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  • 1. Forming and maintaining an accurate “image” of the user within adaptive educational hypermedia systems D. Vogiatzis1 , A. Tzanavari, S. Retalis, A. Papasalouros, P. Avgeriou Dimitrios Vogiatzis Aimilia Tzanavari Department of Computer Science Department of Computer Science University of Cyprus University of Cyprus 75 Kallipoleos St. P.O Box 20537 75 Kallipoleos St. P.O Box 20537 CY-1678, Nicosia Cyprus CY-1678, Nicosia Cyprus Tel:+357-22892749 Tel:+357-22892747 Fax¨+357 22892707 Fax¨+357 22892707 Email: dimitrv@cs.ucy.ac.cy Email: aimilia@cs.ucy.ac.cy Andreas Papasalouros Symeon Retalis National Technical University of Athens Department of Computer Science Department of Electrical and Computer University of Cyprus Engineering 75 Kallipoleos St., P.O. Box 20537 Software Engineering Laboratory CY-1678 Nicosia, Cyprus 15780 Zografou, Athens, Greece Tel: +357-22892246, Fax: Tel: ++301 7722487, Fax: ++301 7722519 +357-22-339062 Email: andpapas@softlab.ntua.gr Email: retal@ucy.ac.cy Abstract Adaptive hypermedia Educational Systems represent an emerging technology that provides a unique advantage over traditional Web-based Educational Systems; that is the ability to adapt to the user's needs, goals, preferences etc. Adaptive hypermedia Educational Systems (AHES) are increasingly becoming part of the mainstream education, yet there does not exist a disciplined way of designing them - most of the development is ad-hoc. This paper aims to fill this void, which is the absence of disciplined design, by recording the expertise of existing Adaptive Web-based Educational Systems in the form of design patterns. We present a categorization of the patterns according to an established paradigm in Adaptive Hypermedia, we examine the patterns in one of these categories and provide exemplary patterns for user modeling. 1. Introduction An Adaptive hypermedia Educational System (AHES) is a dynamic web-based application, which provides a tailored learning environment to its users, by adapting both the presentation and the navigation through the content. Such a system is comprised of learning resources, as well as a set of tools that facilitate the process of studying, such as exams/questionnaires, glossaries, communication tools, etc. The learning content is dynamically generated based on some pedagogical rules that combine the (domain) model of the content with the model of the user. AHES are currently a ‘hot’ topic of research in the broader field of adaptive hypermedia applications and several AHES systems have been built during the past years [Brusilovsky 2001]. 1 Corresponding author 1
  • 2. Several educational institutions are nowadays designing and developing their own AHES due to the fact that AHES leverage the shortcomings of non-adaptive web-based educational systems, also known as Learning Management Systems or Virtual Learning Environments. In contrast to the latter, AHES can provide tailored, one-to-one tutoring, according to the specific characteristics of each individual learner rather than serve the same content massively to all the learners. Furthermore, some educational or research institutions tend to develop their own AHES in order to implement and test their own learning theories or instructional design methods. However, the design and implementation of Adaptive Web-based Educational Systems (AHES) is a complex, if not overwhelming, task. This is due to the fact that it involves people from diverse backgrounds, such as software developers, web application experts, content developers, domain experts, instructional designers, user modeling experts and pedagogues, to name just a few. Moreover, these systems have presentational, behavioral, pedagogical and architectural aspects that need to be taken into account. To make matters worse, most AHES are designed and developed from scratch, without taking advantage of the experience from previously developed Adaptive Web-based Educational Systems, because the latter’s design is not codified or documented. As a result, development teams are forced to ‘re-invent the wheel’. Therefore, systematic and disciplined approaches must be devised in order to overcome the complexity and assortment of AHES and achieve overall product quality within specific time and budget limits. One such approach is the use of design patterns [Alexander et al. 1977], so that these systems are not designed and implemented from scratch, but based on reusable design experience gained over several years of trial-and-error attempts. Therefore good design can be made explicit, and available to the whole community of designers, so that it becomes common practice. In this way, designers of new or existing AHES, especially inexperienced designers, can take advantage of previous design expertise and save precious time and resources. Till now, the most systematic design approach for AHES is the AHAM design model [De Bra et al. 1999]. AHAM is based on the Dexter Model reference model for hypermedia systems. In order to address the adoption issues of an AH system, AHAM proposes three models, as shown in Figure 1: the Hypertext Domain Model, the User Model and the Teaching Model. The Hypertext Domain Model provides a conceptual view of the AH application defining the concepts and their relationships that form the hypermedia content as they are considered by the author. The User Model defines users’ personal characteristics as well as his/her history of interaction with the content that is conceptually described by the Domain Model. Thus, the User Model is closely tight with the previous model. The Teaching Model contains rules that transform the previous two models into hypermedia content presented to the user and control the evolution of the user model during the user’s navigation into the hypermedia content [Papasalouros & Retalis 2002]. 2
  • 3. Learner Model Teaching Model Hypertext Domain Model Conceptual Navigation Presentation Model Model Model Figure 1 Overview of the AHES conceptual Model according to AHAM This paper aims to initiate a pattern language in the domain of AHES for the user model component. The structure of the rest of this paper is as follows: the second section presents the overview conceptual model of the patterns for the user model component, where a categorization of the patterns into 2 main layers (design oriented and implementation oriented). The third section presents portrays an exemplary patterns. Finally, the fourth section wraps up with conclusions and ideas for future work. 2. The map of the user model patterns This section presents an initial set of design patterns for user model component of the AHES. The organization of these patterns can be achieved according to how they reference each other in the ‘related patterns’ field of their description. Figure 2 depicts the relationships between the proposed AHES user model design patterns. The semantics of the arrows between the patterns is that the pattern at the beginning of the arrow is a prerequisite of the pattern at the end of the arrow. The diagram also depicts the sequencing of the patterns, i.e. in what order they should be applied. Figure 3 depicts an expanded version of the patterns for the AWES. The user model component plays a pivotal role in the adaptation that takes place as part of an AHES. It is responsible for forming and maintaining an accurate “image” of the user, which at the same time has to be meaningful and useful to the system. This is subsequently used in the adaptation phase, where primarily content and presentation are tailored to the user’s needs [Kavčič 1999]. The patterns presented here attempt to cover the entire user modelling process (both at design and run time). More specifically, the patterns will try to capture the decisions that should be made for: i) the user model definition which shows what information a user model should have. Information about the user can be regarded as domain–independent and domain– dependent; or divided into knowledge component (user’s knowledge) and interfaces component (user’s preferences); or as static (constant through the learning process) and dynamic (changes during the learning process). ii) the user model initialisation addresses the problem of deriving the user model at the beginning of the learning process. Most of the times users fill in a short questionnaire with questions that refer to a selection of the element of the user model and then well– known methods from artificial intelligence and machine learning can be used for determining about his/her user model characteristics, in full. Such methods include Bayesian networks, rule learning, instance–based learning, learning of probabilities, logic–based, decision–theoretic, heuristic etc., as well as other general techniques and principles (plan recognition), and pecifically developed computational, or specifically 3
  • 4. developed qualitative rules and procedures (rules for selecting and evaluating examples, rules for choosing adaptation type, rules for choosing questions) [Jameson 1999]. iii) the user model maintenance addresses the problem of maintaining an accurate user model. Inputs for this part of the user model are gathered directly from user (willingness to change his/her preferences), through tests and practice (test results, user history of responses and problem solving behaviour), or user’s actions (browsing behaviour, number of nodes visited, visited concepts, time spent on page, total session time, selection of links, searching for further info, queries to the help system). This information is constantly being collected during the learning process and is also used for updating the user model. iv) the user model representation that contains definite design decisions for the implementation of the user model in an AWES. These are depicted in the lower part of figure 3. In particular the user representation is divided into three patterns, corresponding to the three patterns of the upper level of figure 3. That is, there is description representation, initialisation representation and maintenance implementation that correspond to user model definition, user model initialisation and user model maintenance respectively. In particular the user model representation solves the problem of choosing among the data types that can be used for representing the various elements the user model (e.g. triggers for knowledge level) as well as methods for maintaining the user model. In practice, the elements of user model can be represented as trees, lists, hash tables, etc. Concerning the methods, a combination of two or more is frequently applied, especially when different methods are used for initialising and maintaining the user model. This assures more accurate modelling and allows better exploitation of gathered information. These methods include Bayesian methods, machine learning methods (rule learning, learning of probabilities, instance– based learning), logic–based methods (first order predicate calculus), specifically developed computational procedures (user’s expertise is calculated from their navigational actions or time spent on documents), etc. This paper will deal with all the above categories of patterns for the user model component except from the user model representation. The main reason is that no firm results from evaluation studies exist, as yet that could help us in deriving design patterns that hold the distilled knowledge of implementers. User Model Component User Model Definition User Model Representation User Model Initialisation User Model Maintenance Figure 2. The patterns for user modelling 4
  • 5. Figure 3 The expanded pattern language for the AHES 3. Some Patterns 5
  • 6. Pattern Name: User model Definition Problem A user model in an AHES is essentially the information the system holds about the user and is mainly related to the learning process. This information has to be such that the system can better adapt to the user’s individual needs. When observing the learner computer interaction, several adaptations can take place, some of which do so explicitly and some implicitly, based on the interactive events, the path that the learner has taken, the performance concerning some learning tasks, etc [Kobsa 2001]. An adaptive or adaptable educational hypermedia enriches the application functionality by maintaining a “representation of the user” (or “user model”) and providing customization mechanisms to modify application features in response to user model updates. For adaptive educational hypermedia, user model updates are automatically generated by the system (by monitoring and interpreting the user’s interactions); for adaptable hypermedia, user model updates are under the user control. The adaptivity/adaptability feedback could be related to the organizational and presentational issues of the learning resources (permission to move forward, encouragement to read specific sections, undertake some tasks, etc.). The information related to learner’s profile and interaction based on which the AHES can take decisions for adaptivity and adaptability, should be standardised. Such a standardisation will greatly enhance the portability of the user model description as well as the interoperability of AHES that utilize such descriptions of learners. The problem is which elements should be chosen in order to have a well defined user model that could allow the AHES to know who the user is and to keep track of the learning activities. Analysis Data about a user serves for deriving contextual structures in an adaptive environment. There have been attempts to standardize a learner profile such as the IEEE Personal and Private Information (PAPI) [IEEE PAPI] and the IMS Learner Information Package (LIP) [IMS LIP]. These standards have been developed from different points of view. The PAPI standard reflects ideas from intelligent tutoring systems where the performance information is considered as the most important information about a learner. The PAPI standard also stresses the importance of inter- personal relationships discussed also in [Vassileva et al, 2002]. On the other hand the LIP standard is based on the classical notion of a CV and inter-personal relationships are not considered at all. PAPI distinguishes personal, relations, security, preference, performance, and portfolio information. The personal category contains information about names, contacts and addresses of a learner. Relations serve as a category for relationships of a specific learner to other persons (e.g. classmate, teacheris, teacherof, instructoris, instructorof, belongsto, belongswith). Security aims to provide slots for credentials and access rights. Preference indicates the types of devices and objects, which the learner is able to recognize. Performance is for storing information about measured performance of a learner through learning material (i.e. what does a learner know). Portfolio is for accessing previous experience of a user. Similarly the IMS LIP standard contains several categories for data about a user. The identification category represents demographic and biographic data about a learner. The goal category represents learning, career and other objectives of a learner. The QCL category is used for identification of qualifications, certifications, and licenses from recognized authorities. The activity category can contain any learning related activity in any state of completion. The interest category can be any 6
  • 7. 7
  • 8. Pattern Name: User model Initialisation Problem Before a user starts interacting with the Adaptive Web based Educational System (AWES), her user model must be initialised. It is the designer’s job is to define the techniques that initialise the user model. Various aspects need to be taken into account in the initalisation phase. For instance, what is the minimum amount of information to kick start the system, or what kind of information and what amount is the user capable or willing to provide? Naturally, the information stemming from initialisation should conform to the user model definition specifications. Analysis Not all elements of the UM description have to be acquired in order for the user to start interacting with the AWES. There are two reasons for that. In the beginning of an interaction session, users do not like spending a lot of time providing information about them, answering long questionnaires for instance. Second, it is not necessary to have a complete model of the user; a partial model (with proper selection of a subset of UM elements) will be acceptable. There are UM elements that can be acquired directly from the user and data that can be acquired through the AWES. For instance, demographic data can only provided by the user. On the other hand, user knowledge can also be derived by the system e.g. via the prehistory of user’s learning activities in other educational environments. It is also important to initialise the user stereotype, because according to the various groups of users based on their stereotypes the learning tasks will be specified for each group separately. There are two options for the UM, it will be identified with certainty, or it will be speculated. The first option is not usually the case in practical AWES systems. Solution The AWES designer should create fill-in forms with questions that refer to a desired subset of UM elements. The desired subset is required to form an initial view of the user model so as to kick start the AWES. There are a number of ways whereby the desired elements can be derived. Below we provide a list of plausible choices stemming from real AWES systems that a designer should take into account: The desired UM elements could be obtained explicitly, by presenting to the user a questionnaire, which she has to fill in. Typically, the user provides data, such as demographic data, user preferences, and possibly other sorts of data that are compatible with the user model description specification. Another option is to assume values for the desired user model elements from previous training sessions/learning activities of the user. For instance, a user having followed the prerequisites of the current course is considered to have enough knowledge to follow it. Yet another option is to assume certain values (with nothing to backup this choice apart from being plausible) for the desired user model elements, and then to proceed with the interaction, expecting that the user model will be corrected during the running time of the AWES. This is essentially a trial and error approach. 8
  • 9. Deriving the applicable stereotype requires that a minimum amount of knowledge and specifically a minimum number of user model definition elements is available. The derivation of the applicable stereotype can be performed in a number of ways (the following list is to be considered as indicative rather than complete): It can be user driven: For instance the user specifies explicitly that she belongs in the novices’ stereotype. Inferred by rules: Stereotypes are equipped with triggers, which activate them. Rules tell which UM elements and with what values can activate a stereotype. Speculated by rules: If it is the case that there is absolutely no information which can suggest a certain stereotype, then the AWES designer should have some rules to allow selection of the stereotype. For instance, a rule of this kind might be: if user does not specify her knowledge level, then assume it is average. Related Patterns: UM Definition. Know uses In Inspire [Grigoriadou et al. 2001] the user model is initialised, through a questionnaire filled in by the user at the beginning, or by explicitly selecting the category she fits in according to some general characteristics. ELM-ART II [Weber et al. 1997] , requests from the users to declare knowledge units, which are already known to them. In DCG [Vassileva. 1997], the user model, called student model, is initialized with a preliminary test. ACE [Specht et al. 2000] follows a somewhat mixed approach . The user model is initialised by explicit and implicit elicitation from the users. The former is performed, by the user, which specifies her learning strategies and stereotype; whereas the latter is done by a dynamically generated test. Pattern name: user model maintenance Problem With this pattern we provide a solution to the problem of how to maintain an accurate user model. We assume that a user model description and the relevant representation have been provided as well as the user model has been initialised. The user is interacting with the AWES with a purpose of learning something, during this process various aspects concerning the user change; the general idea is to capture the changes so as to maintain the user model. Analysis The assumption that the user model will remain the same as when it was acquired originally is in most cases incorrect. As in tutoring between a human tutor and a student, where the student constantly demonstrates changes, the user of an AWES also changes and as a result his model has to reflect this. During the course of interaction, there is a leverage of user’s knowledge develops and the usage data builds up. Since the adaptation is to a large extent based on user knowledge and usage data, changes should definitely be recorded and be related to a cause-result effect if the system is to function effectively. In fact, information such as “demographic data” does not change with a high frequency. There is also, information like “topics covered” (part of user knowledge) that changes continuously. 9
  • 10. It is also important for users to be in control (to a degree acceptable to the AWES) of their model for several reasons. They need to be able to modify information in their model if they feel that it is inaccurate or incorrect. Also, being in control builds up their trust in the system. Solution • The maintenance of an accurate UM can be user driven or system driven. In the former case it the user who provides explicit information about changes in his/her UM. In the latter case, the AWES derives information by closely watching the user. • The AWES designer should define the conditions that govern the maintenance of the user model. In particular the designer should define the scope of the maintenance changes. The scope defines the reason for updates. The reason is then quantified in terms of choice of elements to undergo change. For instance, if the scope says that only a minimal update of the user knowledge is going to occur, then choice of elements is bound to user knowledge only. On the other hand a wider choice for the scope would allow updates of user knowledge and user preferences (e.g. to read theory and having links to examples). Finally, the frequency of UM description elements updates should be defined. • The UM maintenance module elicits data to update the UM description. Elicitation of data could take many forms; next we provide some characteristic examples. The user is presented with a form to fill in, whereby the update UM is derived. UM description update can also be interactive, when the AWES opens for instance a pop-up form requesting the user to explicitly answer a question. Finally, another option for UM update is through filtering the stream of data that are produced through user interaction. A typical example is the browsing strategy which can be reduced to a small number of primitives, like ringiness, spikiness, loopiness, pathiness. The raw data constitute the actual path that the user has followed, but they must be filtered, process, or summarized to be translated to the predefined number of primitives. Related patterns: User model representation, user model description, user model initialisation. Known users: In [Grigoriadou et al. 2001] there is an Interaction Monitoring Module, which collects information and updates the learner model accordingly. The system allows the users to intervene, expressing their perspective. A similar approach is followed in [Weber et al. 1997], where the update of the user model is driven by the system. It is also possible to inspect and to edit the user model. Yet another similar approach is followed in [Vassileva 1997]. Student model changes are performed according to student progress. Students can also explicitly modify their Personal Traits and Preferences. In [Specht et al. 2000] there is a diagnostic module for automated updates of the user module. Learners can also modify their model anytime. Conclusions In this paper we proposed a disciplined approach for designing AHES which takes advandage of the notion of design patterns that capture the expertise, know-how and tacit knowledge of the developers of such systems. This approach can be beneficial to inexperienced designers of AWES. We have introduced a conceptual schema and some patterns for the user modeling component of the AHES. The proposed set of patterns structured as a hierarchy will help an AWES designer to have a 10
  • 11. broad view of this component, the problems that he/she has to solve as well as their interdependencies. However, we have not tackled the difficult problem of user model representation, which means that we have not proposed solid solutions for the developers. As mentioned in [Borchers 2001], for the design of interactive systems (and in our case educational systems) instructional designers, human computer interaction specialist, software engineers, and subject domain experts, all in a project team, should express their expertise in the form of a pattern language. The future direction of our work is to propose software design patterns for AHES which will be considered a useful language for communication among software developers, and a practical vehicle for introducing less experienced developers into the field. However, we will continue towards creating patterns for the design of AHES, since we agree with Borchers that there is “a good chance to push the concept of participatory design forward by introducing patterns”. This is what we are trying with a European funded project called ELEN (http://www.tisip.no/ELEN). We are involving user representatives, instructional designers, elearning specialists, domain experts, in the design process to evaluate prototypes of elearning systems, and participate in design discussions. Acknowledgement This work was supported by the “ELEN: A Network of elearning centers” project which is partly sponsored by the European Commission under Socrates Minerva programme (ref nums: 101421-CP-1-2002-1-CY-MINERVA-M). References [Alexander et al. 1977] Alexander, C., Ishikawa, S., Silverstein, M., Jacobson, M., Fiksdahl-King, I. and Angel, S. A Pattern Language (Oxford University Press, 1977). [Borchers 2001] Jan Borchers. A Pattern Approach to Interaction Design, John Wiley & Sons, ISBN: 0471498289, 2001 [Brusilovsky 2001] Peter Brusilovsky. Adaptive hypermedia. User Modeling and User Adapted Interaction, Ten Year Anniversary Issue (Alfred Kobsa, ed.) 11 (1/2), 87-110 [De Bra et al. 1999]. Paul De Bra, Geert-Jan Houben, and Hongjing Wu. AHAM: A Dexter-based Reference Model for Adaptive Hypermedia. Proc. of ACM Hypertext '99, Darmstadt, Germany, 147-156, February 1999 [Dolog et al., 2003] Peter Dolog, Rita Gavriloaie, Wolfgang Nejdl, and Jan Brase. Integrating adaptive hypermedia techniques and open rdf-based environments. In Proc. of 12th International World Wide Web Conference, Budapest, Hungary, May 2003. [Grigoriadou et al. 2001] Grigoriadou, M., Papanikolaou, K., Kornilakis, H. and Magoulas, G. (2001). INSPIRE: An Intelligent System for Personalized Instruction in a Remote Environment. In: Proc. of the 3rd Workshop on Adaptive Hypertext and Hypermedia, in User Modeling 2001, LNCS, Springer [Dolog & Nejdl 2003] Peter Dolog and Wolfgang Nejdl. Personalisation in Elena: How to cope with personalisation in distributed eLearning Networks. In Proceedings of the Conference on Worldwide Coherent Workforce, Satisfied Users - New Services For Scientific Information, Oldenburg, Germany, September 2003. 11
  • 12. [IEEE PAPI 2002] IEEE. IEEE P1484.2/D7, 2000-11-28. draft standard for learning technology. public and private information (papi) for learners (papi learner). Available at: http://ltsc.ieee.org/wg2/. Accessed on October 25, 2002. [IMS LIP 2002] IMS. IMS learner information package specification. Available at: http://www.imsproject.org/profiles/index.cfm. Accessed on October 25, 2002. A. Jameson, “What Can the Rest of Us Learn From Research on Adaptive Hypermedia – and Vice– Versa?”, Comments on “Adaptive Hypertext and Hypermedia”, 1999, http://w5.cs.uni- sb.de/~jameson/ahh/ahh-comments.html. [Jameson 1999]. A. Jameson, “User–Adaptive Systems: An Integrative Overview”, Tutorial presented at UM99, 7th International Conference on User Modeling, Banff, Canada, 1999 [Kavčič 1999]. Alenka Kavčič, "Adaptive Hypermedia Learning System on the Web", EUROMEDIA'99, München, Germany, April 1999. [Kobsa 2001] Alfred Kobsa. Generic user modeling systems. User Modeling and User-Adapted Interaction, 11(49):49–63, 2001. [Papasalouros & Retalis 2002]. Papasalouros, A. & Retalis, S. (2002). Ob-AHEM: A UML-enabled model for Adaptive Educational Hypermedia Applications. Interactive Educational Multimedia, 4, 76-88. [Specht et al. 2000] Specht, M., Oppermann, R. (2000) ACE: Adaptive Courseware Environment, Lecture Notes in Computer Science, Vol. 1892 [Vassileva. 1997] Vassileva J. Dynamic Courseware Generation, Communication and Information Technologies, Vol. 5, Issue 2, pp. 87-102 [Vassileva et al, 2002] Julita Vassileva, Gordon McCalla, and Jim Greer. Multi-agent multi-user modelling in I-Help. User Modeling and User-Adapted Interaction, 2002. [Weber et al. 1997] Weber, G. and Specht M. (1997) User Modeling and Adaptive Navigation Support in WWW-Based Tutoring Systems. In: Proc. of User Modeling 1997. 12