Section V; Workshop No.: 511
An Intelligent Language Teaching Tool for the Web
Alexandra I. Cristea*, Toshio Okamoto* and Paul Cristea**
*AI & Knowledge Eng. Lab., Graduate School of Info Systems
University of Electro-Communications
Choufu, Choufugaoka 1-5-1, Tokyo 182-8585, Japan
{alex, okamoto}@ai.is.uec.ac.jp
**Department of Engineering Sciences
EE&CS Division, "Politehnica" University of Bucharest
Spl. Independentei 313, 77206 Bucharest, sect.6, Romania
pcristea@dsp.pub.ro
Abstract:The paper describes the building of an intelligent language teaching tool for teaching the
English language via the Web environment, to be used in collaboration between the University
Politehnica, Bucharest, Romania,and the University of Electro-Communications, Tokyo, Japan, at first.
The tool can also be used in a stand-alone mode. This work is based on the concept of the new
classroom of the future, using mainly distance communication systems, and relying less on teachers
and more on computer-based intelligence.
Keywords: Agents, CALL, Student modelling, English teaching, ITS, User adaptivity
1 Introduction
The absence/replacement of human teachers with computers can be introduced up to different degrees,
starting from the teacher who interacts only seldomly with the class, via the teacher who generates the
course material, but never gets in contact with his/her class, leaving the actual teaching to the computer,
and ultimately to a courseware generated solely by the computer. The present research is situated
somewhere in the middle of this trajectory. Our teachers generate the courseware, but don't interact
almost at all with the student. The problem with the usual computer training systems is that they restrict
substantially the individual creativity, by lack of intelligent interaction, as is the case with the human
teacher. The computers filling the place of the human teacher should also provide at least some
adaptivity. The fix reply patterns, especially in language software, lead to mechanical imitation without
proper understanding, and without being able to reproduce the learnt material in similar situations, even
if they present only slight changes to the learnt case. This phenomenon is similar to what is called in
neural network terminology "overtraining", and the lack of the ability to generalize. This existing
problem is the one that the present system tries to avoid, by allowing intelligent features, like
adaptation to the current user, and also like finding the features/patterns of whole user groups. The
paper describes the modules of the system, their functions and collaboration. These modules are: a
"story editor", for the English teacher to use as input tool of multimedia teaching material (text, video,
pictures, sounds) and a “student/ learner module”, in which the actual student learning takes place. The
"story" input by the teacher has to have some specific features, as for instance, it must contain a
questions and answer section, for testing the material contained in the story. The story input by the
teacher is stored in several different databases: a library of expressions to be learned in the text, a VOD
database, an image background database (for instance, a significant image for the respective lesson - as
for instance the picture of the object the lesson is about) and a full text database. The story is indexed in
these different databases, for an easy retrieval. These databases are managed by two agents, which are
interacting with the user via a computer display. The agents can generate full stories, which were input
by teachers, or can search the databases for similar examples, similar situations, or similar - or helpful expressions. In the end of each session/ lesson, testing is performed via the Question and Answer
process. The system, however, doesn't just evaluate the user, but also records and processes the user's
errors. The system also learns the user's profile and builds a current user model, in order to be able to
generate useful advice, appropriate learning material and more questions. The paper shows also an
example of system usage, starting with the story editing and going all the way through user interaction
and system retrieval and collaboration, till the interaction with the student, first passively, as a simple
html type of multimedia class, and then interacting via short movies and pictures retrived by the
communicative agents. The paper ends with some discussion, and conclusions are drawn.
2 User adaptation goals
User adaptation is attempted in many ways now-a-days. This new discovery is very popular, and
“intelligent” computers are very appealing: computers that know what we want before asking them,
computers that do a lot of work in our place. However, we are also aware of the possible pitfalls,
dangers and problems that can appear as a side effect of implementing intelligent systems. All these
systems rely on more or less educated guesses [Paiva] about the user’s intentions and needs. According
to these guesses, a decision is made and an action is triggered. Here is where we see the first possible
danger: as any guess, the system’s guess can be wrong. Many systems just trigger the respective action
without giving the user any chance to adjust this decision.
(1) We are against systems who leave no control whatsoever in the hand of the user, and impose
their decisions on them. Not only can such system behavior lead to user irritation and
frustration, but also even to renunciation, if the desired goal is rejected too many times.
Therefore, regardless of the system’s conclusions, the user should be given the possiblility to
correct them.
(2) Next, there is the other possible pitfall of being over-careful, of asking the user at each step if
s/he agrees or not with the system decision(s). Such systems can make user-modeling much
easier and preciser, but have the disadvantage of increasing the user overhead tremendously.
The system can turn into a bothersome, over-talkative counterpart, that distracts you
continuously with questions and doesn’t let you get any real work done.
(3) We don’t try to implement intelligence, but just useful adaptiveness, i.e., avoid “l’art pour
l’art”. That is, many AI system designers, enchanted by the possible applications of this field,
try to implement a variety of intelligent features, as, e.g., emotions, etc. We don’t deny that
these might prove useful applications for some areas, and definitely interesting research
subjects. However, our main goal is usefulness, and, even if a teacher might get angry at the
students, or be friendlier with some and less with others, as we don’t see at the moment the
usefullness of implementing such features, we don’t make any attempt in implementing them.
Therefore, the above matters have to be carefully balanced. Information on users has to be stored nonobstrusively, the user should be able at each step to correct the system’s decisions and suggest
something else, without, however, being prompted for it all the time. These user corrections can then
be used as feedback to correct the user model. These are the goals we set for our system adaptation.
Concretely, for our system, this translates in having no buttons that ask you to input “I agree with your
decision” or “I don’t agree with your decision”, etc. Also, although the system may automatically
suggest the best learning path, it doesn’t make the rest of the course invisible, so that the student can
choose a different learning path if s/he feels like it, and so on.
3 System organization
The system consists of two main parts, one for the teacher/tutor user, for course-authoring purposes,
and the other one for the student user, who does the learning.
3.1 Authoring Module (Story Editor)
The information exchange from tutor to system means input of lessons, texts, links, asking for help in
editing, etc. The data from the tutor is stored in a structured way, as can be seen in fig. 1, 2. Input can
be of audio, video, graphical or text nature (fig.1). The first three types must have also a text version
attached. The text is further on divided into lessons, with automatically generated indexing. Each
lesson (e.g., lesson 190-2 in fig. 1) is subdivided into exercises, conclusion, keywords, title,
explanation, pattern to learn, and one or more TEXTs. The TEXTs are subdivided into exercise,
keywords, pattern, title and actual main text. These restrictions are necessary instruments for the two
system agents to work with, as will be shown later on in this paper.
Teacher-name
|
AUDIO
FILMS
PICTURES
LESSON
|
113-1
115-1
119-1
123-1
190-1 196-1 65-1
114-1
118-1
119-2
123-2
190-2 197-1 67-1
|
EXERCISES
conclusion
keywords title
TEXTS explanation pattern
|
TEXT1 TEXT2
|
exercise1
keywords
pattern
text
Lesson
Text 1
Text 2
...
Text i
...
Text n
Lesson
Text 1
Text 2
...
Text i
...
Text n
w
1
title
Lesson
Text 1
Text 2
...
Text i
...
Text n
New
Lesson
Text 1
Text 2
...
Text i
...
Text n
Lesson
Text 1
Text 2
...
Text i
...
Text n
Relatedness
connections
Priority
connections
Test point
2
Fig. 1: Data structure Fig.
2: The subject link database
3.1.1
TEXTs
The basic building brick of the whole structure is the TEXT element. Each video/audio recording has to
have a corresponding TEXT (of dialog, etc.). For each text, it is the teacher’s responsibility to decide if
video is necessary, or if audio suffices (as audio requires less memory space and allows a more
compact storage and a speedy retrieval). Each TEXT also has (beside of main text, etc.), the attributes
described in fig. 1,2. It is natural to use titles and keywords for search and retrieval, but we use,
moreover, the explanation and conclusion files for the same purpose.
3.1.2
LESSONs
One or more TEXTs (with video or not) build a LESSON. Each LESSON also has (beside of texts,
etc.) some similar attributes to the TEXTs (fig.1).
3.1.3
Connections
When creating a new LESSON, a teacher has to add Priority Connections, i.e., to indicate a learning
order, via arrows in a directed graph. Furthermore, the teacher (courseware author) should add
connections between related SUBJECTS, with indirect links. We call these Relatedness Connections
and use them for related subjects with no specific learning order. These relations are useful, e.g., during
tests: if one of the subjects is considered known, the other one should be also tested. The main
differences between the priority connections and the relatedness connections is that the first ones are
directional, weightless connections, whereas the latter are non-directional, weighted connections. The
reason of using two types of connections is mainly to allow the separation of the learning flow
indications from the teacher, that remain fixed, and the system’s inferences, that are adaptive and
change in time, according to the student model. After these priorities and links are set, the system will
then automatically add more links via keyword matching, from explicit keyword files and keyword
searches within subjects. Priorities among the texts of a lesson are set implicitly according to the order
of the texts, but can be modified, if necessary. The teacher / multimedia courseware author can decide
if it is more meaningful to connect individual texts, or entire lessons, for each lesson. The way a new
lesson is introduced, by asking the teacher to set at least the previous and the following lesson in the
lesson priority flow, is shown in figure 2 (steps 1,2). Final priorities will be set by the system according
to findings (teacher's input, keyword matching).
3.1.4
Test Points
The teacher should mark TEST POINTS (figure 2), at which it is necessary to pass a test in order to
proceed (these tests can be at any SUBJECT level).
3.2 Student Module
The information exchange with the student is more complex. It contains usage of the presented
materials, implicit or explicit advice, the student’s advice requests, queries, searches, gathering of data
on the student by the two agents, the Global Agent (GlA) and the Personal Agent (PA). Each of these
agents has its own database on the student(s). The GlA stores general features on students, and the PA
stores the private features of each student.
3.2.1
Student models and agents
The system gradually builds two student models: a global student model (GS) and an individual
student model (IS), managed by two intelligent agents: the personal agent (PA) and the global agent
(GlA). The reason for doing so is that some features, which are common to all students, can be captured
in the GS. A realistic user model has to take into consideration the influences a system can achieve on
the user, in order to allow an easy interpretation of the current state, as well as an easy and clear
implementation of the user model. In this work, we mean by agent a “computer system situated in some
environment”, “capable of autonomous action”, “in the sense that the system should be able to act
without the direct intervention of humans”, “and should have control over its own actions and internal
state”. These agents’ intelligence is expressed by the fact that each agent “is capable of flexible
autonomous action in order to meet its design objectives”, and that it is “responsive” (it perceives its
environment), “proactive” (opportunistic, goal-directed), “social” (able to interact) [Jennings], and of
an “anticipatory” nature (having a model of itself and the environment, and the capability to pre-adapt
itself according to these models) [Ekdahl].
3.2.1.1
The GS
The GS contains the global student features:
 the common mistakes;
 favorite pages, lessons, texts, videos, audios, grading of tests’ difficulty (according to how
many students do each test well or not);
 search patterns introduced, subjects accessed afterwards: if many IS use the same order, than
they are recorded in the GS.
3.2.1.2
The IS
The IS contains the personal student features:
 the last page accessed;
 grades for all tests taken, mistakes and their frequency; if the student takes the test again and
succeeds, his/her last grade is deleted, but his/her previous mistakes are collected for future
tests;
 the order of access of texts inside each lesson;
 order of access of lessons (this can be guide to other students: “when another student was in
your situation, he/she chose...”);
 frequency of accessing texts/ lessons/ videos/ audios, etc. - for guidance and current state
check;
 search patterns introduced, subjects accessed afterwards (to link patterns with new subjects
that the system didn't link before).
3.2.1.3
The PA
The role of the personal agent is to manage the information gathered on the user, and to extract useful
user guidance material from this information. Each step taken by the user inside the environment is
stored, and compared with both what was proposed to the user, as well as with what the user was
expected to do (from the PA’s point of view). The differences between previous expectation and
current state are exploited, in order to be used for new guidance generation.
Beside of analyzing the own user and extracting knowledge from the data on him/her, the PA is able
to request information from the GlA, about, for instance, what other users chose to do in a situation
similar to the PA’s own user current one. Furthermore, the PA can contact other PA’s with similar
profiles (after a matchmaking process), and obtain similar information as from the GlA, only with more
specificity. The PA can decide to turn to another PA if the information from the GlA is insufficient for
a decision about the current support method.
Every time a user enters the system, the PA decides what material should be studied during that
particular session and generates a corresponding list. Therefore, the course index is dynamic, not static.
3.2.1.4
The GlA
The global agent averages information from several users, in order to obtain a general student model.
The deductions of the global agent are bound to be non-specific. The GlA is to be referred before the
PA starts looking for information from other PAs, process that can be more time-consuming. Therefore,
the role of the GlA is to offer to the PAs condensed information, in an easily accessible, swiftly
loadable form. From this description, it is clear that the GlA is subordinate to the PA (from the student
user’s point of view). The GlA cannot directly contact the student user – unless the PA explicitly
requests it. In this way, the generation of confusing advice is avoided.
3.2.2
Subject material’s relatedness weight computation
As shown previously, the priority connections between lessons have no weight attached, but the
relatedness connections have weights. These weights are changed interactively, as they reflect ‘how
connected’ two subjects are. This information is useful for both guiding of the student during learning,
as well as for testing the student. Weights’ values are initialized as strong, when a teacher selects the
respective links, and they are weaker, if the system (via the GlA) deduced them out of keyword search
information (eq.1). The weights are changed by the GlA, according to the behavior of the students
within the environment (eq. 2).
wA,B0=1: teacher’s selection; 0.5: system’s generation; 0: rest; (1)
wA,B t+tconst =  wA,B t + f1(no. of times connection A,B activated1) +
+ f2(no. of times connection A,B was accepted, when
proposed in relation to unknown subject)
+
1
by the user or by other users, depending if it is a weight in the global model or the personal one;
+ f3(no. of times connection A,B was accepted, when
proposed in relation to query)
+
+ f4(no. of times tests related to connection A,B were
solved satisfactorily or not)2;
(2)
where: (0,1): forgetting rate; f1~f4: linear functions;
wA,B>03: weight between subjects A and B; t: time; tconst: period for weight update4;
It is easy to see from these connections, that related subjects will form cluster-type formations.
However, within these clusters, weights expressing the relative relatedness of the cluster components
also appear. The PA of each student makes its own copy of the subject link database, and modifies it
for its student, according to his/her independent behavior. These modifications are, just like the
modifications of the GlA’s subject database, of adaptive nature. The PA has to check from time to time
with the GlA, in order to decide on possible updates of its own subject database, as the GlA is
adaptively reflecting the current average trend in the global subject database. The PA generates the
‘next learning steps’ (the current index) and the ‘review suggestions’, in two separate windows. The
latter contains suggestions to consult lessons and texts, which are connected to the errors which
appeared in the student’s current and previous tests. The PA generates the current index following the
normal learning flow set by the priority order. Going in the opposite direction can happen only when
the student cannot answer some tests or quizzes satisfactorily. In that case, the review suggestion
window activates, as explained, connections to previous subjects, to direct the student to where he/she
can fill the gaps in his/her knowledge. The PA also has to choose between two or more priority links.
The usual procedure is to present all of them to the student in their relatedness connection weight order.
If no such connections exist, than a random order is assumed. However, the current user’s choices will
be recorded by the PA, reported also to the GlA and be reflected in the new connections. That means
that the current user’s choices might be recommended to the next user. The ignored, non-related links
will appear next time lower on the list.
3.2.3
Resuming the agents’ structure
From the described interactions between agents and databases, and between the agents themselves, it is
clear that the agents of the system work in two ways. The first way is based on the embedded
rule/knowledge systems, which try to foresee, prevent and solve conflicting situations. The second way
is as learning objects, which can adaptively change their representation of the subject space, by creating
and deleting links and changing weights.
2
3
4
can be positive or negative
if wA,B = 0, the relatedness connection dissapears
the weights are not updated at every move, otherwise computation becomes too time-consuming
4 Example usage
We have made tests of both courseware usage and courseware design. Fig. 3 shows a snapshot of
system usage as a student, whereas fig. 4 shows a snapshot of courseware creation. In the story editing
interface, the enforcement of the decided courseware structure can be noticed. This environment offers
many more functions, such as password guarded accounts for teachers, correction and insertion of new
material into old courses, search and visualization of all courses existent on the server, or only the
personal ones, help and guidance during course design, etc. The fig. 3 snapshot shows how multimedia
and text can be combined to give a student complete information about a specific topic. The
information on the example video in the image is also reproduced as text. This particular example
actually deals with one of the common mistakes made especially by Japanese people, of answering
“yes” when they mean “no” and viceversa, due to cultural and language differences.
Fig. 3: A personalized text and corresponding movie
Fig. 4: Lesson Input interface.
We are planning to extend the design possibilities of system feedback, to use different teaching
strategies, and to allow a teacher to design various exercises, moreover, to extend these exercises
adaptively. However, the currently implemented version does not support this function, but is mainly
aimed at generating the appropriate list of following learning steps, according to both teacher’s
indications, and system’s adaptations.
5 Discussion and Conclusions
We have shown here the building blocks of an adaptive WWW system. These structures are more
general than presented here, but are not entirely domain independent. Our courseware structure is
aimed mainly at representing a field in which, with the help of texts, videos and audio presentations,
the whole material can be presented. Therefore, we can easily imagine that it could be used, beside the
current usage of academic English teaching, as a system of teching French or Japanese, for instance,
but would probably have to be extended, if it would have to fit a Math or Physics course.
We have started with the same premise as in [Papanikolaou], that our learners are motivated adults,
who follow a goal-oriented learning style.
At the moment, our system adaptation is aimed at constructing individual learner models and an
average model for reference. However, experiments might show the necessity of refining the scale of
modelling, and introducing such models corresponding to different cognitive styles. This might lead to
an optimized handling of brilliant, fast, or advanced students on one hand, and beginners or slow
students on the other hand. We expect that the most problems will occur with average students,
especially due to the effect of different backgrounds. As our course is not self-contained, and is just an
upgrading course, one of the main problems relies in the fact that the background knowledge is not
completely covered by the course, so is difficult to model. I.e., our graphs will give us only partial
answers regarding the lacking knowledge, so this problem has to be treated separately.
6 References
1. Ekdahl, B., Astor, E. and Davidsson, P. (1995) “Towards Anticipatory Agents”, Woolridge, M.,
Jennings, N.R. (Eds.), Theories, Architectures, and Languages, Lecture Notes in Artificial
Intelligence, Springer Verlag, 191-202.
2. Jennings, N.R. and Wooldridge, M. (1998) “Applications of Intelligent Agents”, Agent Technology
Foundations, Applications, and Markets, Springer Verlag.
3. Paiva, A., Self, J. (1994) “A Learner Model Reason Maintainance System”, ECAI94, Ed. A. Cohn,
John Wiley & Sons, Ltd.
4. Papanikolaou, K. A., Magoulas, G. D., Grigoriadou, M.(1999) “A Connectionist Approach for
Adaptive Lesson Presentation in a Distance Learning Course”,Proc.IJCNN’99,Washington DC
(published as CD).