How To Make CALL Systems ...

How To Make CALL Systems More Intelligent
IK-Sung Ohm (Inha Technical College)
Ohm, Ik-Sung. (2003). How to Make CALL Systems More Intelligent.
Multimedia-Assisted Language Learning, 6(2), 111-134.
Recently, some researchers have been actively involved in trying to combine
computer-assisted language learning (CALL) with language technology (LT)
(such as computational linguistics, language engineering, or natural language
processing) into what is often referred to as Intelligent CALL (ICALL). The
meaning of 'intelligent' in ICALL can be defined in varied ways from
interdisciplinary views, but one understanding of the term is that of CALL
incorporating LT for analyzing language learners' language production, in
order to provide the learners with more flexible and intelligent feedback and
help in their language learning activities. An intelligent language tutoring
system (ILTS) has sought to apply the technology of natural language
processing and intelligent tutoring systems (ITSs) to enhance its language
pedagogic role, and often been placed by its practitioners in the field of
artificial intelligence (AI), rather than in LT, more specifically in the subfield
of AI known as ITS. However, the traditional CALL 'killer applications' have
had very little input from LT research nor from ITS. This paper streams
down from an intention to make CALL more supportive of users by grafting
ongoing efforts of the ITS and neighboring LT communities. Though CALL
systems originally have a simple architectural design just to provide students
with practice in language production, it can eschew static or page turner
notions of frame-based or generative models in favor of an adaptive student
model, which will give the system the power to adaptively control both
tutoring activities of the system and language learning activities of the
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student. The key issues here are how to integrate student model technology
into CALL systems and what individual learner variables the system can
possibly use to make itself evolve into Adaptive CALL (ACALL) with the
control of intelligent and adaptive behavioristic feedback.
I. INTRODUCTION
The need for providing effective, appropriate and flexible language teaching and
learning environments is rising, given the increasing demand for communicative competence (Hymes, 1972) for the utilitarian purpose from the work place, and the cost and
energy drained in the settings of foreign language learning. The solution to this need
was expected to naturally come out along with the rapid progress of science and technology in education. The research for enhanced language pedagogic systems has formed
two main streams in computer-based language teaching and learning; ICALL via CALL
and ILTS via ITS, both are outgrowths of CAI.
A CALL system like a traditional CAI model literally acts as an assistant of electronic page turner on a list of frames of text and graphics. An ILTS, on the other hand,
tries to use AI knowledge representations and inference techniques to represent and reason about the subject matter, the student and pedagogical principles. (Sleeman and
Brown, 1982). Although CALL research has been carried out for over 30 years, few language learning systems have made the transition to ILTSs or ICALL as much as
expected. The main reason for this failure of evolution is largely because the development of ICALL/ILTSs is difficult, time-consuming, and costly.
Krashen (1985) annotates that the prime role of a language teacher should be to expose students to plethora of comprehensible input. And constructivists advocate this exposure should be conducted in a learning-by-doing context just to be effective. Their
implications for CALL design approach entail student motivation of trying things out,
formulating hypotheses and testing them and free initiated actions of the learners.
However students cannot do this in a vacuum so a tutor must get involved to help
them discover answers by themselves.
This issue of tutoring in the learner-centeredness is closely related to the idea of individualized instructions. Contrary to the teacher control of language interactions, in a
learner-centered classroom the student has a fair degree of autonomy in participating in
classroom activities. Also in learner-centered materials the student can have more
choices about what to work on. However, this band-wagon effects of learner-centered-
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ness expecially in CALL communities have come to the point of serious reflection as
Garrett (1995, p.345) raises doubts.
...
but it may be
learner-centeredness
premature to assume that a high degree of
necessarily
benefits
language
learning.
If
learners
themselves do not understand their own styles and strategies you're not
doing them good by turning over control of the learning activities to them.
For the realization of autonomy in learning activities, more care and intelligence from
a computer tutor is indispensible. Within this care and intelligence, the student can
properly initiate communications and the tutor can intelligently provide questions and
tools appropriate to the student's level of expertise. For a CALL system to be intelligent
enough to put on this adaptability, the system must be able to react continuously according to the student's learning. Besides presenting learning materials, the system
should serve right or wrong decision-making duty interpreting the student response.
However, this dichotomizing rigidity does not help arrive at the learner success in the
learner-centered learning context. CALL systems still have to perform some other special care for students as Self (1999, p.351) proposed:
The use of the word 'care' is deliberate A student model is what enables
a system to care about a student. Of course, system designers and teachers
care about students too - but somehow our critics have implied that we care
less because we want our systems to care more. A system without a student
model cannot care about an individual student. If the use of a student model
is a defining characteristic of an ITS then ITSs care; non-ITSs do not.
Reviewing the history of CALL evidently shows that CALL systems were developed
with the intention of easing the burden of the teacher's drudgery work and they still
seem to stay in the same rudimentary stage and even unable to set the direction to
move onto. They are only echoing the importance of learner-centered environments. On
the other hand, ILTSs have been showing human-like intelligence in achieving the
pedagogical goal of computer tutor, i.e. a simulated human tutor. As Self diagnosed it,
the reason for CALL's failure to advance onto the level intelligent enough to care a student is because CALL has no student model incorporated in the system (McCalla &
Greer, 1991).
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The remainder of this paper shows how the various aspects of a student model incorporated into the language learning system. The next section details the background
framework of CAI systems and describes related research on a student model. Section 3
is focused on the use of a student model in adaptability, error-specific feedback generation and diagnosing learner variables. Finally, section 4 is a conclusion of the paper.
II. STUDENT MODEL FOR LANGUAGE LEARNING SYSTEMS
1. Paradigm shift from CAI to ITS for Intelligence
The paradigm shift from CAI systems to ITSs can be illustrated as an inevitable
migration from static storyboard representation of instructional material to dynamic
knowledge based representations to achieve the goal that properly designed computer-based tutoring systems have proven highly effective as learning aides (Shute and
Regian 1990), that is, being able to intelligently respond to the individual student's
learning style in delivering customized instruction.
1) Linear Programs
Skinner's (1958) teaching machine was simply programmed mechanical machine in a
linear order, based on the principle of operant conditioning. Material to be learned was
split into very small pieces called frames with a certain presentation order determined
by the learner's responses to previous frames. Most frames had simple questions with
immediate 'right or wrong' feedback. This reinforcer is anything that strengthens the
desired responses, and could be a verbal praise or a feeling of increased accomplishment
or satisfaction. This behavioristic CALL (Warschauer, 1996) proceeds regardless of students' understanding of previous frames. These programs do not provide individualization; all students received exactly the same material in exactly the same sequence irrespective of their abilities, background, and previous knowledge of the domain. Carbonell
(1970) commented that with this type of systems the computer does little more than
what a programmed textbook can do. However, it is important to remember that the
Skinner box was essentially a means of gathering a lot of data about the learning behaviour of rats and pigeons in as short a time as possible and had a lasting influence
on the use of machines in education.
2) Branching Programs
This programmed instruction was improved by Crowder's (1959) proposal of using
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student's responses to control the material being presented to the student, which resulted in the branching programs. They still have a fixed number of frames, but are
able to comment on a student's response and then use it to choose the next frame. The
main features of the programs are (1) offering corrective feedback, and (2) adapting the
selection of teaching material to the student's current stage. By pattern matching techniques alternative answers are allowed as acceptable or partially acceptable, rather than
totally correct or incorrect as designed by Skinnerian systems. However, the teaching
material grows too large to be manageable through straightforward programming and so
a special breed of programming languages, called 'authoring languages', are developed
for creating CAI material.
3) Generative Systems
The intention of generative systems was to do away with all the pre-stored teaching
material, problems, solutions and associated diagnostics, and actually generate meaningful problems. This helped drastically reduce the memory usage and systems were
able to generate and provide as many problems as the student needed to some desired
level of difficulty.
However, these programs still are restricted to drill-type exercises in the domain as
well as structure. They do not possess any real knowledge of the domain nor they can
answer questions. The gap between the student's cognitive processes and the internal
workings of the programs is too wide (Sokolnicki, 1991). Only parametric summaries of
behavior are used to guide problem generation and the student model is primitive, mostly consisting of only an integer to indicate the level of the student's competence.
Yazdani (1986) notes that none of these traditional CAI systems has human-like knowledge of the domain it is teaching, nor can it answer the serious questions from the
students.
4) Adaptive Systems
Traditionally, problems of developing adaptive instructional systems were investigated in the area of intelligent tutoring systems (Burns & Capps, 1988). Adaptivity has
been one of the goal features of any CAI, since the terminology 'ITS' was first coined
by Sleeman and Brown (1982). An ITS is a computer-based system intended to provide
intelligently interactive, adaptively effective, and flexibly individualized learning and tutoring through the application of artificial intelligence techniques and knowledge representation (Sleeman and Brown, 1982), which distinguish them from the traditional CAI
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systems.
Three distinctive features of an evolved ITS are instructional planning (also referred
to as curriculum sequencing), mixed initiative interaction and offering adaptive feedback.
Instructional planning aims at motivating the students and confirming that they have the
ability to solve all problems in the domain. The planner has a dynamic planning capability so that it can generate plans, monitor the execution of the plans, generate new
plans and modify the plans when necessary. It must be adaptive and customize tutoring
plans for each student. Mixed initiative interaction, which allows the tutor to share control over the session with the student, can make an ITS respond to questions from the
student about instructional goals and contents. Offering feedback or hint by providing
the student with a piece of information that the tutor desires will stimulate recall of the
facts needed to answer the question correctly.
Another feature of adaptive systems was formed, as Wenger (1987) calls for, from a
move towards a cognitive oriented form of software engineering in which cognition is
central, rather than focusing on computational models of the domain and pedagogy.
Thus, there came two opposing views of ITS, the traditional view of computers as instructional delivery devices versus the emerging view of computers as a tool for exploratory learning, which were combined into adaptive systems.
2. Definition of Student Model
In the context of intelligent tutoring systems, the purpose of a student model is to
represent the computer system's cognitive features about the learner's knowledge and
behavior, so that the tutor can give instructions tailored to the best achievements of the
student. In order to allow language teaching pedagogy to be individual and adaptive, a
primary task of the system is to capture the student's understanding of both the subject
matter and individual differences (McCalla, 1992b).
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[Figure 1] Student Model and ITS Architecture
The student model contains the knowledge for identifying a student's current state
of understanding of the domain knowledge which the student is supposed to learn and
utilizes this stored knowledge to adapt the instructional content of the system to the
student's needs. With this student model, the difficulty of material and any necessary
remediation (which is called passive sequencing) can be appropriately controlled within
the system. If a student model involves the modification of instruction to accommodate
the needs of the student being tutored and to adapt to the student's progression, the
model interprets where the student stands and accordingly provides feedback on how he
or she proceeds.
This intelligent behavior is dynamically generated by the student model analyzing the
existing background knowledge about a domain and about student's learning of the domain knowledge. Then the model updates its knowledge database and relations about
student's knowledge and skills in the knowledge domain. This description of the student
in the teaching process shows that how the student perceives the task, asks and answers the questions, what the student is doing and what is the student's response on
the warning and the help of the teacher. The process of student modeling includes also
measurement of his or her behavior and attitudes, which is diagnosis of his or her
knowledge that is the base of the teaching process.
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However, it is not an easy job to enable the student model to handle the student's
individual characteristics of the cognitive features which are indicative of the student's
level of understanding, preferences, and progress through the course. According to Zhou
and Evens (1999), these cognitive features may only be assigned nominal or scalar types
since they are data facts and relations about student's knowledge and skills in a knowledge domain. For example, the cognitive feature 'motivation' may be assigned either the
type of nominal or scalar. In case of a nominal type the motivation feature will need
values like low, medium, high.
2. Student Model Architectures
To capture the differences or similarities between the expert and the student model
in terms of misconceptions and missing conceptions (VanLehn, 1988), the overlay model
and the buggy model have been devised in the research field of ITSs. These two models
are generally based on the comparison of the system-generated solutions with the student's responses of the domain knowledge. In order to identify a student goals, they
compute the student's knowledge dynamically at every stage of the program.
With the overlay model the system simply treats the student's knowledge as a subset of the expert knowledge on the subject matter. As the student learns, the subset
grows, and the model keeps trace of the subset. Learned part of the knowledge are just
marked in the expert model. This model assumes that the student would not learn anything that the expert does not tutor, it cannot recognize misconceptions or bugs that the
student may have or acquire during tutoring (Brusilovsky, 1994).
The differential student model, an extension of the overlay model, is devised to partition the domain knowledge into already presented knowledge and that which has not
yet been presented to the student. Then the buggy model extends the knowledge with
the addition of a bug library so the system can capture the student's misconceptions
about the subject matter by using bug rules in the system. Bug rules are erroneous
variants of the correct rules which the student has to apply in order to fulfill a task or
exercise. The buggy plan which represents an alternative erroneous plan stored in the
system captures the kind of misconception about the required learning plan the student
made. The discrepancy between the student's performance and the expert's performance
is determined in the buggy model. The student's answers are analyzed and the deviation
from the expert's knowledge is determined. By applying an overlay model over the
combined expert knowledge and bug library the goal of tutoring is to grow the student's subset of the experts knowledge while eliminating any missing conceptions and
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misconceptions.
3. Student Diagnosis: Model Tracing
Intelligent teaching and learning systems infer a model of student's current understanding of the subject matter and use this individualized model to adapt the instruction
to the student's needs. In order to make the student model evolve student diagnosis is
conducted which is a process that manipulates student's data structure. A diagnosis
system uncovers a hidden cognitive state from observable behavior and reports a conclusion about what was the corresponding behavior of the student during his or her
learning process.
As in real life good teachers do not only register results but they try to define student's mistakes, interactions between the student and the ITS need to be analyzed. This
analysis is a typical procedure for checking answers to questions posed by the system
or analyzing the steps taken during a problem solving session. Thus, the goal of student
diagnosis is focused on enabling students to overcome and correct their misconceptions
which Carbonell (1970) viewed as symptoms of diseases.
The model tracing, one of the most elementary techniques of student diagnosis, assumes that the approximate mental state of the student is available to a diagnostic
algorithm. By adopting the model tracing technique, the system can possibly capture not
only the cognitive model of the domain knowledge but also individual differences of the
student. At each step of problem solving, the interpreter can suggest a set of rules
which can be applied, but opposite to it the deterministic interpreter allows only one
rule. Diagnosis activates all available rules to achieve the set of the next possible states.
One of these states must correspond to the state generated by the student. The cognitive diagnosis functions in a capacity similar to the one-on-one situation a student
would encounter when working with a human tutor.
III. ADAPTATION TO INDIVIDUAL FEATURES
In general, the adaptive learning environment of a language teaching and learning
system is characterized by a knowledge base, a tutoring strategy and, last but not least,
a student model. This student model is what makes the system adaptive since it is used
in order to modify instruction to accommodate the needs of the student being tutored.
The computer tutor, therefore, can monitor the student's progress through a knowledge
base and interpret on what stage the student is and provide sophisticated feedback on
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how he or she should proceed.
From a more specific point of view, the goal of a student model is to develop highly
individualized language learning environments in which the student and computer tutor
can enjoy a flexibility that closely resembles what actually occurs when student and human tutor sit down together for one-on-one tutoring (Seidel & Park, 1994) which has
long been known to be dramatically more effective than both classroom instruction and
traditional CAI (Bloom, 1984). With such flexibility the system can be fully adaptive to
the individual student's on-going learning needs when the students work independently
for their own purposes and at their own pace. Most frequently, tutors care more for
those they know so well. Without the recognition of the unique learning needs of the
student, few students should have the opportunity to achieve their potential. In ESL or
EFL teaching and learning environments, special care for the learner's affective area is
a critical factor for student success. No student will be left behind if these individual
learning needs are properly taken care of.
Without a student model, a computer-based learning system self-evidently performs
in exactly the same way with all users, since there is no basis for sophisticated decision-making otherwise. But each student is unique: students have diverse prior knowledge and intellectual strengths, different learning styles, interests and aptitudes, and
varying sociocultural backgrounds. Actually a student model is expected to support an
intelligent learning environment (ILE) in understanding the individual student well
enough to be able to determine individualized actions. At present, however, computational utility is only the measure for student models which can be assumed as databases
for an adaptive decision-making of an intelligent language learning software.
1. Intelligence to Error-specific Feedback
To be considered as intelligent, the student's response analyzer has to decide whether the answer is correct or incorrect, find out what exactly is wrong or incomplete, and
possibly identify which missing or incorrect knowledge may be responsible for the error.
Furthermore, an intelligent analyzer can provide the student with extensive error feedback and update the student model.
Garrett (1987) and James (1998) describe four types of feedback in relation to error
analysis in intelligent instructional systems: (1) systems which present only the correct
answer; (2) systems which pinpoint the location of an error on the basis of the computer's letter-by-letter comparison of the student's input with the machine-stored correct version (so called "pattern markup"); (3) systems which base their error analysis on
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anticipated wrong answers (error messages associated with possible errors are stored in
the computer and are presented if the student's response matches those possible errors);
(4) systems which use NLP and provide a linguistic analysis of the student's response.
Unlike the more traditional drill and practice programs which use one of the error
checking mechanisms described in (1)-(3), ILTSs which implement NLP overcome the
rigidity of the response requirements of traditional CALL. The programs generally consist of a grammar and a parser that performs a linguistic analysis on the written language input. When learner errors are discovered by the system, the program generates
error-specific feedback explaining the source of error.
Immediate and individualized feedback has recognized as a significant advantage of
CALL over more traditional language instruction. A number of studies in the recent
years (Heift, 2001; Nagata, 1993) have investigated metalinguistic feedback vs. traditional
feedback in different language teaching and learning environments. It was found that
NLP-based intelligent feedback which explains the source of an error is more effective
than traditional feedback. Several studies found that metalinguistic feedback is very effective to adult second language learners. This salient feature of the instructional systems is the ability to generate highly specific feedback whether in form of error analysis
or of responses to learner initiatives. Sophisticated error analysis is crucial for adaptive
interaction between the computer and the learner. Van der Linden (1993) observes that
the two main functions of feedback are to inform and to motivate. It's not much known
about the extent to which help and feedback actually contribute to the language development of the average learner. Many learners seem to ignore the availability of help
materials even in tasks where it can be seen clearly that they need them. However, one
of the best known aspects in ICALL is the development of sophisticated parsers and the
effort to tailor their output to provide error-specific precise feedback for the benefit of
learners. Because a hint often helps the student make an inference needed to arrive at
an answer to a question or to make a correct prediction of system behavior (Hume,
1995).
1) Issues on Generating Feedback
Fundamental to tutor-student dialogues in interactive learning systems are the monitoring, feedback and corrective actions which are necessary to ensure that the learning
process is progressing along the designed paths. Considerations should be given to two
major problems that are related to providing feedback; (1) the timing of offering feedback, and (2) the amount of feedback (Woolf, 1992).
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Depending on the tutoring environment, the timing of feedback can be very important
for its effect on the student. Instant feedback and delayed feedback are two typical approaches that can be used. Instant feedback is especially suitable to a context of positive
feedback. When the response has been parsed correctly or the goal of a task has been
completed, instant feedback can offer encouragement to the student and display a clear
guide to their progress. Anderson and Reiser (1985) articulate that it is difficult for students to find and correct their own errors if they have made a mistake and then completed a series of steps based on that mistake until they have arrived at an impasse.
The student may be overloaded with information when he or she tries and backtrack to
the source of the error and in the end may not learn the correct procedure. Instant feedback immediately opens a message box that pronounces an error has occurred. This allows the error to be recognized by the system and focuses the student's attention to it.
Delayed feedback is more appropriate in a context where more hypothesis-testing is
encouraged. Delaying error feedback gives the students the opening to discover their
own error and provide their own solution. In ill-defined domains, where there may be
many correct or adequate solutions, this allows the system to provide a more realistic
environment. Delayed feedback works well within a scheme where the student can request help. Identified errors can be kept as a history by the system and relayed back to
the student when he or she requests additional feedback. The student can then be allowed to retrace the paths to the error points and to try alternative choices from these
points. This backtracking and retesting can be useful if the goal of the system is to encourage the students to find multiple solutions or to develop their own problem-solving
strategies by exercising meta cognition.
The amount of feedback for a student is also a matter of tactical consideration. Too
much feedback, and students may not have to think for themselves and get the feeling
that the environment is more of a static tutorial. Without enough feedback, students
may become frustrated as they may not see the results of their actions or find themselves trapped in undesirable situations due to unnoticed mistakes.
The amount of feedback for the students is dependent on the knowledge representation of the environment and the feedback approach that is being followed. The type of
knowledge representation technique that is used to represent a domain within a system
usually defines how its feedback is to be generated. In small domains, common problems
and errors can easily be detected and appropriate canned feedback is stored for presentation to the user when needed. This method is dominant with systems that build libraries of common bugs. This technique of explicitly storing feedback is especially suit-
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able to storing multiple levels of detail about a problem. This allows increasingly detailed explanations to be delivered.
The student may well want to know whether the current prediction is correct or not.
The interpretation of analytical results of Cho's (2000) survey with CIRCSIM-Tutor
systems to understand who prefers immediate feedback and which students do not ask
for immediate feedback during the prediction phase shows that the students who made
poor predictions are eager to know their mistakes immediately.
2) Extended Categories of Student Answers
Human tutors make use of different response strategies for variant categories of student answers. When the answer is in an acceptable range of correctness, the tutor offers
an affirmative acknowledgment and proceeds to the next question. With an incorrect answer, the tutor, depending on the category to which the answer belongs, may either just
give away the correct answer or repeat the question for the student. However, implementing repeat strategies for a computer tutor, the extended category list of student
answers should be defined. For example, Kim (Kim et al., 1998) differentiated several
categories of student answers. By adding misconceptions, grain of truth information, and
information for deriving near misses to the knowledge base, additional categories of student answers can be recognized in a more intelligent mode. Below are the categories
used by the updated CST v. 2 (Kim et al., 1998):
(1) Correct
(2) Partial answer, i.e., the answer is part of the correct answer
(3) Near miss answer, which is pedagogically useful but not the desired answer.
(4) I don't know answer
(5) Grain of truth answer, where the student gives an incorrect answer, but also indicates
a partially correct understanding of the problem.
(6) Misconception, a common confusion or piece of false knowledge about the concept being
tutored
(7) Other incorrect answers
(8) Mixed answers, i.e., a combination of answers from the other categories
An advantage of using these detailed categories is that the computer tutor can give
feedback to the student responses in a more intelligent way. A large variety of feedback
messages are also prepared and classified according to the extended category list.
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3) Error-Checking Mechanism
In addition to a grammar and a parser to parse natural language, a computer tutor
utilizes additional error-checking modules which get called back when processing a student spoken answer. For example, consider the task in (1a) where the student was
asked to utter a sentence with the words provided.
(1a) Mary likes to take a walk.
(1b) Mary like to teik walk.
Suppose the student's spoken words are recognized and converted into written words
through the speech recognition module and the answer is given to the error checking
modules as in (1b). The student answer contains a few of mistakes: a grammatical error
in subject-verb agreement, a spelling or pronunciation mistake, and a missing article.
The grammar would detect the agreement error and the remaining mistakes are checked
by additional modules of the system. Figure 1 illustrates the modules of the Natural
Language Processing system.
[Figure 2] Error analysis mechanism based on linguistic structure
The Spell Check module analyzing student input also extracts the base forms of each
word from the grammar. The uninflected words are needed to determine whether the
student answer contains the words provided. When defining an exercise, possible an-
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swers of a given task are stored and the Answer Check module determines the most
likely answer (MLA) the student intended. The Answer Check module further matches
the extracted base forms with the MLA. If any of the words in the task are not contained in the student answer, the system will report an error.
The following Extra Word Check and Word Order Check refer to additional words in
the student answer and errors in word order, respectively. These two checks are handled by the grammar. In the Grammar Check module, then, the sentence is analyzed by
the parser according to the grammar rules and lexical entries provided. The Match
Check looks for answers in an acceptable range by string-matching the student answer
with the MLA. If the sentence passes the Match Check successfully, the sentence is
pronounced correctly. If not, an error is reported to the student.
The system can be organized in a way that if a module detects an error, further
processing is blocked and only one error at a time will be displayed to the learner. van
der Linden (1993) pointed out that displaying more than one feedback message at a time
would make the correction process too complex for the student.
2. Representation of Learner Variables
As the content of student models in instructional systems varies widely, constructing
student models for language teaching and learning systems should be approached differently from the formal fields of intelligent learning systems. In order to provide highly
individualized language training, a CALL system must adopt a model of the student's
individual differences because learners vary widely in their learning styles and
strategies. They learn at different rates, have varying socioeconomic backgrounds, and
have diverse intellectual strengths.
Data mining on those differences is quite an important advantage of technology: it
has the potential to adapt itself and the material it delivers. In new insights, learner
variables are revealed by the works of cognitive psychologists, on the human learning
process in general and on the language learning process in particular: (1) the work of
social psychologists who have studied the relation of attitudes, motivation, personality,
and other emotional characteristics and predispositions to second language learning, (2)
the work of educational psychologists who have identified variables such as individual
cognitive styles, personal learning strategies, and even brain hemisphericity that also
seem to be related to successful language learning. Furthermore, it should be taken into
consideration that those learner differences might be affected by ambient factors: personal characteristics of the teacher; the instructional method employed; the task or lan-
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guage skill to be learned; the classroom environment in which the learning takes place;
and the proficiency level that needs to be acquired.
All of these student variables can be measured item by item, stored in database and
implemented in a systematic way in the student model. A new program of data collection and analysis of learner differences will improve the system tutor's ability to predict
successful language learning and to lead the student to recognize and adapt an individualized language learning model.
1) Aptitude
Aptitude for learning anything can be defined for operational purposes as the amount
of time it takes a student to learn the task in question. Thus, students are observed not
in if they can learn a task or not learn it, but rather in the length of time it takes them
to learn it or to reach a given degree of competency. Carroll's (1962) four cognitive
abilities of foreign language aptitude can be useful for measuring aptitude index: (1)
phonetic coding ability: the ability to segment and identify distinct sounds, to form associations between those sounds and symbols representing them, and to retain these associations, (2) grammatical sensitivity: the ability to recognize the grammatical function
of words or other linguistic structures in sentences, (3) rote learning ability: the ability
to apply memory to the foreign language situation, and (4) inductive language learning
ability: the ability to infer the rules that govern the use of language. They are particularly useful in predicting success in learning to speak and understand a foreign language (Carroll & Sapon, 2002).
2) Transfer and Motivation
The role of transfer and motivation in language learning must be known to the student model so that the model can manipulate their mutually supportive coefficient in
creating an optimal learning environment. Transfer, the application of prior knowledge to
new learning situations (McKeough, 1995), is converted into an index of a learning goal,
and thus the extent to which transfer occurs is a measure of learning success.
Motivation is measured as the impetus (low, medium, high) to create and sustain intentions and goal-seeking acts (Ames & Ames, 1989) since it determines the extent of
the learner's active involvement and attitude toward learning.
The student model can monitor a student's motivation level according to four factors
(Gardner & Lambert, 1972; Oxford & Shearin, 1994) that impact motivation in language
learning: attitudes (the desire to learn a language to integrate successfully into the tar-
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get language community), goals (the desire to learn a language for utilitarian purposes
such as employment or travel), beliefs about self (i.e., expectancies about one's attitudes
to succeed, self-efficacy, and anxiety), and involvement (i.e., extent to which the learner
actively and consciously participates in the language learning process).
3) Learning Style
Learning styles are about each individual's innate traits and preferences. The term
encompasses four aspects of the learner: cognitive style, i.e., preferred or habitual patterns of mental functioning; patterns of attitudes and interests that affect what the
learner will pay most attention to in a learning situation; a tendency to seek situations
compatible with one's own learning patterns; and a tendency to use certain learning
strategies and avoid others. Learning style can be measured according to Krashen's
three types of Monitor users; over-users, under-users and optimal users (Krashen,
1982). At least twenty dimensions of learning style have been identified (Parry, 1984)
but the principal characteristics of field dependent and field independent cognitive style
(Ellis, 1985 based on Hawkey, 1982), which have received the greatest attention, can be
itemized and measured by the student model: (1) Field dependence: personal orientation
(i.e., reliance on external fame of reference in processing information), holistic (i.e., perceives a field as a whole; parts are fused with background), dependent (i.e., the
self-view is derived from others), Socially sensitive (i.e., greater skill in interpersonal/social relationships). (2) Field independence: Impersonal orientation (i.e., reliance
on internal frame of reference in processing information), Analytic (i.e., perceives a field
in terms of its component parts; parts are distinguished from background), Independent
(i.e., sense of separate identity), Not so socially aware (i.e., less skilled in interpersonal/social relationships).
4) Learner Anxiety
Learner anxiety (Horwitz, 1986) and other negative feelings can be stumbling blocks
to learners becoming cognizant of learning and transfer opportunities. By controling the
learner anxiety level and informing the learner with the degree of social and psychological distance (Schumann, 1978), the tutor helps the student to have a low affective
filter and ease off the language or culture shock and facilitate learning success.
5) Language learning strategies
Positive and individualized dispositions towards language learning, which are vital to
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learner success, include features like high motivation, low affective filter, risk-taking attitude, mindfulness, and a sense of responsibility for learning. It is necessary for the
system tutor to help students maintain these dispositions by encouraging them to use
language learning strategies since those metacognitive and problem-solving skills facilitate transfer of learning. Also, to adapt the system to student preferences, the tutor
should understand and keep record of what language learning strategies students already
appear to be using by observing their behavior in interactions. However, assessing and
training language learning strategies is a very complex matter in designing a computer
tutor system.
These strategies are classified by many researchers (Wenden and Rubin 1987;
O'Malley et al., 1985; Oxford, 1990; Stern, 1992; Ellis, 1994). However, most of these attempts to classify them reveal more or less the same five categorizations (Oxford, 1990):
(1) acquisition, (2) storage, (3) retention, (4) recall, and (5) use of new information.
Research and investigations with language learners (Oxford & Crookall, 1989) frequently
show that the most successful learners tend to use learning strategies that are appropriate to the material, to the task, and to their own goals, needs, and stage of learning.
More proficient learners appear to use a wider range of strategies in a greater number
of situations than do less proficient learners. Research indicates that language learners
at all levels use strategies (Chamot & Kupper, 1989), but that a great number of learners are not fully aware of the strategies they use or the strategies that might be most
beneficial to use.
All the data collected and calculated according to learner strategies and other learner
features which are presumed to be essential for good L2 learners such items as listed
by Rubin (1975) and other researchers will help the system decide which direction is the
optimal path for the student to be a good language learner.
IV. CONCLUSION
The purpose of applying a student model within CALL systems is to give the learner-centered CALL environment some intelligent features such as error-specific feedback
and adaptive tutoring benefits for the student. Popular rationales of CALL, i.e., constructivism, authentic learning, learner-centeredness can possibly be implemented in the
framework of student model technology which provides one-on-one care about students
in the language learning for their own purposes and by their own paces. In this individualizing care, they are able to effectively develop their communicative competence
IK-Sung Ohm
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in their ZPD. For an intelligent instructional system to be tailored to the student, a large
quantity of learner variables must be diagnosed, computed and updated for the system's
understanding of the student.
Putting some intelligence onto CALL is considered extremely complex, laborious and
costly (Harrington, 1996) but it can be solved in a variety of ways even without incorporating an NLP parser or AI technology which is very difficult and expensive. It is
about time for the CALL system to migrate from the role of unenterprisingly assisting
the students in language learning to that of actively taking care of the student. With the
provision of intelligent learning environments, language learners' learning styles, strategies and individual differences can be adaptively taken care of in analytic and professional ways by the student modeling technology. From a language teaching perspective, this is quite an important advantage of the technology: it has the potential to
adapt itself and the material it delivers. Now student performance of the problem solving
activities is only one criterion available to individualize the language learning process.
Long-term goals include expanding the student model and eventually, system decisions
can be made based on a dynamic student model rather than static computational factors.
Though this project seems too big to be implemented, CALL can be possibly made intelligent and adaptive by the individualization of the learning process through the work
of analyzing and systematizing key factors such as different learning styles and strategies and other individual learner variables. These itemized numerical control can be
measured and built into complex data structure by monitoring the student input behavior
by the computer tutor.
The focus of this paper has been to discuss how student modeling technologies can
be integrated with CALL in measuring individual learner differences to achieve the
framework of an intelligent and adaptive interface. This interface designing approach
should be a promising one towards the upcoming description of a generic framework for
modeling an Adaptive CALL (ACALL) system.
Acronyms: AI (Artificial Intelligence), CAI (Computer-Aided Instruction), CALL (Computer-Assisted Language
Learning), CBI (Computer-Based Instruction), EFL (English as a Foreign Language), ICALL (Intelligent
Computer-Assisted Language Learning), ILTS (Intelligent Language Tutoring Systems), ITS (Intelligent Tutoring
Systems), ZPD (zone of proximal development), ACALL (Adaptive Computer-Assisted Language Learning)
130
How To Make CALL Systems More Adaptive
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Key words: CALL, language tutoring system, Intelligent CALL
Author and email: Ik-Sung Ohm, [email protected]
Revised version received in May, 2003