Malaysian Online Journal of Instructional Technology ISSN: 1823-1144 Vol. 2, No. 1, April 2005
Adaptation Models for Personalisation in Web-based Learning Environments Mohammad Issack Santally & Alain Senteni Virtual Centre for Innovative Learning Technologies University of Mauritius, Mauritius
[email protected] Abstract One of the main advantages of delivering web-based educational materials is that the same content is delivered to a number of students and can be accessed with no restrictions of time and place. However, there is a wide belief that using the web as only a new kind of delivery medium for educational materials does not add significant value to the teaching and learning process. It is postulated that one of the main problems with e-learning environments is their lack of personalisation. This paper presents a comprehensive review of the current work in the field and describes three potential adaptation models – namely a content-based adaptation model, an activity-theoretical adaptation model and a hybrid adaptation model. A framework is finally proposed for research in promoting personalisation in web-based learning environments. The framework is basically an integration of three adaptation models that are discussed in this paper. The paper exposes the limitations of so-called intelligent tutoring systems and emphasises on the increasing importance of the teacher’s role in the teaching and learning process. INTRODUCTION Nowadays, in this technology driven world, a new concept of distance education has emerged. Different interchangeable terms have been used to denote this concept: e-learning, online learning, web-based learning etc. The concept of web-based learning and the use of the Internet in teaching and learning have received increasing attention over the recent years. One of the main advantages of delivering web-based educational materials is that the same content is delivered to a number of students and can be accessed with no restrictions of time and place. However, there is a wide belief that using the web as only a new kind of delivery medium for educational materials does not add significant value to the teaching and learning process. The integration of technology in learning, needs to address the very important issue of enhancing the teaching and learning process, rather than just being seen as a new flexible delivery medium (Nichols, 2003). It is postulated that one of the main problems with elearning environments is their lack of personalisation (Cristea, 2004; Rumetshofer & Wöß, 2003; Ayersman & Minden, 1995). This paper presents a comprehensive review of the current work in the field, describes three potential adaptation models – namely a content-
based adaptation model, an activity-theoretical adaptation model and a hybrid adaptation model. Finally a framework for research in promoting personalisation in web-based learning environments is proposed. A REVIEW OF ADAPTATION CONCEPTS IN LEARNING ENVIRONMENTS Adaptation to Individual Differences in Learning Environments The concept of ‘adaptation’ is an important issue in research for learning systems. Systems that allow the user to change certain system parameters and adapt their behaviour accordingly are called adaptable. Systems that adapt to the users automatically based on the system’s assumptions about the user needs are called adaptive. The whole spectrum of the concept of adaptation in computer systems is shown below (Patel & Kinshuk, 1997).
Figure 1 Spectrum of the adaptation concept Adaptivity in hypermedia systems to personalise the user’s experience with the system is not a new concept and Brusilovsky (2001) describes three main types of adaptation that exists in web-based hypermedia systems namely content, navigation and layout. In adaptive hypermedia literature they are referred respectively as adaptive presentation and adaptive navigation support (Brusilovsky, 1996) An Overview of the Aptitude-by-Treatment Interaction (ATI) Research Aptitude Treatment Interaction (ATI) research developed as a way to find the best methods of instruction for the student population. Peck (1983) states that ATI is research correlating teaching methods with measures of student aptitudes finding that students may respond differently to a particular method depending on such variables as intelligence, learning style, or personality. Nanney (??) postulates that because of ATI research, we now sometimes attempt to adapt instruction to the learner. The obvious implications of ATI research is therefore the adaptation of instruction to learners' traits to maximize learning outcomes. Cronbach & Snow (1977) suggested the matching of instruction to traits at two levels: macro adaptations, which match treatments to fit different classes of students, and micro
adaptations of treatments on a lesson-by-lesson, student-by-student basis. Macro adaptation implies a multiple method approach to individualisation, the design of alternate treatments that engage different groups of students through different forms of information processing, whereas micro adaptations focus on treatments to adapt the tasks and forms of instruction to meet more specific learner needs and abilities (Jonassen & Grabowski, 1993). Davis (1983) describes three approaches to the use of ATI's to improve learning. The capitalisation approach says go with the student's strengths. The compensation approach says provide a crutch if weakness is predicted, and, the remediation approach, in which the weakness is worked on until it is overcome. Cronbach (1975) also emphasised the important relationship between cognitive aptitudes and treatment interactions. Nevertheless, he states "…Snow and I have been thwarted by the inconsistent findings coming from roughly similar inquiries. Successive studies employing the same treatment variable find different outcome-on-aptitude slopes…" He surmised that the inconsistency came from unidentified interactions. Finally, Cronbach & Snow (1977) concluded that "…an understanding of cognitive abilities considered alone would not be sufficient…" to explain learning, individual differences in learning, and aptitude treatment interactions. Psychologically Driven Adaptivity in Web-Based Learning Environments Rumetshofer & Wöß (2003), on the other hand postulate that in learning systems, adaptivity needs to cover more that what Brusilovsky (2001) proposes for web-based hypermedia systems and propose what they call adaptation to psychological factors. These psychological factors are cognitive style, learning strategy, learning modality and skills. The system is based on simple adaptation rules that match the students’ preferences and provide the student with a set of learning objects matching to these preferences. The research at this point in time, does not however, cater for the evolving and changing needs of the learner. Cristea (2004) highlights the importance of connecting adaptive educational hypermedia with cognitive/learning styles on a higher level of authoring. She briefly reviews some systems and models that address the same issue but with different perspectives. The first system is TANGOW that actually implements the Felder-Silverman dimensions of learning styles. The system includes low-level authoring patterns such as learning material combination in AND, OR, ANY and XOR relations. The second system is AHA! a low level tool with great flexibility based on IF-THEN rules adaptation model. The aim is to investigate how to incorporate high-level specifications deriving from learning styles especially those of fielddependent and field-independent styles into the low-level instances and structures as required by the AHA! system. Hong & Kinshuk (2004) develop a mechanism to fully model student’s learning styles and present the matching content, including contain, format, media type, etc., to individual student, based on the Felder-Silverman Learning Style Theory. They use a pre-course questionnaire to determine a student’s learning style or the student may choose the default style and he is then provided with material according to his/her style. The efficiency of student learning with the prototype presented is however not yet tested.
Magoulas et al., (2003) stress on the importance to accommodating individual differences when designing web-based instructions. The authors propose a design rational and guidelines to implement adaptation strategies in such systems. Their model is based on the Kolb learning cycle and the Honey & Mumford (1986) learning styles. Wolf (2002) proposes iWeaver, an interactive web-based adaptive learning environment. iWeaver uses the Dunn & Dunn learning style model and the Building Excellence Survey as an assessment tool to diagnose a student’s learning preferences. Instead of focusing on student’s learning preferences and to offer contents matching only a specific learning style of learners, iWeaver offers and encourages the trial of different media representations. It does not however adapt to the changing preferences of the learner. Unfortunately, many studies have been unable to find unambiguous support for the construct ‘learning styles’ and that this construct has been under much criticism by researchers. On the other hand, research demonstrates that both low and average achievers earn higher scores on standardised achievement tests and aptitude tests when taught through their learning styles preferences (Dunn et al., 1995). At the same time, we need to take into account the fact that no single learning preference is better than any other. Students become more competent learners if they can have preferences for more than one single learning style. This makes them more versatile learners. This reflection can be sustained by the fact that gifted learners prefer kinesthetic instruction but they also have the ability to learn auditorially and visually (Dunn, 1989). Furthermore, underachievers tend to have poor auditory memory. They learn better through graphics and animations rather than text (Dunn, 1998). Low achievers are also said to encounter difficulty to do well in school because of their inability to remember facts through lecture, discussion, or reading where teachers mostly talk and students mostly listen (Dunn, 1998). Adaptation Problems in Web-Based Learning Environments It is obvious from existing literature that there are a number of attributes that contribute to making individuals different from each other and that there is considerable argument but little evidence supporting the need to adapt instruction to each individual involved in a learning activity, hence giving rise to what is called the personalisation issue. Adaptation is currently a widely studied approach to the personalisation problem. Furthermore, the aptitude by treatment interaction research framework provides evidence of the adaptation approach improving the learning process. Adaptation in web-based learning environments usually takes two general forms: adaptability and adaptivity. These two are however at the extremes of adaptation. The first one gives the user total control over the environment. For instance, a student can freely choose the color, font and other related customisations of the learning environments and can freely navigate through the environment on his own. This is not the best choice since this is currently the standard practice in most web-based learning environments delivering e-learning courses. The other extreme is fully adaptive learning environments where the system decides everything that it deems best for the student based on a stored student model. The danger of such systems is that assumptions of the user preferences may not always be correct. Pre-data collected by the system about the user may contain data, which the user has perceived to be true but which may not actually be the case or such data
might be valid for only a certain time or under certain conditions that the system is not able to decipher. Such systems sometimes referred to as intelligent tutoring systems are also limited in knowledge and restricted to the teaching of well-structured domains. For instance, declarative and procedural knowledge can be dealt with effectively using these systems. However, when it comes to unpredicted situations, ill-structured domains and the need for intuitive adaptation, they are not the most efficient (Streibel, 1991). New trends in the field of adaptivity is currently emerging with the idea of adapting with individual differences such as cognitive and learning styles of the students to provide more robust student models on which the system can make more accurate decisions of “what to” and “how to” teach. The “how to” is a very important issue in teaching and learning since it deals mainly with pedagogical approaches and scenarios that would best help in knowledge transfer and acquisition by the student. A major issue here is that very often, validity of learning styles instruments have been questioned by researchers. Furthermore, there exist a number of such instruments that categorise learners differently. Simple [If-Then-Else] rules, as incorporated in some adaptive systems also do not suffice for adaptation based on learning and cognitive styles. Such systems do not necessarily evolve with time since they do not have mechanisms for effectively updating the cognitive and learning styles sections of the student model effectively. Finally, the concept of adaptation is also dependent on the way we define learning. If learning is viewed as a classical activity where the student is presented with an instructionally sequenced learning material, learns it “by heart” and reproduces the material in the written exam, then limiting adaptation control to the machine seems a straightforward simple solution. Such classic e-learning is in fact a replica model of traditional behaviourist classroom teaching where there is lack of innovative pedagogies and methodologies for sustainable educational development. The web has been postulated as a very good constructivist platform for improving the teaching and learning practices (Nichols, 2003). Therefore, web-based learning environments can be used to support a wider range of educational activities such as collective work, project-based learning, contextual learning and collaborative work. Efficient automated adaptation in these situations can prove to be a very difficult task at hand. The best agent to provide adaptation in these situations will be the teacher himself. A Content-Based Adaptation Model Learning objects describe any chunk of decontextualized learning information, digital or nondigital, such as an image, text, video, educational game or sound files. The aim of those entities is to provide a tremendous set of learning knowledge that once developed can be exchanged among organisations, and be used to build individual lessons and courses (McGreal & Roberts, 2001). The key factor for this flexibility is not performed by the physical learning object itself but by its standardised description or more precise in metadata specification (Rumetshofer & Wöß, 2003). Learning objects are often used as components to assemble larger learning modules or complete courses, depending on different educational needs.
The student model The student model is an important part of the system, as it will contain the necessary individual adaptation attributes of the learner. From a recent survey at the University of Mauritius concerning students’ learning styles and cognitive styles (Santally & Senteni, 2003), it was found that students can have preferences for one particular style, preference for more than one style and different levels of preferences for the different styles. The student model will therefore consist of four main components: (1) cognitive style, (2) cognitive controls, (3) learning style and (4) performance. It is clear that students would still learn if they were not given materials according to their preferences if the range of the scores is taken into account. Therefore the goal of adaptation is this context is to present the student with the most suitable option in any given learning situation.
The content model The learning objects approach to designing e-learning courseware is not new. This is however, a field still under research and the under-utilisation of existing learning object repositories is a major concern for many educators and researchers involved in this area (Santally et al., 2004). The concept of extending the learning object metadata to cater for psychological factors has been proposed by Rumetshofer & Wöß (2003). A section, for instance, will therefore be represented as a sequencing (in some cases, there may be loops depending on the tutorial strategy) of learning objects. Each concept that will be illustrated in the section will consist of a series of learning objects with varying belief values for each component that has been added to the metadata description (Figure 2).
Figure 2 Learning objects in multiple representations for sequencing Referring to Figure 2, Concept 1 can be taught using LO1, LO2 or LO3. However, the selection of a learning object to teach a particular concept will depend on the student model and his current experience in the course. Sometimes a learning object can be presented more than once depending on the level of understanding of the student or on the tutoring requirements for this section. Different students will therefore have different pathways to reach their learning goals and this brings the required flexibility and personalisation of the learning experience.
A possible sequence for student x would be: [Concept 1, LO1] [Concept 2, LO3] [Concept 3, LO1] [Concept n, LO2] Activity-Theoretical Adaptation Model Psychologists and educators have long been interested in understanding how people learn, for the concept of learning is central to many different human endeavours (Shuell, 1986). Traditional conceptions of learning rely heavily on the notion of a durable change in behaviour while from the cognitive perspective, the focus is principally laid on the acquisition of knowledge and knowledge structures rather than on behaviour. Such approaches also postulate that learning is an active, constructive, and goal-oriented process that is dependent upon the mental activities of the learner. Most cognitive conceptions of learning argue the need for “meaningful” learning to take place. Constructivists (and socioconstructivists), however, claim that meaningful learning occurs when the learner is placed in an authentic situation where he needs use his previous knowledge, percepts from the environments and cognitive processes to solve the problem at hand. Based on his experience, the learner constructs his own meaning through reflective activities. There is more and more claims that the real debate should not be centered around particular approaches to learning but on how to achieve the real goal of education that is, prepare students to apply the learned knowledge and skills in real world situations. The notion of “blended approach” (mixture of behaviorism, cognitivism, and constructivism) is being also widely emphasised to support such claims (Schneider, 2003; Deubel, 2003). Such a blended learning environment can be seen from an activity theory perspective (Engestrom, 1987) as consisting of a subject, a goal, a supporting community, rules governing the operation of the learning environment and a division of labour supporting the collective nature of the learning activities. See Figure 3. The technology-supported learning environment can therefore be seen as a common instrumentality for a community of learners (with the support of the teachers and resource persons) inquiring in and studying collectively a phenomenon or a social practice. Personalisation of this instrumentality would serve to develop the division of labour between learners who are inquiring in and learning about the same object of interest. The instrument(s) in this case is the learning content, support materials for the activities, software tools and online forums.
Instruments
Figure 3 Structure of human activity (Engestrom, 1987) Taurisson & Tchounikine (2004) propose the use of supporting agents in such an environment to handle tasks (delegation of tasks to the agents) that are not interesting or central the learning activity such as organisation of a vote with the community of learners. This is a plausible approach when the tasks do not have pedagogical implications on the learning activity. However, in designing pedagogical scenarios to implement collective activities, negotiation between the teacher and the student community is important to develop the division of labour. A Hybrid-Adaptation Model It is stressed in the paper that e-learning environments need to add value to the teaching and learning process and constructivists who argue the importance of learner-centred approaches never take the risk of saying that there is no need for the teacher. Instead the teacher role is seen to change to a facilitator. A solution to such problems and that can be applied to realworld contexts are to build hybrid adaptive learning environments where the human tutor is in constant interaction with the students. The system may have an adaptive engine supported by “enabling-agents” who help the teacher and student in better decision-making. Inputs from the teacher will help update student models that can assist the agent in proposing better support to the actors in such environments. This idea is correlated to what Brusilovsky (2003) describes as meta-adaptation in adaptive environments. The need of human teachers in adaptive environments is also briefly argued by Kinshuk et al., (2001) as being the one who sets the context, selecting and scheduling other educational technologies, managing the curriculum and overseeing the learning progression. Brusilovsky & Nijhawan (2002) proposed the KnowledgeTree framework for adaptive e-learning based on distributed re-usable learning activities. The KnowledgeTree framework is implemented in the form of a learning portal where the teacher works together with the adaptive system to select material to be presented to the learner. This is a step towards a hybridisation of such learning environments. However, the role of the teacher is limited to selecting and sequencing materials to be presented and the system’s role is to propose additional instructional material to the student.
Such a hybrid model therefore should take into account the different actors in the learning environments, the tools each actor will have access to; for instance a learner needs to access different tools to help him use his preferred learning strategies, the system has access to simple analysis tools to guide the student and to advise the teacher, the teacher needs to have access to pedagogical tools for tutoring; instructional models based on learning theories to devise appropriate instruction and learning activities and student models that will guide the system and teacher in adapting instruction to the students. The model will in fact be a combination of the content-based and activity-theoretical adaptation models that were earlier discussed. AN ADAPTATION FRAMEWORK FOR PERSONALIZATION IN WEB-BASED LEARNING The integration of these three models provides for a framework (Figure 4) that will support authentic learning in web-based environments while ensuring that students’ individual differences are catered for in the process. The next phase of the current research would be the implementation of this framework in a web-based learning environment.
Student Model
updates/ updates consults
updates/ gives advice/help updates decisions consults Teacher
interacts C with/guides
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System Agent
Monitors Activity/ gives advice uses Student
carries out Learning Activities
authors
consults
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Learning Strategies
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uses Learning Preferences
consist C of Learning Material
Figure 4 Framework for adaptation in web-based learning environments
A first phase implementation and testing is already in process where the content-based adaptation model part is being put in phase. An algorithm for adaptation has been devised to match the learning object with the student profile while learning material is being presented to the student. The teacher’s role in this phase will be to set the sequence for the learning objects to be presented to the students while the system will apply the algorithm to select the most appropriate object. A pilot test will be carried out with a group of students to study their perception of the system. In the next phase of development, the system agent will be implemented as well as the adaptation mechanism to be put in place. The teacher will be able to use information gathered by the agent on the student’s interaction with the system to decide how to update the student model and about the adaptation strategies to be used. The final implementation of the framework will be tested in real-world contexts in academic courses that use a more constructivist approach to teaching and learning where learning activities will be set and that students will be able to work on collective phenomena in a personalised environment. CONCLUSION This paper describes three potential adaptation models – namely a content-based adaptation model, an activity-theoretical adaptation model and a hybrid adaptation model. A framework is finally proposed for research in promoting personalisation in web-based learning environments. The idea is to depart from classic intelligent tutoring where the system (intelligent tutor) is limited in terms of knowledge, tutoring strategies & pedagogies, flexibility, lack of intuition and at the same time imposes restrictions on student actions and learning. REFERENCES Brusilovsky, P. & Nijhawan, H. (2002) A framework for adaptive e-learning based on distributed re-usable learning activities. In M. Driscoll & T. C. Reeves (Eds.). Proceedings of the World Conference on E-Learning ( E-Learn 2002). USA: American Association of Computer Education, pp. 154-161. Brusilovsky, P. (2003) Adaptive navigation support in educational hypermedia: The role of student knowledge level and the case for meta-adaptation. British Journal of Educational Technology, 34(4), 487-497. Brusilovsky, P. (2001). Adaptive Hypermedia. User Modeling and User-Adapted Interaction. 11, 87-110. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2), 87-129. Cristea, A. (2004). Adaptive and adaptable educational hypermedia: Where are we now and where are we going? Paper presented at the Web-based Education Conference, Feb 16-18, Innsbruck, Austria.
Cronbach, L. & Snow, R. (1977). Aptitudes and Instructional Methods: A Handbook for Research on Interactions. New York: Irvington. Cronbach, L. (1975). Beyond the two disciplines of scientific psychology. American Psychologist. Vol and Series No, 116-127 Davis, G.A. (1983). Educational Psychology. New York: Allyn & Bacon. Deubel, P. (2003). An investigation of behaviorist and cognitive approaches to instructional multimedia design. Journal of Educational Multimedia and Hypermedia, 12(1), 63-90 Dunn, R., Griggs, S.A., Olson, J., Gorman, B. & Beasley, M. (1995). A meta analytic validation of the Dunn and Dunn Learning Styles Model, 1995.Details of journal, book or online?? Dunn R. (1989). Individualizing instruction for mainstream gifted children. In R Milgram. Springfield (Ed.). Teaching Gifted & Talented Learners in Regular Classrooms. Illinois: Charles C. Thomas, pp??? Dunn R. (1998). Commentary: Teaching students through their perceptual strengths or preferences. Journal of Reading, 31(4), 304-309. Engeström, Y. (1987). Learning By Expanding: An Activity-Theoretical Approach to Developmental Research. Helsinki: Orienta-Konsultit. Hong H. & Kinshuk (2004). Adaptation to student learning styles in web-based educational systems. In L. Cantoni & C. McLoughlin (Eds.). Proceedings of the World Conference on Educational Multimedia, Hypermedia & Telecommunications (ED-MEDIA 2004). USA: American Association of Computer Education, pp. 491-496. Jonassen, D.H. & Grabowski, B. (1993). Individual differences and Instruction. Hillsdale, NJ.Publisher?? Kinshuk, P.A., Oppermann R. & Russell, D. (2001). Role of human teacher in web-based intelligent tutoring systems. Journal of Distance Learning, 6(1), 26-35. Magoulas G., Papanikolaou, K. & Grigoriadou, M. (2003). Adaptive web-based learning: accommodating individual differences through system’s adaptation. British Journal of Educational Technology. Vol 34(4). http://www.dcs.bbk.ac.uk/~gmagoulas/bjet.pdf McGreal, R., & Roberts, T. (2001). A primer on metadata for learning objects. http://www.elearningmag.com/issues/Oct01/learningobjects.asp. Nichols, M. (2003). A theory for eLearning. Educational Technology & Society, 6(2), 1-10, Available at http://ifets.ieee.org/periodical/6-2/1.html
Patel A. & Kinshuk (1997). Intelligent tutoring tools in a computer integrated learning environment for introductory numeric disciplines. Innovations in Education and Training International Journal, 34(3), 200-207. Peck, M.L. (1983). Aptitude treatment interaction research has educational value. Proceedings of the Association for Educational Communications and Technology Convention. New Orleans: Association for Educational Communications and Technology, pp. 564-622. Rumetshofer, H. & Wöß, W. (2003). XML-based adaptation framework for psychologicaldriven e-learning systems. Educational Technology & Society, 6(4), 18-29. http://ifets.ieee.org/periodical/6_3/4.pdf Santally, M., Govinda, M. & Senteni, A. (2004). Reusable learning objects aggregation for elearning courseware development at the University of Mauritius. Instructional Technology and Distance Learning. 1(7), 27-34. http://www.itdl.org/Journal/Jul_04/article02.htm Santally, M. & Senteni, A. (2003). Learning and cognitive styles consideration in the design of hypermedia courseware for distance learning. Proceedings of the International Conference on Open & Online Learning 2003. ICOOL 2003. http://icool.uom.ac.mu/ Shuell, T.J (1986). Cognitive conceptions of learning. Review of Educational Research, 56(4), 411436. Schneider, D. (2003). Conception and implementation of rich pedagogical scenarios through collaborative portal sites: clear focus and fuzzy edges. Proceedings of the International Conference on Open & Online Learning, ICOOL 2003. http://icool.uom.ac.mu/ Streibel, M.J. (1991). Instructional plans and situated learning in instructional technology. In G.J. Angling (Ed.). Past, Present and Future. USA:Eaglewood, pp 117-132. Taurisson, N. & Tchounikine, P. (2004). Supporting a learner community with software agents. Educational Technology & Society, 7 (2), 82-91. Wolf, C. (2002). iWeaver: towards an interactive web-based adaptive learning environment to address individual learning styles. European Journal of Distance Learning (EuroDL). http://www.eurodl.org/materials/contrib/2002/2HTML/iWeaver
