
3.4 Tools Selection Methods
To conduct the proposed research study different software and designing tools are proposed to be
selected using the most-fit strategy using parametric suitability analysis.
The proposed research considered only two parameters, i.e. Open Source (offline Version) and
Cloud-based (online) tools for Afaan Oromo data collection, analysis, and designing the
intelligent language Tutoring system Model, Prototyping for functional demonstration and
validating.
The research study selects the following tools from different possible tools using systematic tool
selection criteria and detailed selection method attempt to mention in the following figure 3.3
3.4.1 Data Analysis methods and Tool Selection
Once the data are gathered and organized, it was analyzed and presented using statistical tools such
as tabulation, diagram, percentage, and rating. It has been analyzed to provide answers to the
34
research questions and justify the worth of the problem under investigation and to construct a
proposed model, and demonstrate its solution.
This study proceeds with critical comparison and analysis of various tools as illustrated in
figure-4 and selected Edraw Max for designing the proposed conceptual Model, Google Form
for conducting online survey and for response analysis and diagrammatical representation, and
Cognitive Tutor Authoring Tool (CTAT) tool as prototype development and evaluation tool for
proof of concept implementation.
These tools are selected based on the suitability assessment for the best fit selection for
accomplishing the task and some other parameters like the necessary feature they have,
availability, platform independence and openness. The diagram in figure 4 shows the tools selected
with the procedures and criteria used for selecting these tools for the completion of this research
study.
E-draw Max: The research study used this tool for modelling and designing the proposed Model
and different diagrams in this research. E-draw Max is a versatile diagram software with features
that make it perfect for sketching a professional-looking diagram. The reason behind selecting this
tool is, it has more functionality and features like data import and export, drag and drop, mind
map, ease of use than other tools like Microsoft Visio and Cute Draw[41][42].
Google Form: The Google form is an online surveying tool that is used for collecting the end
user’s data using Questionnaires. End-users can use this cloud-based surveying tool for filling up
the form and submit their responses remotely via the internet. It has built-in data analysis and
diagrammatical representation of the analysis that aid in the interpretation of the responses. Google
Form is used because it provides powerful and easy to use features that ease data gathering efficient
and automatic[43].
Cognitive Tutor Authoring Tool (CTAT): is an authoring tool that makes tutor development
more efficient for both programmers and non-programmers and supports the development of both
models tracing ITS tutor, i.e., cognitive tutor and example tracing tutoring to represent a different
trade-off between ease of authoring on the one hand and generality flexibility of the resulting on
the other hand. CTAT Interprets student problem-solving behavior using cognitive model capture
35
in the form rule skill of students to learn and compare against those appropriate according to the
model. CTAT has the benefit that the examples shown are more likely to be complete and can act
as semi-automated test cases for the cognitive model later. [44]. CTAT allows authors to link
tutoring knowledge to a graphical user interface (GUI) with little programming effort and
demonstrate model solutions rapidly. CTAT supports developing a tutoring system that provides
individualized learning and step-by-step guidance during the problem-solving process. CTAT
assists within a problem, such as feedback on the steps, next-step hints, and error feedback
messages[20].
Research Tool Selection
Data Collection and
Analysis
Data Analysis
Model Design
Ease Use
Scalability
Features
Functionality
Availability
Open source
Selection
Criteria
Ease use
Cross platform
Functionality
License
Availability
CTAT
ASAT
GIFT
SPA
Edraw max
MS visio
Cutedraw
Google form
SPSS
PSPP
Google Form
MS form
Monkey Survey
Prototype
Google Form
Selected Tool
Selection
Criteria
Data Collection
Model Design and Prototype
Edraw Max
CTAT
Google Form
Edraw Max
CTAT
Figure 3. 3 Tool selection Method
3.5 Method selected for Tutoring and student domain
3.5.1 Bayesian Network
Bayesian network (BN) is a probabilistic graphical model representing a set of random variables
and their conditional dependence with the directed acyclic graph. The Bayesian network is ideal
36
for taking an event that occurred predicting any of the several possible known causes was a
contributing factor. Generalization of BN that each represents and solves decision problems under
uncertainty. Inference Bayesian network assesses the conditional probability of any variable (or
set of variables) while individual values are instantiated with other variables[45]. Bayesian
Network is suitable for representing, sequencing, and ordering the topic to be taught in the
hierarchical and condition (prerequisite) because BN is used to represent uncertainty, complexity,
and, more importantly, the causality of an event[46]. The possibility of an occurrence based on
previous knowledge of a disorder that may be linked to it.
𝑃(𝐴|𝐵) = 𝑃(𝐵|𝐴)𝑃(𝐴)/𝑃(𝐵)……..1
P (A/B) a conditional probability of event A given that B is true
Event A
Event B
Event C
Figure 3. 4 Bayesian Network example
3.5.2 Bayesian Knowledge Tracing
Bayesian knowledge tracing (BKT) model is used to map the performance of the student
(observable variable) to an estimate of the level of knowledge (unobservable or latent variable).
This model takes observation about student performance while solving the problem where a skill
to be learned is involved. BKT is needed to assess or estimate something that cannot be observed
using the history of success and failure give us more information to estimate the knowledge status
of student[47]. BKT is more suitable for ITS student domain because designed to asses’ knowledge
of well-defined fine-grained skill and a skill is atomic and either know or not know. Personalized
instruction and feedback is based on the student model, student model represent the estimation of
student current knowledge level from a determined set of skill.
37
Table 3. 1 comparison of student domain model method
parameter
Overlay
Stereotyping
perturbation
Bayesian Knowledge Tracing
Adaptability
No
Low
Low
High
Low
Low
High
No
No
Yes
Ease
guidance Low
support
Performance
No
tracking
Decision support Yes or No Cluster
(Low)
Correct
(Moderate)
and Personalized (High)
incorrect (Low)
38
CHAPTER 4
4 DATA COLLECTION AND ANALYSIS
This chapter of the research thesis clearly describes the analysis of primary data and facts about
the existing Computing Platform, end-users awareness towards Intelligent Tutoring System (ITS)
technology, ideological recommendations in terms of application and change from professionals
within the research domain, and finally, the answers to interview questions, technical observations
of researcher and responses of respondents gathered in research questionnaire are covered
4.1 Primary and Secondary Data Analysis
In this study, different data collection techniques like personal interviews of different stakeholders
(i.e. domain professionals) and researcher’s self-technical observations were used for collecting
the facts about the existing state of the art systems along with the challenges and flaws in the
existing education systems.
Also, the impact of the lack of supporting systems like ITS for teaching and tutoring Afaan Oromo,
impact on the development of the nation, awareness of the Afaan Oromo professionals and endusers about alternative technology-enabled systems for teaching Afaan Oromo with supportive,
guidance, delivery, personalized & Interactivity, reliability, everywhere accessibility, availability
/ anytime accessibility, features for the students/learners are analyzed.
Further, an assessment of the end-users awareness towards the understanding of the status of ITS
adoption in the education system for both school and higher education in Ethiopia was done. Also,
the opportunities of ITS technology applications were tried to explore in the domain. Finally, the
need for this ITS technology for ‘end-users and ‘domain professionals’ as an alternative system
was investigated towards the assurance of reliability, 24*7 availability, everywhere accessibility
on digital devices efficiently were assessed.
4.1.1 Primary Data Collection and Analysis
To get a comprehensive view of the situation and the current state of the art of systems used for
teaching-learning the Afaan Oromo, this research used both printed and cloud-based (online)
survey questionnaires.
39
In a manual survey, printed questionnaires were prepared and distributed among the respondents.
Online survey questions (questionnaires) were created over cloud-based SaaS, i.e. Google form,
to take care of the ability to collect data first hand and preserve data anonymity and at the same
time enforce the required fields as compulsory.
This was chosen over the other online survey tools because it is easy to access, user-friendly, and
free for all with no restrictions whatsoever. It is easy to present the data in a statistical
representation /diagram and can store/export the survey responses to a spreadsheet for further
analysis. In addition, using an online survey is not only cost-effective but also time-efficient and
does not require any human efforts. The number of respondents who responded using an online
survey was 50, while the rest 48 respondents responded using printed questionnaires.
The data gathered through printed questionnaires were later filled over the online questionnaire so
as to aggregate the data and utilize the capabilities of Google form in data analysis.
The data was gathered from a total of 98 respondents from different parts of Ethiopia like Arba
Minch 22 (13 teachers and 9 students), Shashamane 24(teachers), Addis Ababa 15 (teachers), Arsi
Nagele 27 (8 teachers and 19 students), and 10 (academic non-teaching staff).
During the qualitative interview, an end-user questionnaire and the researcher’s technical
observations checklist were used. The study has used selected features/parameters like 1) End
user’s awareness towards alternative ITS supported learning and teaching, 2) Issues in existing
art of system, personalized learning Afaan Oromo, 3) Cost-effectiveness, 4) Interaction,
feedback, and hint, 5) Ease of learning guidance and presentation, 6) Anytime learning, 7)
Anywhere learning, From any device learning 8) Reliability, 9) Digitization and other promising
extra features for further search and scientific analysis of the existing Afaan Oromo learning.
Parameter 1: Issues in existing learning and teaching Afaan Oromo
To explore the issues and challenges of existing learning and teaching Afaan Oromo, the domain
professionals were interviewed, end user’s response to questionnaires was collected, and
researcher\s self-observation were conducted. Based on the analysis of respondents’ responses and
technical observations, it has been observed that the problems in learning and teaching Afaan
Oromo has serious and alarming issues in general and specific in town areas of Ethiopia. It requires
40
serious attention to educational institutions for worthy solutions. The majority of the respondents
(i.e. 95.9 %) were students, instructors (elementary, high school, preparatory, and universities),
and their education level is a university degree and above. Results revealed that learning Afaan
Oromo becoming the most experienced practice if supported by technology due to technological
advancements increases.
Figure 4. 1 Response summary to the Availability of computer Application for learning Afaan Oromo
According to the respondents’ observation, lack of availability of supporting system for learning
Afaan Oromo is found high 33.3 % and 52.5 % respectively as presented in figure 4.1 and as most
of the respondent interested to learn Afaan Oromo, i.e. 86.5% but they learn using different
platforms like classroom 72.7 %, peer 70.5 %, android App 69.3 % and E-learning system 4.5 %,
Intelligent Tutoring system 1% (which is significantly minimum) and other 3.4 %, as presented in
figure 4.2.
41
Figure 4. 2 Response summary to the most learning way of Afaan Oromo
Some respondents’ percentage as presented in figure 4.2, honestly revealed that they had tried to
learn Afaan Oromo using different methods in their working environment, living environment,
and using computer and mobile applications, but they are highly interested and excited to learn the
Afaan Oromo without or with minimum support of human tutoring. This implied that a huge
population is keenly interested to learn and adopt technology-enabled tutoring systems like ITS as
proposed by this research.
Figure 4.3 Response summary to the type of computer system they use before
Furthermore, it has been observed that most of the community people want to learn Afaan Oromo
but their choices and preferences are different. Those who learnt Afaan Oromo’ still use dictionary
and textbook methods, and these are not interactive media, not supportive and also time-
42
consuming. These cannot easily support personalized learning like teacher or supporter/tutor to
understand, just like the computer system listed in figure 4.3.
Figure 4.4 Response summary to the assistance of ITS to learning Afaan Oromo
The respondents responded that they need aid/assistance like Intelligent Tutoring System to
learning Afaan Oromo in Ethiopia were found high 71.4 % respondent are strongly and 25.5%
highly agree as reveled in the (figure 4.4). From this, we can understand that learning Afaan
Oromo makes them beneficiary, and it can increase the development of the country socially,
morally, and economically, need serious and immediate attention for a suitable solution.
Parameter 2: Cost-effectiveness and improve skill
Cost-effectiveness is also one of the driving factors for any technology towards future survival and
adoption.
43
Figure 4. 5 Response summary to the ITS enables to reduce the cost of learning and improve skill
The responses of the target respondents clearly indicate that the current learning Afaan Oromo
methods are not only inefficient but costlier than the proposed, as shown in figure 4.5. Besides,
68.8% of respondents were strongly agreed, and 27.6 % of respondent were agreed that the usage
of ITS solution/application enables based solution can improve their skill and reduce the overall
cost (figure 4.5). Overall, 96.4% of respondents responded that they could spend their time and
cost to teach Afaan Oromo (figure 4.5). The interview conducted with domain professionals and
researcher technical observation shows that, apart from some schools like Arsi Nagele that deliver
Afaan Oromo as a subject does not have support and guidance computer application/ system for
learner community still students/learner dictionary to learn which is static. In general, these
methods require a lot of money and consume much time that can be exchanged for money. This
indicates that the learner community has a strong need for an alternative system like Intelligent
Tutoring System based solution that is cost-effective and convenient for learning and teaching.
Parameters 3: Suitability, Adaptability, and interaction
Since other education supporting system like E-learning and android application is lack of
dynamicity, they have less interaction and less adaptability. The interviewee clearly revealed that
they are facing several challenges while they are trying to deliver and how the way of interaction
has an effect, and around 76.5% of the respondents of interview adapting student behavior and
interacting are strongly agreed. One of the challenges; the students/learners face is response during
learning. It was also observed that the other supporting system could not keep track of
students/learners' performance level and their knowledge status. Some of the interviewed
professionals indicated that interaction is critical due to the nature of learning, like feedback and
hint is needed when students take action toward the problem/question. Pedagogic professionals
responded during the interview that interaction with a student is very crucial the way each
application or system interact with them have positive and negative output toward learning,
interacting at each step when they solve the problem, and the system must adapt their student level.
44
Figure 4. 6 Response summary to the enables to acquire and maintain the competitive advantage of learning
and respond efficiently and quickly
In general, interaction and adaptability in the existing state of the art systems are complex. In
addition, 71.4% of the respondents were strongly agreed, and 26.5% were agreed with suitability
and interaction learning using ITS solution/application revealed that it enables to Analyze and
respond efficiently and quickly (figure 4.6)
Parameter 4: Guidance and Personalized learning
Guidance and personalized learning are the two most important issues in the education support
systems and the classroom. Many studies show that personalized learning is efficient easily
adaptable with students; this makes personalized learning efficient. The interview conducted with
the instructor and pedagogic professionals in different education institutes and the researcher’s
technical observation shows that the ease of guidance and personalized learning make students
feel free to interact with the system without any restriction. Most of the interviewed expert found
that their institution uses mobile and computer application to support student learning which is
somehow complex and not easy to use and learn even though those system does not guarantee that
ease guidance. Among the interviewed professional, 90.6 % were strongly agree with the
effectiveness of ease guidance and personalized learning. The deep observation found that there is
no way to trace back to individual students when they interact with the system and do not access
45
any
time
they
need.
Figure 4. 7 Response summary to improve the satisfaction level and improve personalized learning
Through this observation, it can be concluded that even though education organizations use other
supporting systems to increase personalized learning, but it’s not efficient and does not provide
easy guidance. 69.4% of the respondents strongly agreed, 27.6 % agreed that learning using ITS
solution/application Increase individualized learning and maintain learning, as presented in figure
4.7
Parameter5: Anytime, anywhere and from any device learning
The cross border (anywhere) learning Afaan Oromo at any time is critical and most required
features of the system like ITS in general and Ethiopia specifically. The interviewed professionals
clearly stated that the existing practices in the education environment are not available in 24*7
mode services except some android application, but it is available only in during working time 8*5
mode services (eight hours in a day from 8:00 AM to 5:00 PM and five days in a week from
46
Monday
to
Friday).
Figure 4 8 Response summary to the availability of the learning environment of Afaan Oromo existing
system
From the respondents, 72.4% of the respondents stated that the current learning system does not
serve 24*7 manner (figure 4.8), and the interviewed respondents have a strong opinion need for
such a system to improve the current state of art systems which is lacking in anytime, anywhere
over any device verification service delivery as presented in figure 4.8. This indicates that anyone
who needs to learn Afaan Oromo on weekends and out of government working hours could not
get the service. So the immediate solution should be a need of the hour to resolve such issues and
challenges, which is a positive thrust for developing or discovering a system like ITS.
Parameter 6: Digitization
Digitization is the act of transforming manual processes and services into electronic or computerenabled formats. Some of the interviewed professionals stated that some of the education
organization has started digitization of their deliver by computer support system but still it is not
fully-fledged digital systems and others are not even started yet but, in the future, they may have
a plan to use computer-supported learning environment.
47
Figure 4. 9 Response summary to the need for Alternative System
The interviewed professionals and 88.6% of surveyed respondents believe an alternative system
like Intelligent Tutoring System technology can improve learning Afaan Oromo (figure 4.9). This
shows that digitization of the learning environment is an important concept that can enable one
step towards technology adoption and transformation towards techno-savvy Ethiopia by improving
the education environment’s service delivery pathways and learner community satisfaction.
Parameter 7: Learner community awareness towards alternative ITS Technology
To draw the awareness and comprehension of the participants about ITS technology and the use
of the ITS-based solutions in the deliver subject, several questions were asked to analyze the level
of awareness and needs for alternative technology in an intelligent tutoring system for Afaan
Oromo.
This study revealed that there is low awareness about ITS technology and a strong need for ITSbased solution in the education environment. From the total of respondents, i.e. 63.3% responded
that they do not know what the ITS technology is (figure 4.10), and 84.7 % of respondents have a
high interest in ITS technology, and they think that it can be a premier (important) technology that
can provide promising solutions especially in supporting educational system. Furthermore, 65.3 %
of respondents found they agree that they did not hear any education organization that uses ITS
based solutions in Ethiopia (figure 4.12), 96.5% of respondent agreed to believe that ITS
48
technology will help you to solve the problem of learning in Afaan Oromo, as indicated in figure
4.12. At the same time, 94.9% of the respondents recommended the adoption and implementation
of ITS in their education environment (figure 4.13). This shows that a powerful will exists towards
improved learning Afaan Oromo which can be achieved by the adoption of ITS technology as an
option.
Figure
4
10
Response
summary
to
the
Figure 4 11 Response summary to Stakeholders Interest towards ITS
49
awareness
of
ITS
Figure 4 12 Response summary to ITS adaption by Education institute
Figure 4. 13 Response summary to ITS Adoption Recommendation in Firms
50
Figure 4 14 Response summary to ITS Solution for learning Afaan Oromo
4.2 Secondary Data Analysis
4.2.1 Salient Computer-based Tutoring System vs Intelligent Tutoring System
In other computer tutors like computer assistance instruction, E-learning and other tutorial system
use traditional way deliver to learner community which more lack of intelligence, i.e., which can
be used for reading, write or any other operation with the data stored in the database because which
is predefined, have lack of dynamicity and not suitable. Computer-aided instruction (CAI) to tries
to enhance both the learning material and the teaching process by facilitating help, but it cannot
deal with the complex behavior of students and problems. CAI requires teachers to fully specify
presentation text, all questions, and their associated answers, and a strict flow of control through
the course, allowing at best different branches to be taken based on the student’s pre enumerated
possible responses for the problem such style of teaching has often been referred to interact with
a page, only one-sided learning students does not allow to ask question and domain knowledge is
not represented[19]. Other supporting systems like E-learning, Tutorial system, computer-based
training, computer-based learning, and android application; all of them are static in their nature
their interaction is a one-sided system to students like computer assisting instruction. Those
education supporting systems are limited to sharing resources between learner community and
teacher in the form of a portable format and do not take individual learning differences. I.e. those
systems have lack of interaction like instant feedback and hint while presenting problem and does
not follow student behavior. However, tutoring system; like ITS technology, records students’
51
status in each problem and trace their knowledge in the form of the behavioral recorder. The ITS
supports personalized learning that considers each individualized learner's capacity and gives
instant support and feedback for each action student takes. This makes ITS technology learning by
doing student learn while they are doing problem-solving. In ITS, there is no one way of
interaction; they interact in both way student to the system, a system to the student, and represent
knowledge. This feature of ITS makes it adaptable, easy, supportive, perfect guidance toward the
topic. The précised feature /characteristics based comparative analysis of other computer assisting
system like E-learning, Computer-based training, tutorial vs Intelligent Tutoring system is
summarily described in table 4.1
Table 4. 1 Comparative Analysis of Computer Assisted System and Intelligent Tutoring System
S. N
1.
Comparison
Computer-based Tutoring Intelligent Tutoring System
Parameters
System
Interaction
One way but not instant Instant Two Interaction
interactive
2.
Not adaptable. Pre-defined High adaptability based on
Adaptability
learner’s/ student’s academic
only
behavior
3.
4.
Availability
and Better, anytime and Poor Better, anytime online and
reliability
reliable
Instant Support
Poor and Instant support is Better and Instant support by
limited
highly reliable
as
needs
human the system itself
intervention
5.
Personalized learning
Poor
and
personalization
limited Better,
individual
Helps
/
based
personalized
learning behavior
6.
Presentation
Limited flexible
52
on
Better and more flexible
7.
8.
Tracking capability of Not available
Keep track of each student
students behavior
learning behavior and adapt it
Solution Nature
Only
Stored
and
static Solutions Computed, Stored
and
Solutions
experiential
adaptability
using
learning
Multiple
pathways facilitation
4.3 Critical Analysis of ITS
The development and research conducted for designing and developing the proposed ITS aims to
provide an intelligent education/tutoring system with an improved learning platform and enhance
students' ability to learn independently using innovations. These teaching methods and education
models have continued to deepen adapted to students' knowledge and learning capabilities [21]. In
the case of the proposed ITS system model, the student domain can play an essential role in storing
information about each and individual students such as his current state of the domain knowledge,
history, and status of students. Student domain affects the decision of other domains like ITS
domain, tutoring domain and knowledge domain. The tutoring domain makes every decision based
on the student domain, what to present and scenario change. Adaptation and personalized learning
also keep on track through the student domain a proper modelled student domain allow ITS system
to make informed decision lead to an effective and efficient ITS model.
The student domain has a different model like the overlay model. In this model, if students know
topics which are considered as expert knowledge level, and the student model consider as a subset
of the knowledge domain. These elements are marked as yes or no depending on if students know
or not know elements.
The Stereotyping model is a variation of overlay model group students in clusters based on some
shared characteristics, i.e. students with similar characteristics grouped in one cluster. In many ITS
systems, this model is to solve the problem of initialization[48]. The knowledge of students'
perturbation model is classified as correct and incorrect with standard error students make, then
the student model is then the overlay model over an expanded collection of knowledge objects,
including both correct and incorrect knowledge proposals.[49]. Constraint-based modelling
53
represents domain knowledge by a set of constraints over the problem state. This model's value is
that it is considered right unless a solution violates at least one constraint. The constraints model
is further represented by the overlay model if the student miss the solution.
Knowledge tracing attempts to determine what the student knows, including misconceptions they
might have. It is an approach used for the student’s assessment. Model tracing attempts to
understand how the student solves a given problem. It is an approach to plan recognition. Model
tracing is especially useful in systems intended to provide guidance when the student reaches an
impasse. Knowledge tracing is also useful as an evaluation tool and a pedagogical decision aid
(e.g. What piece of teaching material to apply next)[49][50].
Table 4. 2 Suitability Characterization and Analysis of intelligent tutoring System models for students
domains to select the appropriate student model [50]
S/N Learning
Parameters
Overlay Stereotyping Perturbation Constraint- Knowledge
model
model
model
(Metrics)
1
based
Model
Tracing
Tracing
model
Generality and low
Low
Low
high
high
high
No
Low
Low
Moderate
high
Moderate
Flexibility
2
Adaptability
with student
3
Guidance
low
Low
Low
moderate
moderate
high
5
Student
low
Categorize
Low
Low
High
moderate
performance
students
tracking
6
Decision
low
Low
Low
Low
High
High
Low
Low
Low
moderate
high
Moderate
support
(tutoring
domain )
7
Complexity
54
4.4 Summary
From the survey, researcher’s technical observation, and interview; it has been observed that; the
existing learning environment for Afaan Oromo have a lack of supportive systems and
applications, i.e. current state of the art system and practices are limited in features and capacity.
These are highly static application and have low or limited interactive support facilities for the
students. Also, the challenges in the current systems usage and practices are identified in the
education environment, mainly in Afaan Oromo learning. The major challenges related to Afaan
Oromo learning and teaching environment include inconvenience adaptability of application to
students, lack of personalized learning, inconvenience on searching learning material, difficulties
in guidance, ease of learning and Availability. The identified challenges related to learning and
teaching Afaan Oromo include slow, complex, inefficiency, and tracking of students. In general,
the existing practices are found to be inefficient, expensive, inconvenient (lack of any time,
anywhere learning), and less flexible.
The study revealed that lack of availability for learning supporting systems in the educational
organization became a severe issue in Ethiopia that have a negative impact on the development of
the country and needs a worth solution with an alternative technology that is ITS proposed by this
research.
Furthermore, it has been analyzed that the end-users were not satisfied with the existing system
practices of Afaan Oromo and revealed a strong need for an alternative system with cost-effective,
anytime, anywhere availability, adaptive, interactive, ease of guidance, ease of use and facilitate
personalized learning. The study revealed that ITS technology could be a premier solution
following student status, and ITS supports learning by doing the practice.
In developing countries like Ethiopia, it’s challenging to find solutions with contextualized and
customized features of ITS based solutions, and therefore, an in-depth investigation and analysis
about the domain is required to make it easy for professionals to transform the existing system to
ICT enabled solutions like ITS.
Based on the interview inputs and technical observations, it has been analyzed that most of the
education organizations are willing to digitize their learning environment but limited because of
55
the lack of skilled human resources and ICT equipment. Others have not to plan or an idea about
transforming the education system by supporting systems like ITS with new features like
availability of service, easy accessibility, keep track of each student, and resource sharing.
Finally, the aforementioned researchers’ technical observation, domain professional interview, and
end-users survey responses conclude that the problems and challenges are serious issues and
require strong and worthy solutions using ICT-enabled technologies such as ITS proposed by this
research. This can be a next-generation solution and transformations technology model to make
the learning environment more interactive, adaptive, and supportive. The proposed system, i.e.
ITS, could be a great and significant contribution to the new knowledge domain and the country
to resolve the aforementioned issues and challenges with immediate effect on the ground in reality.
56
CHAPTER 5
DESIGNING AN INTELLIGENT TUTORING SYSTEM MODEL AND PROTOTYPE
5.1 Background issues on Intelligent Tutoring system
From the researcher’s technical observation and professionals’ interview and a survey
questionnaire, it is cleared that; learning and teaching Afaan Oromo in education institutions are
not sufficiently supported by computer-based tutoring systems. The student/learner does not get
immediate access to get support anytime, anywhere in a personalized way except in the classroom.
Few android applications are developed to teach Afaan Oromo at a basic level, and these
applications have salient limitations and intelligent features with adaptability dynamics. These
teaching and learning Afaan Oromo without a support system have their flaws and challenges on
student performance and skills.
5.2 Designing an Intelligent Tutoring System Model
5.2.1 Suitability assessment for selection of Intelligent Tutoring System category
Even though an intelligent tutoring system is powerful in many education fields for supporting learners, it
does not mean that one size may fit into all. Different categories of intelligent tutoring systems are
being used based on the method they apply. To select the most suitable ITS category for the
selected problem with clustered and customized issues in Afaan Oromo, this study characterized
and feature-based, analyzed the different Models for the suitability, and finally selected the
cognitive tutoring system for designing the proposed model for Afaan Oromo.
Table 5. 1 Suitability Assessment for the selection of intelligent tutoring system category
Parameters
(Metrics)
Cognitive based Tutor
Method
Adaptive control
of
Constrain based Tutor
thought- learning from performance errors
Rational
Knowledge
Procedural and declarative
Declarative, i.e. (constraints)
Action (what can be done)
Based on the Problem state (what wrong
representation
Instruction
evaluated
is done)
57
Feedback
Immediate feedback with each Feedback
messages
directly
to
constraints, but not based on solution
step using instant advisory
strategies, i.e., no advisory
Hints
Yes,
it
Provides
hints
with On missing elements, but not provides
solution strategic direction
Guidance
on solution strategic direction
Multiple solution paths (steps by A single solution pathway
step)
Student Model
Model
tracing
algorithm Constraint-based modelling
(knowledge tracing)
Solutions
One
Computed and stored
correct
solution
stored
and
computed
Bugs
Yes, Supported
No Support
High
Low
If not match, Solution incorrect
If not, Match Solution may be correct or
representation
Cognitive
fidelity
Solution Match
not
In this study, cognitive types of intelligent tutoring systems are selected to design the proposed
intelligent tutoring system model for Afaan Oromo. A cognitive tutor provides different features
and advantages suitable for the proposed model. Nowadays, a cognitive tutor has a successful type
of ITS deployed in various domains, including algebra, geometry, and many others.
This can be used for model tracing for a student models while the single student-specific model is
produced by knowledge tracing. The domain model is based on the cognitive model of the domain
expertise, which describes the knowledge needed to perform tasks like analysis performance of
students/ learner, i.e., excellent or poor.
58
Cognitive tutors analyze how humans solve problems in a specific domain to deliver and present
knowledge to produce a cognitive model. One of the best futures of cognitive tutors provides
immediate support to students/learners by interacting with immediate feedback at each level during
problem-solving and giving hints. Model tracing checks each student's action in a specific problem
to check whether or not they perform correctly by comparing each student’s step directly with one
or more correct or incorrect steps that are dynamically generated by the tutoring system.
5.2.2 Designing the Proposed Intelligent Tutoring System (ITS) Model
ITS system is different from other computer assistance instruction ITS that uses artificial
intelligence mainly to represent knowledge and has its teaching method, which behaves like expert
knowledge domain and tutoring domain. It is also able to diagnose the situation in which the
student is evaluated and provide a solution that enables him/her to succeed in the learning process.
In the fact that each student use their method or strategies, but the strategies vary depending on
what they want to learn[51].
59
Interface
Students request
feedback and hint
assessment
scenario change
Topic
Instructional contents
Question
Tutor domain
Selection module
Behavior
recorder
Teaching topic decision
Teaching method
decision
Student domain
Performance
assessment
Student
knowledge status
Content
Matcher
feedback
Hint
Knowledge Domain
Student list
Course
Topic
Question
Learning Management System (LMS)
Figure 5. 1 The Proposed Intelligent Tutoring System Model
The proposed intelligent tutoring system model contains presented in figure 5.1 has three-layer
with four domains. Here 1) The first layer contains the user interface, 2) the second layer contains
Tutor and student domain, and 3) the third layer contains knowledge domain. All these layers are
presented in figure 5.1.
60
1. As presented in figure 5.1, the interface layer compromises the students/learner that
participate ITS system and the front end of the ITS system. This layer provides the way
students/learners of Afaan Oromo interact with the ITS system.
2. As presented in figure 5.1, the second layer contains both the Tutor and student domain.
The tutoring domain contains different modules, i.e., a selection module, contents, and
matchers, which matches contents and topic to be tutored with the contents. This module
is responsible for what to be learned for the student based on their individual performance,
and selection module selects both topics to be tutored and problems to be present for
student/ learner in that topic. This domain is responsible for strategies to deliver/present
what the next action is. Another domain in this layer is the student domain. This layer is
responsible for keeping track of students/ learners and contains a behavior recorder module,
feedback, hints, student list, and student knowledge base which store students/learners
knowledge status. Behavior recorder module record student/ learner behavior while they
solve the problems based on the action they take, i.e., process tracking, take students
problem-solving ability assessment and provide feedback for their action and hint if
students get difficulty and seek to help for each problem in specific step. Feedback and hint
interacts with the knowledge domain fetch problem hint and feedback from the knowledge
domain based on the topic to be tutored using matching techniques. Knowledge status of
the student is saved on a knowledge base with their list takes values from behavior recorder
module to keep an update of student’s status. This process increases the adaptability of the
proposed intelligent tutor system model; every action made by the tutored domain can
depend on the student domain component. Both domains interact with the upper layer, i.e.
interface layer, through their selection module of tutor domain and behavior recorder of
student module. The tutor domain interacts with the student domain to decide strategies
and tutored topic to be presented. The selection module gets students status from
knowledge status component interaction between these two modules, crucial for easy
guidance, personalized, adaptability.
3. As presented in figure 5.1, knowledge representation is an integral part of an intelligent
tutoring system that enables an entity to determine the consequences by thinking and
61
reasoning about the course delivered to the students. In the proposed ITS model, the
knowledge domain is responsible for the representation of course and topic along with the
question in each topic. Course, topic, and question represent information that is used by
the student domain and tutoring domain in the tutorial process to give information and
reason for the learner in the form of computation. Along with the question, all the
information about the question is represented, like feedback, hint, and solution of each
question. The course and topic are also represented with their information. Further,
implementing the learning management system (LMS) for more flexibility and availability
and supporting active learning are included in this layer.
5.2.3 The Tutoring Domain
As presented in figure 5.1, the Tutoring domain keeps knowledge about teaching strategies and
tactics to select a topic for a student based on the student characteristic and knowledge status which
is stored in the student domain, and how to select and present a topic for students/ learner. The
selection component decides the chapter or topic to present for each student according to her/his
current knowledge status, how the selection component presents a specific topic for each student
because it’s the main characteristic that increases effectiveness to the task of teaching with an ITS.
This is called the adaptability to the student.
The tutor domain also selects a topic of presentation using the Bayesian network for the proposed
model. Here is presented in figure 5.2, the Bayesian network is a probabilistic graphical model that
represents the causal probabilistic relationship among a set of a random variable, i.e., Topic to be
taught and conditional dependencies among the variable (topic) using Directed Acyclic Graph
(DAG) and conditional probability distribution. Thus random variables represented by a node and
two-node are connected by directed edge[46], as shown in figure 5.2.
Figure 5. 2 Sample topic Dependencies
62
For each node, i.e., topic in the domain constitutes the Bayesian network in the domain where
topics B1 and B2 are independent. Topic B1 is conditionally dependent on topic C, and topic A is
conditionally independent on topic C and also dependent on B1 and B2. This means the student
must master or get a minimum pass mark on topic C to proceed to next topic B1 and B2 if students
learned topic C selection component make active the next topic B1 and B2 students take either of
one at a time to learn topic A students must be learned B1 and B2.
Let us visualize some problem-specific topic and their relational dependence in Afaan Oromo.
A. Qubee
A1. Qubee gurgudaa
A4. Dubaachiftuu
A2. Qubee diqiiqaa
A5.Dubbifamaa
A3. Qubee dachaa
B1.Sagalee
C. Jechaa Ijaaruu
B1 Gababa
C1.jechaa Qubee dura
dubaan
B2. Dheeraa
B3.Jaba
C2. Maqaa Guyyaalee
B4.Laafaa
C3. Maqaa Ji’ootaa
B5.Hudhaa
C4.Lakkofsaa
B6. Irraabutaa
E. Sirnaa tuqaalee
E1.gosota Tuqaalee
D. Jechaa waliin hirirsuu
E2.Jechaa tishoo
F. Himaa ijaaru
63
Figure 5.3 Example of Afaan Oromo Topic Dependencies
Here the aforementioned topics, as presented in figure 5.3, are dependent on each other. The
students/learners need to pass topics to continue from one topic to another if they have relational
dependence. If students want to learn the formation of a word (Jechaa Ijaaru) but he/she did not
have prior knowledge of the dependent topic, then student need to learn each topic of a letter
(Qubee), differentiate letter which is a vowel (Dubaachiftuu) and consonant (dubbifama) and voice
(Sagalee), i.e. in order to entirely understand the topic formation of the word which is necessary
to learn topic letter including vowel and consonant and formation of voice. In otherwise case, the
proposed ITS model does not show/ display directly the formation of the word. Here, in figure 5.3;
the topic in the same box has no relational dependence among each other. Such relational
dependence among topic is modelled using a Bayesian network based on the student score in each
problem topic used for inference to decide which topic is next to be learned as presented in figure
5.4.
A1. Qubee
gurgudaa
A2. Qubee
didiiqaa
A3. Qubee
A4. dubbifamaa fi
dubachiftu
B1 Gababa
B2 dheeraa
B4 laafaa
B3 jaba
B5 irraabutaa
C. Jechaa Ijaaruu
E. sirnaa tuqaalee
D jechaa waliin hirirsuu
F. Himaa ijaaru
64
B6 hudhaa
Figure 5. 4 Directed acyclic graphs for the representation of a topic
As presented in figure 5.3, If the students need to understand the topic (node) A4 completely then
they have to master all its parent topic (node) A1, A2, A3.
𝑃(𝑥1, 𝑥2, 𝑥3 … … … … 𝑥𝑛) = ∏𝑛𝑖=1 𝑃(𝑥𝑖|𝑝𝑎𝑟𝑒𝑛𝑡𝑠(𝑥𝑖))…………………. (1)
Where xn is topics
5.2.4 Student Domain
As presented in figure 5.1, the student domain reflects how much the students know about the
domain as a cognitive and learning experience. This represents the estimation of the student's
current knowledge level or status from the determined set of the topic. A proper student model
allows making an informed decision about the content of the topic, which should be presented by
the selection component of the tutoring domain in a particular set of skills or knowledge status.
The Bayesian knowledge tracing (BKT) used by the student domain is to trace the knowledge of
student while they are solving the problem of each topic does a student know topic X from their
pattern of correct and incorrect responses on the problems or problem steps involving in topic X?
Table 5. 2 student model topic question sample
Question 1
2
3
4
5
6
7
8
9
Success/
y
y
x
y
y
y
x
x
x
failure
If the student success is required for every question of the topic, the student likely knows the topic,
and one failure student likely knows the topic, nine failure or one success likely student not know
the topic. In this case, success is more than failure, so more likely student knows the topic. BKT
estimates the knowledge status of students from the history of success and failure. In ITS
measuring students' knowledge while students are learning used to decide to improve instruction
like what is the next topic present to students.
65
Topic 1
Topic 2
Student
action
Student
action
Tutor
intervention
Known
Tutor
intervention
Student
knowledge
Student
knowledge
unlearned
learned
P(T)
P(S)
P(G)
correct
incorrect
unlearned
P(T)
P(G)
correct
learned
P(S)
incorrect
The students have to shift from unlearned state to
learned state to pass from one topic to other
topic
Figure 5. 5 representation of topic and Bayesian knowledge tracing
P (Lo) probabilities of initial knowledge
P (G) probability of guessing question correctly
P (T) probability of transition from unlearned state to learned
P (S) probability of student miss question by mistake
If 𝑪𝒐𝒓𝒓𝒆𝒄𝒕𝒏
𝑃(𝐿𝑛−1 ) =
𝑃(𝐿𝑛−1 )∗(1−𝑃(𝑆))
)∗(1−𝑃(𝑆))+
𝑃(𝐿𝑛−1
(1−𝑃(𝐿𝑛−1 ))∗(𝑃(𝐺))
(2)
𝑰𝒏𝒄𝒐𝒓𝒓𝒆𝒄𝒕𝒏
𝑃(𝐿𝑛−1 ) =
𝑃(𝐿𝑛−1 )∗𝑃(𝑆)
𝑃(𝐿𝑛−1 )∗𝑃(𝑆)+ (1−𝑃(𝐿𝑛−1 ))∗(1−𝑃(𝐺))
(3)
𝑝(𝐿𝑛|𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑛) = 𝑝(𝐿𝑛 − 1|𝑜𝑢𝑡𝑐𝑜𝑚𝑒𝑛) + ((1 − 𝑝(𝑙𝑛 − 1|𝑜𝑢𝑡𝑐𝑜𝑚𝑒) ∗ 𝑝(𝑇)
66
(4)
𝑃(𝑐𝑜𝑟𝑟𝑛) = 𝑝(𝐿𝑛 − 1) ∗ (1 − 𝑝(𝑆)) + (1 − 𝑃(𝐿𝑛 − 1)) ∗ 𝑃(𝐺)
Lo=0.4
T=0.1
S=0.3
(5)
G=0.2
Table 5. 3 Bayesian Knowledge Tracing decision-making table
Topic
Outcome
Know before P(Ln- Know after
1)
P(Lo)
1
F
0.4
0.28
2
S
0.28
0.618
3
S
0.618
0.865
4
F
0.865
0.765
5.2.5 Knowledge domain
As presented in figure 5.1, the Knowledge domain is the basic part of the proposed ITS model
used to represent knowledge to be taught by the proposed model. It models all the information
about the knowledge of the course, topic, and each topics question. In the proposed knowledge
domain, Afaan Oromo represents us subject with the topic in it. Each topic has a problem to be
solved after the completion of each topic, along with the representation of hint and feedback for
the questions. In the model tracing ITS system, the production rule is used to represent knowledge
in the form of an if-then rule. The problem of a topic are modelled based on these rules, hints,
feedback, and answer to the questions are stored as a fact for further operation of student request.
Also, the topic of the contents is displayed for student-based on their performance in the interface.
Topic
Course
Afaan Oromo


Qubee

Sagalee
Jechaa ijaaru



67
Question
Figure 5. 6 courses follow of Afaan Oromo for knowledge representation
As presented in figure 5.6, the Production rule-based ITS system knowledge domain model
captures the knowledge involved in the solution step by step. This model contains rules to
determine the set of questions in the tutor. These rules are different from topic to topic, and
questions to questions for each of them have their own rules.
If students practicing the Afaan Oromo letters
If letter A and B are determined, and the next two space is empty.
Then set C and D as the next letter (Set a goal to determine the next value)
If there is two letters together and two consonant and the second consonant is H or Y or S
Then set those consonants as Double consonant (Set a goal to determine the double
consonant)
5.3 Process in Proposed Intelligent Tutoring System Model
As presented in figure 5.1, the process in the proposed intelligent tutoring system model follows
both the loop of conventional teaching and the nested loop of the intelligent tutoring system. The
model selects the topic to be taught based on the preference of the students/learners; if they do not
have prior interaction by force to fill survey that evaluates their level of skills of Afaan Oromo,
then the selection module presents the selected topic on their skill level at the end of each topic
students take the problem to solve what they learned from the topic.
68
Generate
selected topic
Present problem
Help
YES
NO
Student
request help
Stude nt
Action
Update student
knowledge status
Be havior
Re corde r
Feedback &
Hint
Evaluate
task done
Update student
Model
Topic done
Figure 5. 7 Flowchart of the proposed ITS model
As presented in figure 5.7, each student either seeks help or takes action to solve the problem. If
the students immediately request the help, the model gives them hints about a specific problem
they are trying to solve. But if they do not request for help, students take action to solve the
problem. Here the behavior recorder records the action of student and gives feedback and hint at a
specific level and the step they made a mistake because question/problem require students/learners
to actively apply their knowledge along with the question behavioral recorder modelled with the
expected answer, misconception, hint and feedback, along with other information about the
question. The tutor gives short feedback on the qualities of the learner’s initial answer. The learner
improves the answers, and the tutor evaluates the learner, whether they understand and their
knowledge status. Finally, updates the student knowledge status component. The inner loop
continues until all problems are completely solved in the above process. After these task
69
completion, the overall student knowledge status component is updated. A summary of all
completed topics is taken to select the next topic to be taught.
Figure 5. 8 inner processes of proposed ITS model
As presented in figure 5.1, the components of ITS, i.e. the inner and outer loop of the proposed
intelligent tutoring system, are illustrated as follows:
Outer loop: series of dedicated lessons/topics and challenging problems related to the topic being
tutored.
Inner loop: collaborative tutorial interaction geared toward covering steps or pieces of the
contents of each topic in the outer loop
 For each chapter in the course
o Read each topic
o For each exercise

Student attempts step

Get feedback and hint on step

Try again
o If the master topic/ reach minimum requirement, exit loop
 Take next chapter[52]
70
5.4 Prototype and Demonstration for the proposed Intelligent Tutoring system model.
In scientific and engineering research, the conceptual or theoretical models are required to be
functionally validated through either technical demonstration before the scientific community or
by the acceptance of the stakeholders.
Figure 5. 9 topic contents page
As presented in figure 5.9, a prototype is an essential part of the process of creating or producing
a partial functional system to showcase the applicability of the conceptual or theoretical models in
the real world and proposed as a solution for the selected problems. In order to design an Afaan
Oromo ITS for the learner's community, this research needs to provide concrete and functional
feedback to the development of the system based on a research-based model. Before fully-fledged
ITS system development begins, it needs rigorous testing either in the form of a prototype with
limited features or a fully-fledged functional system.
In the prototype, the designer can get the chance to test the product before the actual
implementation. In general, a prototype need not have all features of the targeted system designed
based on a model. The ultimate objective is to minimize risks resulting from areas such as
deployment, manufacturing and consumer acceptance by recognizing a potential issue early on.
[53][54].
The design and development of the prototype for function evaluation and validation of the
proposed concept and its theoretical system model is essential in the latest research domains. The
71
main goal of the prototype and its demonstration is to prove the conceptual system model design.
A proof of this concept demonstration has been constructed using the cognitive tutor authoring
system (CTAT).
The Prototype
The majority of the components in the proposed ITS model have been demonstrated as per
theoretical specifications formulated for each domain. The demonstration has been incorporated a
two-part ITS student’s interface and student behavioral recorder graph. The topic and question
between student and Afaan Oromo ITS system are carried out using java production rule (JESS)
hints and feedbacks stored and as the fact of jess rule.
As shown in figure 5.9, the above home interface topic and contents of the topic to be learned are
displayed as the prerequisite of the system. On the left side of the page, it shows the list of Afaan
Oromo topic and their subtopic. The content of the themes is illustrated on the right side.. It means
they are not dependents on each other, as shown in figure 5.9. These topics are presented parallel
in their order. It is optional by the system to present a student’s selected topic they want to learn.
Other topics, which are conditional dependents, are displayed on the student status. If a student
cannot learn the prerequisite topic, then the system cannot display the topic in the study for the
student to learn the prerequisite topic to keep track of each student. When students complete the
topic contents, the statements display a practice question before they take the evaluation question,
as shown in figure 5.10
72
Figure 5 10 Topic practice page
As presented in figure 5.10, the interface of a student, and the practice questions are presented for
students after reading the command of the questions. Once they understand the questions, student
start practicing the problem where students get the question, feedback on their practice, and hints
are provided to the students if they need it. The student’s behavior recorder, as shown in figure
5.11, uses to record the behavior of the problem and leads them toward the correct answer step by
step. This, each problem has its behavioral recorder and uses to facilitate support to students when
they need hints or feedbacks, behavioral graph modelled with correct steps in all situations of the
problem, hint, feedback, and misconception in the incorrect situation.
73
Figure 5. 11 behavioral recorder graph for letter practice
5.5 Performance evaluation and End User acceptance
5.5.1 Performance Evaluation
In this subsection of this research study, the performance of the existing learning supporting system
and the newly proposed intelligent tutoring system model for Afaan Oromo has been
comparatively evaluated along with the end-user acceptance in order to validate the performance
in terms of selected parameter like personalized learning, ease of guidance, instant support,
adaptability, reliability, interaction, availability and digitalization. This study collected the
validation and the acceptances evidence/ inputs from the real users, stakeholders, i.e. the learner's
community, to validate the proposed ITS model by comparing the other existing state of art
education supporting systems and practices.
Here the critical acceptance and evaluation remarks are based on the qualitative facts with technical
descriptions collected from the primary source, i.e. real users, stakeholders, i.e. the learner's
74
community, secondary sources of studies and evidence. The following table 5.4 presents the
performance evaluation for validation and learner community acceptance of the Proposed
Intelligent Tutoring System (ITS) model for the teaching and learning of Afaan Oromo.
Table 5. 4 Performance evaluation
75
NO
Features
for Acceptance level 1 Critical Remarks for validation & Acceptance
validation
acceptance
& Low,
2
Medium,3
High, 4 Very High
Other
Proposed
Existing System
system
Model (ITS
practice enabled
Model)
1
Adaptability
1
4
In other existing state of the art computerized tutoring
systems, there is a lack of adaptability of the students like
CAI, CBT E-learning Android Application and others
but in the proposed ITS model, students can be keep
tracked to adapt students in the systems to give an adapt
course and topic.
2
Personalized
2
4
In other existing state of the art computerized tutor
effectiveness of personalized learning is very poor (low)
Learning
due to the lack of following of the student during
students practice in the system. However, the proposed,
ITS model facilitates personalized learning by assessing
individualized performance on the topic, and every
learner gets the right amount of practice.
3
Interaction
1
The existing state of art computerized systems have lack
4
interactive communication between systems and student,
and also they are in one direction from system to
students, which is not interactive but in the proposed ITS
76
model the students and the system are interacting with
each other while the students are tending towards a
solution through instant feedback and hint.
4
Ease Guidance
2
3
In other existing state of the art systems, the guidance is
difficult because many of the current systems are static
and have lack dynamic communications, but in the
proposed ITS model, students are guided easily, and
every problem is supported by multiple solution
pathways; just not at the end and instant feedback is
given at any step by step.
5
Anytime,
4
The existing state of the art systems has limited any time,
4
anywhere, over
anywhere and over any device accessibility in compare
any device
to ITS. But in the proposed ITS model system it is
accessible anywhere anytime over any device and with a
24/7/365 availability
6
Instant support
1
The existing state of the art systems have lack of instant
4
support as most of those systems have not been modeled
well to provide the instant support. They only display the
contents without any support but the proposed ITS
system has instant support through giving hint, instant
feedback and changing scenarios, and ITS is learning by
doing with instant support with expected answer,
misconception, question ant other necessary information
about the problem.
7
Reliability and 2
ITS system provides a more flexible and reliable
4
computer tutoring system with a high degree of
Flexibility
77
dynamicity, and they are not static like other existing
systems while working with ITS system
8
Cost-
3
In existing system there are high cost of education
3
material but in the ITS system, students can get learning
effectiveness
material and practice problem with less cost through
internet
5.5.2 User Acceptance Test
In this research phase, the qualitative measurements and assessment of learners and teachers'
community are done. These communities' inputs justify that the Intelligent Tutoring System Model
can be selectively and a better and alternative tutoring system with intelligent features and can
enhance teaching, learning, and tutoring support.
As presented in figure 5.12, the acceptance test was done after the evaluation and validation of the
model’s functional prototype. The acceptance test results revealed that 87.5% learner’s community
agreed with the cost-effectiveness, 83.3% agreed with the service availability, 85.7% agreed with
the ease of supportiveness, 81% agreed with user’s student adaptability, 85.7% agreed with
personalized learning, 90.5% agreed with ease guidance, 81% with high flexibility and reliability
of the system. The overall learner’s community revealed that the intelligent supports promised and
provided by the intelligent tutoring system for Afaan Oromo are not only robust but revolutionary
for the next generation tutoring systems with intelligent features and can be used for transformation
and betterment of education environments in Ethiopia.
78
user Acceptance in %
User acceptance
100
80
60
40
20
0
85.7
9.5 4.8
81
90.5
81
14
14
5
5
4.8 4
Parameters
Yes
Figure 5. 12 User acceptance of the Model
79
May be
No
85.7
4.8 9.5
83.3
11.19.5
CHAPTER 6
CONCLUSION, CONTRIBUTION, AND RECOMMENDATION
The main findings of the study in this section, along with research contribution and
recommendations, are discussed and forwarded to future research communities.
6.1 Conclusion
This research primarily aimed to identify the issues, challenges on the current state of the art
systems used for teaching and learning Afaan Oromo in Ethiopia. Upon the gathered data analysis,
it was observed that the lack of a supportive systems for education are critical issues all over the
educational organization and exponentially increasing with the advent of intelligent tutoring
system, tools, and techniques. This is how computer technologies are positively affecting and
transforming education. Education is key for the development of the nation, and therefore it needs
immediate attention for suitable solutions. In the current pandemic era, educational systems are
highly challenged and affected all across the world. This is happened because of lack of
technology-enabled supportive systems for the education domains. However, ITS systems can be
treated as robust and next-generation tutoring systems with add on intelligent features for
delivering the education and educational supports then and there at your door in anytime, anywhere
over any device manners with minimum human intervention to the learner community in Ethiopia.
This work presents a model for an intelligent tutoring system to support students and learners
community to make the education environment more interactive and suitable for learning and
teaching using adaptive features in computer-supported applications where student’s performance
can be assessed and tracked. Unlike other related researches, this research study uses a different
approach by incorporating the three important parts. 1) Firstly, investigate and analyze how an
intelligent tutoring system adapts the behavior of the student, contents to present and assess the
performance. 2) Secondly, describe how personalized learning is delivered for the students with
facilitated support and easy guidance, adaptability, interaction, instant support for the student in
real-time, anywhere, anytime manner to the target students. Finally, 3) after rigorous analysis and
suitability assessment, the two categories of intelligent tutoring system (constraint-based ITS and
Model Tracing ITS) are compared to select the suitable ITS type for the proposed ITS model.
80
The major three different research questions are all answered through the study, the answer for
each research question is stated as follows
Answer for research question:RQ1. Primarily this research was aimed to identify the issues (Flaws) and challenges in
the current state art of the systems in teaching and learning Afaan Oromo. Up on exploring
and analyzing both primary and secondary data issues and challenges are identified like 1)
lack of supporting system to teach and learn Afaan Oromo, 2) lack of dynamic system to
teach and learn Afaan Oromo, 3) poor adaptability of system, poor topic modeling (lack of
topic selection method) and 4) low student follow in the current state of the system
RQ2. After issues and challenges of teaching and learning Afaan Oromo is identified,
important parameters are identified to overcome issues , challenge of existing system and
for designing an intelligent tutoring system Model for Afaan Oromo. These are
Adaptability, Ease guidance, ease support, personalized learning, interaction (feedback and
hint), anytime anywhere, cost effectiveness and improve skill
RQ3. Finally an Intelligent Tutoring System Model is designed with adaptive features to
improve the learnability efficiency of students using method of Bayesian network and
Bayesian knowledge tracing in tutoring and student domain respectively. Bayesian
network for topic modeling and topics selection based on the performance of student from
student domain in which knowledge (performance) modeled by Bayesian Knowledge
tracing to improve learnability and efficiency of student through adaptability, personalized
learning, ease guidance, supportiveness and ease interaction.
After rigorous analysis of previously done researches, primary data analysis, and suitability
assessment of the success factor of ITS solutions in education, it was observed that ITS could be
an alternative solution for the significant transformation of education in Ethiopia. Furthermore, the
newly proposed Intelligent Tutoring System Model is designed.
The model has three layers. The interface layer, where students interact with the system and
contents are presented. The student domain has different components, and they are modelled based
on two model tracking algorithms to keep track of each student in the system for instant support,
81
ease guidance. The Bayesian knowledge tracing is used to trace student performance on the system,
and then the system adapts student’s status. The tutoring domain has different components,
responsible for what to present. It models using a Bayesian network with a direct acyclic graph
(DAG) to model the conditionally dependent topic in the course. Finally, the knowledge domain
is designed, which stores course topic and problem contents. The problem is represented using
production rules in the newly proposed ITS model.
The proposed ITS model is designed, developed with a prototype using the Cognitive Tutoring
Authoring Tool (CTAT) for evaluation and validation. This study collected the validation and
acceptance data and opinion from the salient users, learners' communities, and experts. The
acceptance and validation results of the model and the prototype clearly revealed that most of the
learner’s communities, 85.7 % agreed to adapt with a higher degree of acceptance. The ITS model
can be the next-generation transformation catalyst for learners communities and educational
organizations towards the betterment and enhancement of the education systems and their
qualities.
6.2 Contribution
1. The first contribution of this research study is the use of an Intelligent Tutoring System, in which
a computer tutor is to transmit real-time information for students while they are learning.
2. From a traditional education supporting system to a new technology-enabled education
supporting system, better features and functionalities are proved and demonstrated.
3. This research also contributes a new and advanced thought process for education transformation
enabled technology in developing countries like Ethiopia to solve those issues that happen in
education and to align the country with world developments.
Therefore the proposed Intelligent Tutoring System Model can be used as a baseline to create a
new paradigm of education transformational environment and used as a base model to build an
intelligent tutoring system for other subjects and languages as well.
6.3 Recommendation
Intelligent tutoring system still has the power full to enhance the education environment, and
therefore, a lot of researches that need to be done is in the area. As stated in the limitation part, the
82
proof of the concept was done in one of the popular well-defined knowledge domain with limited
performance and addressed with the feature. So the researcher recommends designing and
developed an intelligent tutoring system for the ill-defined knowledge domain to its fullest
implementation and better performance.
Besides the usability of the service based on the text-based dialogue, this research did not cover a
game-based intelligent tutoring system due to the limited time and resources, the researcher
recommends the design and development of a localized game-based intelligent tutoring system
mainly to help lower class student, i.e. forking garden (KG) students, elementary student and to
help Ethiopia military training with the full implementation.
Future research studies can include the different features and categories of students for those who
have disabilities with advanced features. Finally, the researcher suggests the full implementation
and evaluation of the prototype in educational organizations.
83
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[51] M. L. Morales-Rodríguez, J. A. Ramírez-Saldivar, A. Hernández-Ramírez, J. P. SánchezSolís, and J. A. Martínez-Flores, “Architecture for an Intelligent Tutoring System that
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APPENDIX A Survey questionnaire for learners communities
1. What is the type of your profession?
Student
Gov’t Employee
Instructor
Other
2. What is the level of your education? *
Certificate
MA/MSc Degree
Diploma
PhD. Degree
BA/BSc Degree
Other
3. Do you use any computer system applications to learn or teach? *
YES
No
4. If your answer to question No 3 is YES, what type of system do you use? (You can mark more
than one) *
E-learning system
Computer-assisted instruction
Training system
online exam system
Tutorial system
intelligent tutoring system
5. Have you ever or even tried to learn the Afaan Oromo language using a technology-enabled
system?
Yes
No
6. If your answer to question 5 is YES, how do you learn the Afaan Oromo language?
Classroom
Android application
From peer
E-learning
Intelligent tutoring system
other
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7. Rank the availability of computer applications for learning the Afaan Oromo language.
1
2
3
4
5
8. How do you recommend aid/assistance like Intelligent Tutoring System to learning Afaan
Oromo in Ethiopia?
Strongly
Highly
Neutral
Little
9. Do you believe that the existing computer system applications used for the learning environment
are robust and suitable as an alternative medium for learning Afaan Oromo?
YES
NO
Don't Know
10. Is the current learning environment is available anytime, anywhere over any device mode i.e.
24*7*365?
YES
NO
Don't Know
11. If the answer for question No 10 is NO, do you believe that there is a strong need for such
kinds of systems to improve the current state of art systems lacking anytime, anywhere over any
device in learning Afaan Oromo?
YES
NO
Don't Know
12. If your answer for Question Number 5 is YES, then are you satisfied with the way and
environment facilitated by technology-enabled systems?
YES
NO
Don't Know
13. If the answer to question 12 is NO, do you think that there is a strong need for an alternative
system for teaching/tutoring Afaan Oromo?
YES
NO
Don't Know
14. Do you think that there is a strong need for an alternative system like an ‘intelligent tutoring
system’ towards improving the teaching and learning process of the Afaan Oromo language in
Ethiopia?
YES
Don’t Know
NO
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15. Do you think that an alternative system like an ‘intelligent tutoring system’ can improve the
learning of Afaan Oromo?
YES
Don’t Know
NO
16. If your answer to question 15 is YES, What issues can be improved?
Time can be saved
Anywhere, anytime over any device accessibility possible
Search material can be supported
Improve personalized learning
Flexibility for learning to
other _________________
Section II: Perception towards Intelligent Tutoring System Technology
1. Do you know what an “Intelligent Tutoring System (ITS)” is?
YES
NO
2. Have you ever heard of any education institution/school which uses ITS for education/training
YES
NO
3. State your current interest in ITS technology
High
Low
Moderate
No Interest
4. Do you think that ITS technology will help you to solve the problem of learning in Afaan
Oromo?
YES
Don’t Know
NO
5. Does ITS technology provide compatibility with most aspect of your learning style and way you
like to learn?
Strongly Disagree
Slightly Disagree
Slightly Agree
Strongly Agree
Neutral
6. Do you recommend the adoption of ITS technology in the education and training environment?
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YES
NO
Neutral
7. Do you recommend the implementation of ITS technology in an educational organization?
YES
NO
Neutral
III Please indicate to what extent you agree with the listed statements
1. Learning using ITS solution/application enables you to ……….
Strongly
Disagree
Slightly
Disagree
Neutral Slightly Strongly
Agree
Agree
Acquire and maintain a competitive advantage of
learning languages like Afaan Oromo.
Analyze and respond efficiently and quickly
Increase student’ satisfaction level
Increase personalized learning.
Facilitate Ease of Learning
Increase motivation to learn
Reduce overall cost of learning
Improve diverse skills
APPENDIX B Interview questionnaire for Instructor
1. How do we start teaching students Afaan Oromo and categorizing students/ learners at
the initial stage?
2. How do you decide the stage of Afaan Oromo language capacity of a student/ learner?
3. How do you provide grading of the student’s/ learners language level?
4. Is there any element to decide a student's capacity as low, high or moderate …?
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5. Do you think that learning Afaan Oromo is efficient using one to many approaches i.e.
like classroom learning?
6. Do you think that personalized learning can be the best alternative for language learning
like Afaan Oromo?
7. Do you think the way of presentation affects Afaan Oromo language learning? If Yes
how?
8. Is current language learning is available in 24*7*365 mode?
9. If the answer to question 7 is NO, do you believe that there is a strong need for such kinds
of a system that improves learning Afaan Oromo at any time, anywhere over any device?
10. Have you ever heard about the ‘Intelligent Tutoring System’? If the answer is YES,
then what kinds of language intelligent tutoring system do you know?
11. Do you think that learning a language by computer/ ITS have a negative impact? If
your response to question 11 is YES, then what kind of negative impact? Pl elaborate.
12. Do you believe the current language learning environment is reliable?
13. Do you think that an alternative system like an intelligent tutoring system can improve
Afaan Oromo language learning? If yes, how it improve?
14. Do you think that instant feedbacks or Hints will improve the linguistic interactions
amongst students/learning and improve their learning capacity? If the response to question
14 is YES, how and what type of feedback and hint will improve language learning?
15. Do you think that the salient linguistic problems can negatively affect the student's
learning? If “YES “how and what type of problem do you recommend to resolve?
APPENDIX C Interview Question for pedagogical Expert
1. Do you think that student behaviour can affect learning?
2. What do you think about a student's learning capacity/knowledge at first (high, low,
moderate)?
3. Do you think that all students are of the same knowledge level?
4. If the answer to question 3 is NO, how can you categorize students based on their
knowledge and skill levels?
6. Do you think that the way of presentation affects the student's learning?
7. If the answer to question 6 is Yes, How it affects and which presentation way is more
effective?
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8. Do you think learning a language is better effective and efficient in one to many modes?
Like classroom?
9. Do you recommend a technology-enabled supporting system for language teaching?
10. Do you think personalized is effective in language learning? If yes, how?
11. Have you heard or know about the Intelligent Tutoring System? If “YES “what type of
intelligent tutoring system do you know and prefer?
12. Do you think that learning by computer/ ITS has a negative impact? If “YES “what
kind of negative impact it has?
13. Do you think that the current language learning environment is reliable?
14. Do you think that technology-enabled support systems like intelligent tutoring can
improve language learning? If “YES”, how can it improve?
15. Do you think that interacting with the stepwise feedback and hint will increase student’s
learning? If “YES” how and which type of feedback and hint do you recommend?
16. What type of problem do you think can affect a student’s learning? If “YES” how and
which type of problem do you recommend to resolve?
APPENDIX D Acceptance and Validation model and prototype assessment
1. Do you think that the proposed system Model fulfils its intended objectives?
Yes
No
Maybe
2. Does the designed model meet your expectations that you raised when interviewed?
Yes
No
3. Indicate your level of satisfaction with the new prototype to address the issues that exist in the
current system
Very Satisfied
Satisfied
Not Satisfied
4. Do you think if the prototype implemented to its fullest functionality will give you adapt to
students?
Yes
No
Maybe
5. Do you think if the prototype implemented to its fullest functionality will give for students
ease, support, and guidance?
Yes
No
Maybe
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6. Do you think if the prototype implemented to its fullest functionality will facilitate
personalized learning?
Yes
No
Maybe
7. Do you think if the prototype implemented to its fullest functionality will available on any
time, anywhere manner?
Yes
No
Maybe
8. Do you think if the prototype implemented to its fullest functionality will cost-effective?
Yes
No
Maybe
9. Do you feel that this system model, when transformed into the real-world application, will
support the learning and teaching process of Afaan Oromo?
Yes
No
Maybe
10. Do you feel that this system model, when transformed into the real-world application, will
be a new knowledge contribution towards alleviation of the aforementioned enlisted issues and
challenges?
Yes
No
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