Perceived Suitability of the Structured e-Learning Content Based on the
Classification of Tacit Knowledge
Abstract
This paper involved the design and evaluation of the Structured e-Learning Content based on the tacit knowledge
classification. The paper firstly investigated the current state of research in e-learning software and addressed the current
issues of explicit and tacit knowledge processing by taking into account the elements of pedagogical design such as
conveying precise search results, offering key points concisely, and chunking the content into digestible segments for
easy onscreen reading. Furthermore, an e-learning system for structuring contents was designed based on a new
pedagogical model for separating the learning contents into segments that employed super-ordinate concepts in the
student's cognitive structure, such as title, introduction, problem, objective, method, result. A questionnaire was
constructed by adapting factors of perceived Ease of Use (EOU) and perceived Usefulness. The sample comprised 102
postgraduates students from two schools in a renown public university in Penang. The findings showed that the mean
total scores for Usefulness and EOU were at moderately agreed at 3.48, 3.87 and 3.37 respectively. The findings revealed
that the Structured e-Learning Content is suitable by program and school, and triggered satisfactory levels of processing
for tacit knowledge.
Keywords: Tacit Knowledge, e-learning, TAM, Pedagogy
INTRODUCTION
The digital content for e-learning is huge, and retrieving this content normally results in a random list output that can be
daunting, meaningless, and not efficient for learning purposes (Roca, Chiu, & Martínez, 2006). Each item on the list
consists of a vague description of the document with the keywords entered for the search matched in various
combinations. Clicking on the item would invoke a download or full display of the document which could be one page
or hundreds of pages long. These facts reflect the current lack of pedagogical preparation to provide an efficient
representation that helps ensure meaningful use of or access to the documents in the e-learning systems. Thus, there is a
need to employ technologies for designing more suitable presentations of the learning contents and documents.
Structuring knowledge has been recognized as an important issue and systems can be personalized for different
knowledge displays. Also, representing learning contexts in several ways and styles, based on the pedagogical design of
the learning environment, can provide quick and accurate additional information for those interested in details
(Coffield, Moseley, Hall, & Ecclestone, 2009).
The knowledge representation of content in the e-learning applications has been categorized into various formats in
a different number of educational environments. Structured knowledge can be found in different forms, which
seek to reside, as a conceptual stage, in the head of the person using e-learning resources (Wong, Yin, Yang, & Cheng,
2011). This kind of knowledge contains several forms (both tacit and explicit) that most learners understand. Another
type presents knowledge as information, which represented in a mode of speaking in the form of documents (Ferrer &
Alonso, 2010). The adoption of structured knowledge in the academic fields alters the impressions of educators and
learners and allows them to perform their tasks meaningfully. E-learning systems now offer huge sets of data and
information based on deploying different learning tools and techniques for teaching, learning and reference purposes,
which could be seen as a blessing with plenty of information readily available just a click away (Tang & McCalla,
2005). Even so, it could equally be seen as an exponentially growing nightmare, in which unstructured information
chokes the delivery system without providing any articulate or meaningful knowledge. The main goal of e-learning is
to share knowledge and wisdom and this is accomplished through the distribution of data and information. The user or
the learner has to access the relevant data and information and then analyse, synthesize and integrate them into
meaningful knowledge. E-learning contents and databases are getting huge and without proper management of the
contents and the use of efficient learning strategies the collection remains as data. An example of an effort around this
issue for the business databases is the use of data mining, which is a method of extracting meaningful information from
mountains of data (Dredze, et al., 2007). No such provision is available for e-learning.
Nonaka and Takeuchi (1999) have categorized knowledge into explicit and tacit knowledge. Explicit knowledge is
knowledge that can be easily or directly expressed and documented and shared through hard and soft copies of
documents such as those uploaded to the e-learning databases. Tacit knowledge, on the other hand, is expertise, high
level mastery, and wisdom that were developed through years of practice and reflection and cannot be directly shared
or transferred to another person or captured into a document. They suggest that tacit knowledge can be developed
through numerous iterations of the process of externalization of the ideas by the experts and the processes of
combination and internalization by the novices or the learners. The process of externalization involves providing
meaningful structures to the content or skill in a way that can be efficiently reconstructed by the learners. For elearning, the structuring of the digital content in a meaningful or holistic way that offers the development of tacit
knowledge can accomplished through the principles of pedagogical design. Pedagogical design consists of the use of
high-order patterns in presenting knowledge contents by employing strategies such as structuring, ordering, chunking,
and customizing. Knowledge providers and current educational services such as Google, Yahoo, ProQuest, etc. offer
search engines that locate and attempt to structure knowledge from web or e-learning databases but they only offer data
and pieces of information in the form of explicit knowledge.
This paper took into account the elements of pedagogical design such as conveying precise search results, offering
key points concisely, and chunking the content into digestible segments for easy onscreen reading. After addressing the
basic issues and other difficulties in learning from online materials, an e-learning system was developed to present
these materials structurally based on a new pedagogical design model for separating the contents into segments that
employed super-ordinate concepts following Ausubel’s (1968) prescriptions that were already in the student's cognitive
structure, such as title, introduction, problem, objective, method, analysis, and result. TAM was employed to evaluate
the acceptance of the system as shown in Figure 1.
Suitability
Usefulness
Ease of Use
TAM
Structured e-Learning
Content
Title
Introduction
Problem
Statement
Objective
Method
Finding
Figure 1: Theoretical Framework
The study variables included Ease of use as the degree to which a person believes that using Structured e-Learning
Content would be free from effort as measured. While Usefulness as the degree to which a person believes that using
Structured e-Learning Content would be useful as measured. However, the tacit classification for knowledge is
presented in Figure 2 in terms of join of knowledge wholes and formation of knowledge wholes.
Tacit
Join of
Wholes
Structured
e-Learning
Content
Wisdom
Connection of
Parts
»
ut
ur
e
Knowledge
«F
«Context »
Formation
of a Whole
Explicit
Information
Current
Techniques
Data
«P
as
t»
Gathering
of Parts
«Understanding »
Researching
Absorbing
Doing
Interacting
Reflecting
Figure 2: Conceptual Framework (Modified from Clark, 2004)
STRUCTURED E-LEARNING CONTENT DESIGN
This paper adapted the main steps of Ausubel that follows logical rules in organizing the information and ideas it
receives, and sorts and arranges them in an orderly fashion in into the learner’s cognitive structure. Ausubel offers a
theory for organizing or integrating information meaningfully into a schema or cognitive framework. According to
Ausubel, the human mind follows logical rules in organizing the information and ideas it receives, and sorts and arranges
them in an orderly fashion in into the learner’s cognitive structure. Ausubel argues that an efficient cognitive structure is
hierarchically organized, that is, highly inclusive or general concepts form at the top of the knowledge hierarchy with less
inclusive sub concepts and informational data subsumed below them. Creating the proper categories and arranging them
into a hierarchical structure allows the learners to retain and recall a specific set of knowledge more efficiently. Having a
cognitive structure that is clear and well organized facilitates the learner to absorb new information without much effort
and in a shorter time.
Ausubel’s theory proposes the use of major or super-ordinate concepts to act as anchoring posts for the new
information to be meaningfully acquired. Academic and research articles are written or presented using specific
formats and set of concepts or phrases for section headings, such as “problem statement”, “method”, “data analysis”,
and “findings”, etc. These super-ordinate concepts are already in the student's cognitive structure, especially among
those pursuing graduate studies.
A new pedagogical model was designed based on the adaptation of factors of TAM by Davis (1990), knowledge
conversion by Clark (2004), and a model to ensure reliability by Ferrer (2010). The structuring of the new model
evolved the concepts of Ryberg, Niemczika and Brenstein (2009) and Stufflebeam (2001) to design and structure a
meaningful pedagogical model. Figure 3 shows the Structured e-Learning Content page design.
The utilisation of pedagogical patterns is still increasing, especially in the e-learning field. Whereas developers of
these applications apply pedagogical patterns in a conventional manner, the learning community is still far from
employing pedagogical designs in the development of a proper learning pattern. Ryberg and others has made an
attempt to characterise pedagogical meaning in terms of design patterns (Ryberg, Niemczik, & Brenstein, 2009). In
listing good education and training practices through pedagogical designs, they recommend nine characteristics
related to putting a proper pedagogical design on record for a clear representation; thus, this research followed the
following steps in customizing the pedagogical design, such as:
- Name: This represents the proposed pedagogical design in a single phrase, enabling quick organisation
and retrieval.
- Problem: This is a description of the problem, involving its target or a needed outcome, and clarifies
that the problem exists.
- Context: This presents a precondition that needs to be concluded in order for that problem to occur.
- Forces: This provides a short explanation of contexts and how these interact among community
members. Some forces may be inconsistent.
- Solution: This concerns instructions, which may involve alternative forms. The solution may contain
pictures, diagrams, prose or other media.
-
-
Examples: These are particular pattern implementations and resolutions, similarities, visual examples
and known uses which can be provided to a user who needs to understand the context.
Resulting Context: This represents the results after adopting the pedagogical design, and comprises
post-conditions and other new problems that might result from figuring out the first problem.
Rationale: This presents the concepts of choosing the pedagogical design, and contains a description of
how the pattern works and how forces and constraints are determined to build the required
representation.
Related Patterns: These consist of the differences from and relationships with other patterns that are
used to figure out the problems.
Precel, Eshet-Alkalai and Alberton (2009) argued that most academic online learning is perceived as
complementary to lecture-based courses, and therefore employ pedagogical approaches that are adopted from the
traditional, frontal teaching and learning process. Consequently, online courses and materials do not usually employ
pedagogical approaches that fit online learning. As a result, students’ achievements when reading digital text are
reported to be lower than their achievements when reading printed text. Reading academic text in a digital format is
problematic for most learners because of disorientation problems and the low level of ownership that readers have in
digital text.
The following process characterizes how the system runs by drawing the relations among agents. The reason of
employing MAS is to improve the representation result of the stored contents. The process was classified into
three parts:
i) Client: presents the user or learners that serve the internet for retrieving their request.
ii) Server: presents the work cycle between client request and database contents, which helps to
organize the user query to be switched into the system database through content switch.
iii) Database: presents the place for storing the data, this study took in account the different type of
contents to be stored into the database clusters.
To implement the multi-agent system, three basic types and five external types of agents were defined in this
stage to clarify the agent’s goals, tasks, and user interface. Agents were developed by using Hypertext Preprocessor
(PHP) and MYSQL. Table 1 presents the agents, their goals and their main tasks. While Figure 3 shows the designed
Structure e-Learning Content page.
No
1
2
3
4
5
6
7
8
Table 1: Agent’s Goals, Tasks, and User Interface
Tasks
User Interface
Main
Agents
Recording
Agent
Managing
Agent
Goals
Store
the
e-learning
contents
Manage the e-learning
contents
Classify the contents into
structured classification
Organize
the
content
classification
Retrieving
Agent
Retrieve the e-learning
contents
Represent the structured
view for the contents
External
Agents
Search
Agent
Profile
Agent
Classifier
Agent
Tracking
Agent
Goals
Tasks
User Interface
Find queries
Match queries with the
available contents
Retrieve the user details
Search page
Works as a controller
between objects
Report the availability of
the e-learning contents
Not Applicable
Summarize
the
user
activities
towards
elearning contents
Summary page
Analyzer
Agent
Optimize
the
user
interface
Utilize a new object
between two activities
Detecting the missing
elements in the e-learning
contents
Analyze the content
usage in term of view and
download.
Add article page
Manage
Content,
Categories, Groups, and
User Pages
Display result page
User profile page
Crash report page
Figure 3: Display for Structure e-Learning Content
METHOD
This study employs the evaluation research techniques by Stufflebeam (2001). He defines evaluation means as a study
designed and conducted to assist some audience to assess an object’s merit and worth. Copes, Vieraitis, River and Hall
(2005) stated that evaluation research is conducted to assess a program’s merit and worth, to improve program delivery,
to develop knowledge, and to ensure oversight and compliance towards specific regulations and standards. In addition,
the Utilization-Focused Evaluation method based on the premise by Patton (2002) was used that concern on every
evaluation should be judged by their intended utility and actual use by intended users and offers excellent
accountability and accuracy.
Usefulness (11 items)
Ease of Use (9 items)
Table 2: Measurement Process
Suitability
Technology Acceptance Model Assessments by Davis
(1990)
The usability testing of this research was conducted on eleven postgraduate students out of twelve from CITM
and CS in USM. The testing was conducted to perceive students satisfaction towards content and Graphical User
Interface (GUI). The instrument was adapted from Chin, Diehl and Norman (1988) which involved 27 items for user
interface satisfaction. The data collection method for usability test was conducted based on Lee recommendations as
in Table 3.
Table 3: Data Collection Methods for Usability Testing (Adapted from Lee, 1999)
Technique
Details
Observation
Data collection by observing the user’s behavior throughout the usability testing.
Interview/Verbal Report
Data collection by the user’s verbal report using interview after completing the usability
testing.
Thinking-Aloud
Data collection using user’s thought throughout the usability testing.
Questionnaire
Data collection using question items that address information and attitude about usability
of the Structured e-Learning Content.
Video Analysis
Data collection by one or more videos used to capture data about user interactions during
usability testing.
Auto
Data-Logging
Data collection by auto-logging programs used to track user actions throughout the
Program
usability testing.
Software Support
Data collection using software designed to support the evaluation expert during the
usability testing process.
A total of eleven participants were involved in this usability test to ensure stable results. Each participant spent
approximately 50 minutes to one hour. In general, all participants found the Structured e-learning Content web site to
be clear and straightforward (With an average 84.77 points) as shown in Table 4.
Table 4: Usability Test Result
Participant
Overall
reaction to
the software
(max. 60)
Screen
(max. 40)
Terminology
and system
information
(max. 60)
Learning
(max. 50)
Capabilities
(max. 60)
(100)
1
2
3
4
5
6
7
8
9
10
11
Average
50
55
45
40
41
53
56
52
48
39
46
47.77 / 60
37
33
39
31
37
28
30
35
30
39
38
34.27 / 40
49
57
52
50
42
56
41
45
55
52
47
49.63 / 60
50
46
43
45
42
47
38
43
46
44
48
44.72 / 50
59
55
52
47
57
51
49
48
58
53
49
52.54 / 60
90.74
91.11
85.55
78.88
81.11
87.03
79.25
82.59
87.77
84.07
84.44
84.77
100
Each individual session lasted approximately 50 minutes to one hour. During the sessions, the test administrator
explained the test session and asked the participant to fill out a brief background questionnaire (Appendix A).
Participants read the task scenarios and tried to find the information on the website. The tasks consisted of the main
steps such as registration, login, browsing, navigating, and downloading, as well as exploring all the links and system
functionalities available in the software. After each task:
i) The participants rated the overall learning to the Structured e-Learning Content on a 9-point Scale with
different ranging for six subjective measures.
ii) The participants rated the website screen by using a 9-point scale for four subjective measures.
iii) The participant rated the website terminology and information by using a 9-point scale for six subjective
measures.
iv) The participant rated the website learning by using a 9-point scale for five subjective measures.
After the last task was completed, the test administrator asked the participant to rate the Structured e-learning
Content capabilities by using a 9-point scale for six subjective measures.
RESULT & DISCUSSION
The total reliability was calculated for 17 items from the Usefulness & EOU. The Cronbach’s Alpha for the 17 items
was =.896 by 102 participants, as shown in Table 6. The total reliability for the usefulness was calculated for 8 items
and the Cronbach’s Alpha for these items was =.772, as shown in Table 5, while the total reliability for the EOU was
calculated for 9 items and the Cronbach’s Alpha for these items was =.767 by 102 participants..
Items
For 17 Items
Usefulness
EOU
Table 5: Total Reliability
Cronbach's Alpha
.896
.772
.767
N of Items
15
8
9
The assumptions of normality for Usefulness & EOF were supported by the data as shown in Figure 4 and Figure
5. All Q-Q plots fell along the straight line showing that the variables were normally distributed within groups.
/
Figure 4: Normality for Usefulness
Figure 5: Normality for EOU
The total mean for Usefulness was 27.86 (3.48/5 by the Likert scale) with SD = 6.10, indicating that the
participants found Structured e-Learning Content to be useful, as shown in Table 6. An analysis by group reveals the
means for usefulness for master by coursework students was 28.63 with SD = 5.96, while the mean for master by
research students was 29.86 with SD = 5.28, and the mean for PhD was 26.18 with SD = 6.25. Results of the
ANOVA test showed that F (2.99) = 2.717 at p = .071. As p > .05, there is no significant difference between the
respondents with respect to usefulness by program.
The total mean for EOU was 34.84 (3.87/5 by the Likert scale) with SD = 5.25 indicating that the participants
found Structured e-Learning Content to be easy to use, as shown in Table 6. An analysis by group reveals the mean
for EOU for master by coursework was 34.57with SD = 4.72, while the mean for master by research was 35.35 with
SD = 4.25, and the mean for PhD was 35.00 with SD = 6.22. Analysis using the ANOVA test showed that F (2.99) =
.148 at p = .863. As p > .05, there is no significant difference between the respondents with respect to EOU by
program.
As p > .05 for all tests, the results indicated that there were no significant differences between the respondents
with respect to Usefulness and EOU by program.
Table 6: Means, standard deviations and results of ANOVA tests for Usefulness & EOU by Program
Results of ANOVA
USEFULNESS
EOU
Master_C
Master_R
PhD
Total
Master_C
N
49
14
39
102
49
Mean
28.63
29.85
26.17
27.86
34.57
Std. Deviation
5.96
5.28
6.25
6.09
4.72
F (2,99) = 2.717
p =.071
F (2,99) = .148
Master_R
PhD
Total
14
39
102
35.35
35.00
34.84
4.25
6.22
5.25
p = .863
The total mean for Usefulness was 27.86 (3.48 by the Likert scale) with SD = 6.09 indicating that respondents
from CITM found Structured e-Learning Content to be useful as shown in Table 7. An analysis by group reveals the
means for usefulness of Structured e-Learning Content by CITM was 29.25 with SD = 7.22, while mean for school of
Computer Science (CS) was 27.80 with SD = 5.94, and mean for others was 27.43 with SD = 6.52. Results of the
ANOVA test showed that F (2,99) = .246 at p =. 783. As p > .05, there was no significant difference between the
respondents from various schools with respect to usefulness.
The total mean for EOU was 34.84 (3.87/5 by the Likert scale) with SD = 5.25 indicating that respondents from
CITM found Structured e-Learning Content to be easy to use as shown in Table 7. An analysis by group reveals the
mean for EOU of Structured e-Learning Content by CITM was 34.37 with SD = 5.06, while mean for CS was 35.00
with SD = 4.91, and mean for others was 34.31with SD = 7.00. Results of the ANOVA test showed that F (2,99) =
.146 at p = .865. Again, as p > .05, there is no significant difference between the respondents with respect to EOU.
As p > .05 for all tests, the results indicate that there are no significant differences between the respondents with
respect to Usefulness & EOU by school.
Table 7: Means, standard deviations and results of ANOVA tests for Usefulness & EOU by School
Results of ANOVA
USEFULNESS
EOU
CITM
CS
Other
Total
CITM
CS
Other
Total
N
8
78
16
102
8
78
16
102
Mean
29.25
27.80
27.43
27.86
34.37
35.00
34.31
34.84
Std. Deviation
7.22
5.95
6.52
6.09
5.06
4.91
7.00
5.25
F (2,99) =.246
p =.783
F (2,99) = .146
p = .865
CONCLUSION
This paper demonstrated the current issues towards representation and structuring the e-learning contents. A new
pedagogical model was presented ‘Structured e-Learning Content’ based on the recommendations of Ausubel for
structuring and representing contents. After that, an evaluation was conducted to address the suitability of the new
pedagogical model in terms of EOU and Usefulness among 102 participants. The results revealed that there were no
significant differences between the respondents with respect to Usefulness & EOU. The findings indicated that the
Structured e-Learning Content was acceptable and suitable by program and school, and triggered satisfactory levels of
processing for tacit and explicit knowledge.
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APPENDIX A
Perceived Usefulness
Q1: Using the Structured e-Learning Content for learning would enable me to retrieve and understand the contents
quickly.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q2: Using the Structured e-Learning Content would improve my learning performance.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q3: Using the Structured e-Learning Content for learning would increase my productivity.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q4: Using the Structured e-Learning Content would enhance my effectiveness on the learning process for certain topic.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q5: Using the Structured e-Learning Content would make it easier to display the main contents of research articles.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Agree
(4)
Strongly Agree
(5)
Q6: The Structured e-Learning Content would be useful for learning.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Q7: This content was more difficult to understand than I would like for it to be.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Q8: The Structured e-Learning Content presentation is eye-catching.
Agree
(4)
Strongly Agree
(5)
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Perceived Ease of Use
Q9: The pages of the Structured e-Learning Content look dry and unappealing.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q10: Learning to operate The Structured e-Learning Content would be easy for me.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q11: I would find it easy to get the necessary information from the Structured e-Learning Content.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q12: The Structured e-Learning Content offers intuitive interaction facilities.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q13: It was easy to become skillful in using the Structured e-Learning Content.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q14: It is easy to remember the main elements of articles using the Structured e-Learning Content.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Agree
(4)
Strongly Agree
(5)
Q15: I found the Structured e-Learning Content was easy to use.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Q16: I would learned many things that were surprising or unexpected when using the Structured e-Learning Content.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)
Q17: The good organization of the content helped me be confident that I would learn this lesson.
Strongly Disagree
(1)
Disagree
(2)
Slightly Agree
(3)
Agree
(4)
Strongly Agree
(5)