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A Simple Web-based Adaptive Educational System (SWAES) Ian G. Kennedy, Sanaz Fallahkhair*, Ricardo Fraser**, Amirah Ismail****, Veronica Rossano*****, and Anna Trifonova******
University of the Witwatersrand, Johannesburg, South Africa 2050 * University of Brighton, Lewes Road, BN2 4GJ, United Kingdom; ** The University of the West Indies, St. Augustine, Trinidad and Tobago; ***T he University of Warwick, Coventry, CV4 7AL, United Kingdom; **** University of Bari, Via Orabona, 4 - 70126 Bari – Italy; ***** University of Trento, via Sommarive n. 14, 38050, Povo, TN, Italy.
[email protected],
[email protected],
[email protected],
[email protected],
[email protected],
[email protected]
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A Simple Web-based Adaptive Educational System (SWAES) Abstract An adaptive educational system is complex to model. Such a system should allow various didactic, educational and student styles and is traditionally divided into four modules for analysis and building: Domain Knowledge Base, Tutoring, the Student Interface, and the Student. In particular, the system should adapt to differences between students and the changes in a particular student. One of the main problems when trying to develop an adaptive educational system is that definitions used for instructional design by psychologists and pedagogy experts are vague, while developers work with concrete terms like variables, stochastic values and objects. To make the concept of an adaptive educational system more concrete, this work models the simplest possible adaptive educational system that could exist. The contribution of the paper is that it gives a good description of a basic adaptive educational system and reasons about concrete solutions. The novelty in the paper is that adaptivity occurs in each of the four modules: The system selects an appropriate domain knowledge base model, an appropriate tutoring model, an appropriate (student) Interface model, and an appropriate Student model. Use of these modules will facilitate the design of adaptive educational systems that are educationally effective and will benefit future research and development on adaptive educational systems. Keywords: Adaptive educational system; Domain knowledge base; Instructional design; Student interface; Tutoring module
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Introduction Education is an investment for the future of society. To develop society educators need to achieve the best education for every individual through providing every appropriate means for the particular individual. Today, modern networks allow us to run educational programs on computers that are located on the other side of the world, allowing distance education. Computer media allow us to encapsulate human knowledge in a durable form. The low and decreasing costs of data transmission make it a cost-effective way to distribute knowledge. Suddenly there is a need for educators to rethink their approach to providing modern education (Kennedy, 1996). To provide the best education for each individual, educators must perforce adapt their delivery of education to the individual. One adaptation is to free the individual from constraints of place and time, for example through use of Web-based educational systems. Educators can now rejoice that their students can progress at their own rates. Individual pacing is the real advantage of Web-based self-study and educational systems. The grand idea is to design a educational environment that will enable the individual students to acquire knowledge just in time (Sampson, 2002), anytime, anywhere, at their pace, and tailored to their personal needs. An adaptive Web-based educational system is different for different students and groups of students by taking into account information accumulated about the individual student or group of students (Brusilovsky & Peylo 2003). The current problem in education is that there is a proliferation of instructional material on the Web but a distinct lack of any adaptive instruction on the Web (Bassiliades 2002).
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A challenging research goal is the development of advanced Web-based educational programs that can offer adaptivity and intelligence (Brusilovsky, 1999). A solution requires thought about the structure of such a system. The system needs to have its components well analysed. For example, it makes good economic sense to separate the educational content from the means of delivery. This way, for example, as Web browsers develop, the content does not become obsolete. The need is for a good model with the variables which characterise the individual student: goals, preferences, knowledge and so on. The adaptive system will use these characteristics to continually adapt to the needs of the individual student. Traditional educational research has focused on studying the instruction of groups of students rather than looking at individual education. Little is known about the individual characteristics which are vital to help us develop the student module and the tutoring module for adaptive educational systems. A useful review of the field appears in Ally (2005). There is now an urgent need for a strong model for the designing Web-based adaptive education. The main aim of this group’s work was to consider how to design and develop a simple adaptive web-based educational system to assist, for example, adult learners in learning a sample subject. Early discussions in the group centered around what should the adaptive system adapt to (i.e., measure and respond to), and how could it adapt (i.e., change the standard delivery and deliver the subject in a more appropriate way)? In addition, what results could be expected? The current paper describes a basic Adaptive Educational System and reasons about possible solutions when developing a concrete Web-based Adaptive system. The aim of this paper is to explain in clear terms a simple system. The paper is limited to assuming a browser-like interface to the student, as it is ubiquitous, familiar, available as an open source program, and supports hypertext, animation,
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instant messaging and video plug-ins. Ideally the student interface should allow on-line tests, group work, communications etc. The authors of the paper have deliberately chosen the lowest common denominator. They know there are so many modes of teaching (language labs, field visits, laboratory work etc.). However they have had to make a start. Sadly, there is not yet agreement between scholars in the field on the functions that each block (module) must perform, and even less agreement on where and how to draw the links between the blocks. For example, Ally (2005) says incorrectly that the intelligence built into the student interface must be able to learn from experience - analyze what the learner did in the past and adapt the interface appropriately. We disagree. Rather, we believe that the student interface module must send directed questions to the student and collect the student’s response via an appropriately sized form. This data is passed to the Student module. Here it is used to update the system’s knowledge of what this student did in the past and signal to the Tutoring module to send different material to the student in future. It is misleading to lump together into one module all the functions that belong in four separate modules. Other authors such as Martens (2004) lump the tutoring model and the tutoring module into one component, the so-called tutoring process model. Scholars have to be very careful with use of the word ‘feedback’. Psychologists use this word as a synonym for ‘reply’ or ‘response’ or ‘answer’. We use ‘feedback’ to mean the process in which part of the output of a control system is returned as input. Without this kind of feedback within a system, adaptation is not possible. Difficult questions arise with regard to pedagogical feedback to the student: When does the system know it should provide feedback to the student? On what basis does the system select
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examples, analogies, multiple views or different levels of explanations? What triggers this feedback? Should not feedback be a function of the student characteristics? This paper does not answer these difficult but important questions.
How Does an Adaptive Educational System Work? Different delivery is appropriate for different classes of students. A lecturer may skip a topic or a whole chapter if it is inappropriate for the group. In the same way, different delivery is appropriate for different individuals. So an advanced educational system should be able skip a topic or a whole chapter if it is inappropriate for an individual student. Ally (2005) sensibly comes to grips with the basics of designing for the differing needs and styles of students. When students come to the educational process, they come with many individual differences, such as unique learning styles, different motivational levels, different backgrounds, different levels of expertise, and different expectations. The question is how to develop an intelligent (educational) system that identifies these individual differences and adapts the instruction to meet learners’ individual needs. A welldesigned intelligent (educational) system will be able to cater to learners’ individual needs in a distributed environment (p. 168). Kennedy (1996) reminds us of the importance of one-to-one education but asks this critical question: Why must expensive expertise be present during instruction? If strategies like tutorial instruction and reinforcement are important, then an Adaptive Educational System might emulate a tutor providing reinforcement and pedagogical feedback. An adaptive system (sometimes called an intelligent agent) can adapt thus: 1. Get an initial model of a particular student - student model. Some misunderstanding, misconception or miscalibration may arise. Page 6 of 25
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2. Give some initial educational material from the knowledge base, revise the initial model indicating what content to skip, what to present, and in what order - current model. 3. Deliver educational material and revise the current model. 4. Repeat until all content has been mastered by the student, and the system has a good model of the student for future use - student’s profile. At runtime, the program can: 1. Dynamically include or exclude topics in the knowledge base. 2. Dynamically rearrange the order of presentation of topics in the knowledge base. 3. Loop on topics in the knowledge base that require drill or practice. How are these things determined dynamically? For example, the program may present topics in a prescribed linear order or in a random order. This second strategy might be useful to present a randomised drill to a school scholar but not if the domain demands that the content be presented in historical order or as steps in a specific procedure. Under what conditions would aneducator omit a topic or change the order of presentation used in previous years? How would an educator or an adaptive educational system detect that a different order might be required and that a changed order was better? What evidence or set of experiments is required to establish that a specific change was needed and beneficial when implemented?
The Four Components of an Adaptive Educational System Software and courseware engineers genreally agree that the only way to develop an educational system is to ensure separation of the system into modules such that every module has clear functionalities and responsibilities and the interaction between the modules is well Page 7 of 25
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defined. In an effective design, a change in one module does not usually require changing others. For example, if it is subsequently decided that a Web-based interface is no longer desirable, as long as the developer includes the same functionality and input/output in the Student Interface module, then all other modules can in principle remain untouched. The engineering idea is to see each module as a stand-alone unit with a specific functionality and input/output sockets. This paper analyses each module’s functionality and interactions with other modules. The modules give the system developer an abstract but functional view of an educational system. The following modules are adopted for the current paper (Figure 1): 1. The domain Knowledge base module – a repository for the content to be mastered; 2. The Tutoring module – the logic according to which the adaptation is done; 3. The Student Interface module – the human-computer interface; and, 4. The Student module – the current knowledge of the system about the current users. Figure 1 gives a view of a Simple Web-based Adaptive Educational System (SWAES), with the four modules in grey. In Figure 1, the student has been oriented to be on the right hand side. The flow of information or control from module to module has been shown by including single-headed arrows. These flows have also been labelled. Readers should keep the difference between a model and a module. In general, the module will be programmed to respond appropriately to many models that might be invoked. In Figure 1 the authors have also added models: the m didactic, p tutoring and n Student models, from which the program can select at run-time in order to adapt to the needs of the individual student.
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Figure 1: A simple Web-based adaptive educational system. While some authors such as Kazi (2004) would show the Student Interface module interacting with the domain Knowledge base, the present authors believe that this linkage is unnecessary, and contrary to the clean design of an Educational System. Other authors (Wang et al, 2004) complicate their designs in other ways by mutually interconnecting the tutoring and student models. The large backward arrow is the Give Directions path, essential for allowing the student to guide the Adaptive Educational System. For the Educational System to be classed as being Adaptive, changes must be made dynamically, while a student is interacting with the system. Real-life examples of this occur when educators deviate from their prepared path because of
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response from the class. An adaptive system must adapt to differences between individual students as well as to individual students. The domain knowledge base module The domain knowledge base is the corpus of knowledge that the Adaptive Educational System is required to transmit to the students. Just as lecturers at run-time (lecture time) cannot generate facts which are not in their notes or minds, so too an adaptive educational system cannot adapt to educate the student about anything that is not in the knowledge base. This limited characteristic of this knowledge base is not to be taken as a shortcoming of the knowledge base, but something which can be improved with programming. The domain knowledge module also contains the data for automatic presentation of tests to assess the student’s progress. A lot of work needs to be done beforehand by the educator before the course material and assessments can be laid down in a knowledge base and served up in the most logical and appetising way to the student. All that the educators have under their control at run time is the way they serve what is on the menu. The particular model or structure the authors chose for their knowledge base – the knowledge base model - was a tree. A tree is only one representation of a knowledge base. Another equally valid structure would be a relational database or a semantic network. The tutoring module The Tutoring module is the heart of the Adaptive Educational System. It encapsulates the way that the Adaptive Educational System conveys the domain knowledge base to the student. It accepts signals from the Student module which control how it adapts for the different needs of different students, and it adapts to the changing needs of a single student.
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The tutoring model is based on experience of what works with the target audience. The authors assume that the educator has some practical experience in what works, and maybe even knows about some good research results in the field. Thus the decision of what and how to adapt is derived from didactics. The student interface module A Human-Computer Interface is obviously required to deliver the domain Knowledge base to the student. The Tutoring module follows the didactics in the specified tutoring model, and sends the information to the Student Interface module. The Student Interface module is the Human-Computer Interface, which has been extensively studied in many environments and over many years. The Student Interface Module is not a program to merely allow the student to view material, as Web browsers do. It must be able to forward codified data in an agreed-upon protocol to the Student Module. Not shown in Figure 1 are the interface models available for selection by the program to adapt to the interface needs of the individual student and to the student’s terminal device. An effective Student Interface module is critical for attracting and maintaining the attention of the student. Kennedy (2004) cites these as consistently used: 1. relevant questions in topic titles to engage the student’s long-term attention. 2. gratuitous graphics to start every topic and thereby engage the student’s short-term attention. However, should the system always be subject to students’ choices? Should an adaptive educatinal system let students play computer games rather than complete assignments? We think that this is being extreme, unless it could be shown to facilitate the learning of a subject.
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The Student Interface module responds to student events and creates structured information which is sent to the Student module, the system’s repository for information about the student. The Student Interface module sends information particularly about the changes in the student’s knowledge state so that the Student module can update the student’s profile. The Student Interface module is in a unique state for each of the students currently logged in. It contains variables such as active_window_name, cursor_position etc. The Student Interface Module can call upon a library of Student Interface models. The Student Interface module collects answers to questions, codifies them and passes them to the Student module where they are simply stored. In response to the question: “Are you a soldier, sailor, airman or airlady?” it passes the reply as a string to the Student module, where this student’s record is updated. The Student Interface to Student module commmunication protocol must allow for four types of unidirectional responses, typically collected through forms: 1. True/ alse responses 2. Multiple choice 3. Numeric entries 4. Fill in the blank responses For the system to be called adaptive, students must be in the loop. There must be some measure of their individual performance levels. The difference between the measured performance and some desired performance must be fed back to the module delivering the content so that the material or the experience is customised for the current student and results in efficient and effective learning.
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The student module The Student module is the module that is responsible for keeping all the knowledge that the system has about every particular student. From this can be extracted aggregated knowledge about all the students. The system uses the module to gather initial data from any new student and subsequently identify that particular student. The Student module signals to the Tutoring module to enable it to adapt to the particular student. There are many parameters about the student that the Student module needs to be aware of – the student’s personal data, current knowledge, learning strategies, goals and desired ways to reach them. All these functions of discovery, storage, and updating are provided by the Student module. Speaking about analyzing the student and using data from or about the student raises issues of student privacy. In traditional teaching, privacy was assured by the professional standards of ethics of lecturers and teachers. In Adaptive Educational Systems, technical staff also have access to saved data, and they need to be reminded of the need for confidentiality of student data. An adaptive educational system should be capable of doing student analysis and modeling. Such an up-to-date analysis enables the system to adapt to the current student’s needs and track them if the needs change (Leng, 2002). The results of any student analysis are stored in the Student module. The adaptive system needs to store and subsequently pull together all the student’s attributes that can be acquired implicitly (behavior, motivation, etc.) and explicitly (personal details). There are two types of attributes that the Student module can implicitly and explicitly acquire from the student. Explicit personal data is static and is acquired from the student’s
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response. However, the implicit information is usually acquired through observing the student’s behavior, interaction patterns, and time spent. All of the clues were collected at the Student Interface module and passed on to the Student module. From this data, the Student module stores presumptions about learner’s attitude, motivation and interest. These attributes are dynamic and they correspond to the student’s interactions and behavior while using the system. In the Student module, each student is initially modelled as possessing some prior knowledge, which is a subset of the knowledge base (Figure 2).
Student Knowledge Over the Target Knowledge Base
The Knowledge Base
Figure 2: The current student knowledge is an expanding subset of the knowledge base. Every educational system contains a knowledge base. The students should be learning (e.g., increasing their knowledge, skills and experience) with the help of this target knowledge base. Hopefully, the students are also learning from other domains. According to Brusilovsky (1996), the idea of the overlay is to represent an individual student’s knowledge of the subject as an “overlay” of the domain. For each concept in the domain, an individual overlay field stores some value which is the best estimation of the student’s level of knowledge of this concept. This may be just a binary value (known ─ not known), a qualitative measure (good ─ average ─ poor), or a quantitative measure, such as a Page 14 of 25
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probability that the user knows the concept. An overlay of the student’s knowledge can be represented as a set of concept:value pairs - one pair for each domain concept. Overlays can independently measure the student’s knowledge of different topics. Overlays were originally developed in the area of intelligent tutoring systems and student modeling. In many Intelligent Tutoring Systems, the Student model is just an overlay of the student’s knowledge. As a result, in the area of adaptive systems, an overlay of the student’s knowledge is sometimes merely called the Student model. The Student module finds out what subset of the knowledge base that the student is familiar with and with what degree of confidence. Consequently the Tutoring module can present to the student another subset of the knowledge base in an appropriate order for the student or with an appropriate look, depending on the rules in the Tutoring module. The aim is always to increase the gray area of Figure 2, by putting the material into the ‘gray matter’ of the student’s brain. Thus the student’s knowledge can be modeled as a list (or set, database, or tree) of learning resources/concepts (depending on how the knowledge is modeled) and the corresponding competence of the student. For example: Student_X {LR1 : 0.5 ; LR2 : 1 ; LR3 : 0 ; LR4 : 0,7; …}, where 0 means “Not at all familiar with this” and all the values might have also some categorical meaning such as “unacceptable”, ”acceptable” or ”distinction”. When the system first encounters a new student, the initialization of the student record in the Student Module is effected by presenting some assessment questionnaire or quiz, which has previously been devised. Alternatively, it can be presumed that the student does not yet know any thing in the knowledge base.
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The record of the student’s knowledge in the Student module is updated with every assessment of the student by the Student module. This event occurs with every form submitted by the student through the Student Interface module. The Student module contains fields that record which leaves and branches the student has mastered or traversed (and hopefully internalised). For each student, the Student module keeps track of those leaves and branches that the student claims were previously mastered, or were visited. In general, for each student, the Student module dynamically updates what it believes is now the current knowledge of the student. The Student module additionally has fields containing the profile of the student. There are other functions that the student module may provide, including: •
Tracking the preferences and characteristics of the student.
•
Modeling of general student behavior.
•
Modeling with personal details such as age, gender, education, race, religion, etc.
The Student module uses the best of the available n models for each of the individual students. The Student module tracks how the students actually go about their task of studying the material, and stores their progress, as well as details such as styles and preferences. The power of the adaptation process is strongly dependent on the availability of a number of models. Information from the Student module is passed on to the Tutoring module so that it can take all material and filter through the wanted material to show to the student. In addition the Student module might feed information to the Student Interface module so that the Student Interface module can choose the most appropriate concrete interface model to use for the students, based on their current student model. All models should be continually updated (refreshed) so the best choice is always being used.
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A concrete adaptive system and other possible development strategies The domain knowledge base module To make this work concrete, the primary author pretended that his co-authors were his students in the field of adaptive educational systems. He supplied to all his co-authors a sample corpus, the bookmark file of references related to SWAES, consisting of critically reviewed links into carefully selected readings in the World Wide Web. The corpus was already categorised into one topic (SWAES) and placed into a suggested order of reading. This knowledge base is most easily modelled as a tree. The folders constitute branches of the tree, and the content pages constitute the leaves of the tree. The authors realized that they needed to characterise the knowledge base as a model of educating. They chose to characterize it as a reading list. A reading list is a list of references that the educator believes the student must study and which can be tested in an examination. The student should go through all the material trying to make sense of it. It was appropriate to characterise the domain knowledge base as a tree of knowledge with leaves and branches that could be left out by an advanced or knowledgeable student, however one may wish to measure those terms. A structure which can usefully be called a knowledge value would contain a parallel tree of decimals, each of which indicates the student’s competency in the concept on a scale from 0 .0 to 1.0. Learning can then be defined as the difference between the current knowledge value and the prior knowledge value. In its simplest form the student’s competency can be coded merely as an integer, 0 or 1, indicating that the student has not yet studied the topic or has already mastered it. Because the authors adopted a Bookmark file as the knowledge base, they were forced to use its tree structure as their particular model for domain knowledge. There is also an impact of
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this adoption of this structure on the student interface: The student interface must then allow the student to browse, navigate and search through the tree. In a generic fashion, this (single) model of the student will drive what needs to be programmed in the Student module. The didactic module The didactic model encapsulates the particular educational approach to be taken, here to adult students. As the co-authors were intended to be the users of the knowledge base, the simplest example model for a knowledge base, would be that it was a reading list. Therefore, without loss of generality, the single model considered here is a reading-list. The educator gives the student anything that might be useful, excluding those leaves or branches of content that the student claims to have mastered. Another example of an existing didactic model is a “Learn and Reproduce this material at examination time” model. A further didactic model is the “Please write a critical review of this material” model. In these models, educators are stereotyping their way of educating. Other models can be incorporated as they are identified, analysed and subsequently programmed. Different models can then be tested for effectiveness with different students. The student interface module With regard to thje student interface module, in this instance the authors decided to make the interface adaptable in a way that would let students navigate effectively through the content. The student module The Cognitive Trait Model (Lin, 2003) is a possible way to model the student. Further notes about this model are provided in the discussion section.
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This Student model could be termed an “honesty model” where the adult students assess their need for material. It is based on self-recognition of one’s own competency. Being adults, the students would be expected to retrieve the educational material themselves, read those passages that were referred to, ignoring others. They would be expected to highlight important text and figures, making their own marginal notes as they went along. The length of such notes could be used as evidence of student activity towards their score. Each student would be expected to discover the “shape of the field” and “make connections” linking together the diverse and possibly contrary viewpoints of the disparate authors, and making an integrated and integral whole of the material. In respect to the previously discussed measurement of learner performance, the desired performance might be that the students open and study every topic page except those they have proven mastery of. In a more complicated situation, the system might measure some psychological variable that would be useful and then fruitfully use this measure to adapt the way the system delivers content. The simplest practical way for assessing the student and using the knowledge score as a base for customising the experience for every student is simply to ask before each topic “Are you sure that you know what the ... is?” and “Are you confident that you know enough about ...?”, and then respond by arranging that the student skip the topic or study the topic as the case might be. In practice, this process can be made more subtle by always phrasing the title as a question: “What is ...?” and “How does one ...?” and linking to a list of sub-questions (branches), which are then linked to the final text (leaves).
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Further work A hidden agenda is for the students to make mental connections between the presented concepts. The content pages are in reality never “atomic” and completely self-standing. There would be overlaps of knowledge, contradictions, and gaps resulting from the multiple authorship of the content. This area is an interesting area of current research. The Cognitive Trait Model or CTM (Lin T., 2003) would have us measure those characteristics of the student that were persistent (and useful for changing the educational style). When fed to the educational module, the Student Interface module would adapt to the profile of the student, and deliver a customized experience, that could otherwise only be achieved through good one to one tutoring. Are there any traits of the student that can actually be measured directly even if these traits are not currently defined in the psychology literature? What about a measurement of working memory capacity (WMC)? According to Joner (2004), Eagle’s definition of WMC is the ability to keep attention focused in spite of distraction or interference. Should educators perhaps invent some other more pragmatic measures that are either functionally equivalent or just as useful? Programmers want a specification for what variables to measure and what actions (interventions) to take. Simply put, educators must be able to describe to a programmer what they want the Adaptive Educational System to do. The designers of future advanced educational system need to be given very clear specifications for all the models of how educators wish to use the system to educate. Do educators want to change the student’s behavior (e.g., teach students not to plagiarize) or get the student to make a mini-model of the knowledge in the domain? What if educators wish to dynamically change their mode of delivery from one based on behaviourism to one based on constructivism? Surely the adaptive educational system should allow for this? The educator steeped in constructivism theory may
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well wish to see the students construct their own view of the world (as our mini-knowledge base for our authors has shown). The educator steeped in behaviorism theory will wish to change the behavior of the student and insist that this change be measurable. Are these views reconcilable, and can a Web-based adaptive educational system ever provide for all modes of educating, not just concentrating on the lowest common denominator of an electronic textbook? After all, adapting to the needs of the students alone is inadequate. The system must adapt to the needs of the educators as well. Attention can be given to the following measurable factors: •
Speed of execution: This might be the time that the student takes to read a topic, give an answer, or to do a task.
•
Navigational pattern: This can be holistic (short-term) or serial (long-term). The Adaptive Education System could keep track of the paths that the students follow during their navigation through the content.
•
Ability to process tasks simultaneously: This might be how many different chapters the student visits simultaneously, given that browsers may be opened in multiple or tabbed windows. For example, if the domain is to read this paper, the students might start with reading the literature review, and before they have finished that topic, they jump and start to navigate in the unrelated data analysis topic.
•
Ability to memorize and retain: if the adaptive educational system has an assessment test it could return the test result as a measure.
•
Attention span: This and other operationalised measures may be investigated. Appropriate designs can be coded using Unified Modeling Language as advocated by
Bassiliades et al. (2002), which describes the introduction of stereotypes to the pedagogical
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design of intelligent educational systems and appropriate modifications of the existing package diagrams of the Unified Modeling Language.
Managerial support for the educator The last viewpoint of this paper is that in reality the picture is bigger than the blocks in Figure 1. Educators need a system to manage all of the components, not just an intelligent educational system. An Adaptive Educational System (AES) must be composed of an Intelligent Tutoring Systems (ITS) plus an Educational Content Management System (ECMS). Educators must not only have a system that tutors in an intelligent fashion, but also one that enables the content to be easily managed. Thus, AES = ITS + ECMS. Nichani (ca. 2002) has defined an (Educational) Content Management Systems (ECMS) as being an (Educational) Management System (EMS) plus a Content Management System (CMS), or: ECMS = EMS + CMS. Finally, then: AES = ITS + EMS + CMS.
Conclusion and Recommendations To make the concept of an adaptive educational system more concrete, this work modeled the simplest possible adaptive educational system. The paper has given a description of a basic Adaptive Educational System. Adaptivity may occur in four places; the system selects an appropriate Didactic model, an appropriate Tutoring model, an appropriate (student) Interface model, and an appropriate Student model. Concerning the Tutoring module and concerning the knowledge bases, Educators need to allow for as many ways of learning as is possible, and they need to keep open minds on the issue. Education takes on many modes. There are groups, tutorials, labs, and many other ways of educating.
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In the future, work also needs to be done on the different models or structures for knowledge bases (assuming that the educators are thinking of conveying content to the student and not e.g., trying to convey understanding). Research needs to be done at the highest possible level of thought on all aspects of educational system design including the course material, its tags (metametadata) and a vocabulary and grammar for the tags. Research also needs to be done on using a standard Learning Object (Mohan & Brooks, 2003). The use of a Learning Object format should enable all the Learning Objects to be used with consistency and interoperability in the knowledge base. It is trusted that a Learning Object format will change the way that SWAES are designed. Finally, the SWAES model needs to be authenticated in practice and revised and extended if necessary.
Acknowledgements Thanks to Kinshuk for lecturing at the Summer School in Finland that drew the coauthors together, to J. M. Spector who suggested useful direction for the paper, and to Sabine Graf for her ideas on the Student model.
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