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Adaptive Learning Systems

Adaptive Learning Systems. Associate Professor Kinshuk Information Systems Department Massey University, Private Bag 11-222 Palmerston North, New Zealand Tel: +64 6 350 5799 Ext 2090 Fax: +64 6 350 5725 Email: kinshuk@massey.ac.nz URL: http://fims-www.massey.ac.nz/~kinshuk/. Introduction.

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Adaptive Learning Systems

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  1. Adaptive Learning Systems Associate Professor Kinshuk Information Systems Department Massey University, Private Bag 11-222 Palmerston North, New Zealand Tel: +64 6 350 5799 Ext 2090 Fax: +64 6 350 5725 Email: kinshuk@massey.ac.nz URL: http://fims-www.massey.ac.nz/~kinshuk/

  2. Introduction • Adaptive learning systems with particular focus on cognitive skills • Accommodation of both the ‘instuction’ and ‘construction’ of knowledge • Design based on informed educational methodologies

  3. What exactly we mean by Adaptivity in Adaptive Learning Systems?

  4. “Intelligence”/adaptivity Increased user efficiency, effectiveness and satisfaction by Improved correspondence between learner, goal and system characteristics

  5. Need of Intelligence/adaptivity • Users generally work on their own without external support. • System is used by variety of users from all over the world. • Customised system behaviour reduces meta-learning overhead for the user and allows focus on completion of actual task.

  6. Adaptable Systems Systems that allow the user to change certain system parameters and adapt the system behaviour accordingly. Adaptive Systems Systems that adapt to the users automatically based on system’s assumptions about user needs.

  7. How does adaptivity work? • System monitors user’s action patterns with various components of system’s interface. • Some systems support the user in the learning phase by introducing them to system operation. • Some systems draw user’s attention to unfamiliar tools. • User errors are primary candidate for automatic adaptation.

  8. Levels of adaptation • Simple: “hard-wired” • Self-regulating: monitors the effects of adaptation and changes behaviour accordingly • Self-mediating: Monitors the effects of adaptation on model before putting into practice • Self-modifying: Capable of chaging representations by reasoning about the interactions

  9. Problems in adaptation • User is observed by the system, actions are recorded, giving rise to data and privacy protection issues. • Social monitoring becomes possibility. • User feels being controlled by the system. • User is exposed to adaptation concept favoured by the designer of the system. • User may be distracted from the task by sudden automatic modifications.

  10. Recommendation for adaptive systems • Means for user to (de)activate or limit adaptation procedure • Offering adaptation in the form of proposal • User may define specific parameters used in adaptation • Giving user information about effects of adaptation hence preventing surprises • Editable user model

  11. Domain competence And computers

  12. Constituents of Domain Competence Reflection oriented and abstract Action oriented and experiential Easier to learn from mistakes Difficult to learn from mistakes Know-why Know-how Trial and error logical processes Know-why-not Know-how-not Know-when An example of the know-how aspect of know-when is the temporal context required for an appropriate sequence of operation An example of the know-why aspect of know-when is the environmental and behavioural contexts required for making a decision Know-when-not Context oriented and both experiential and abstract Know-what Know-about Awareness oriented

  13. Constituents of Domain Competence Know-how Ä It has an operational orientation. Ä It is mainly action-driven and hence pre-dominantly experiential. Ä It is difficult to inherit it from someone else’s experience. Know-how-not Ä Learning by mistakes. Examples : Computer simulation and virtual reality

  14. Constituents of Domain Competence Know-why Ä It has a causal orientation. Ä It is mainly reflection-driven and therefore based on abstraction. Ä It can be inherited from someone else’s line of reasoning. Know-why-not Ä Logical processes. Ä Needs deeper reflection.

  15. Constituents of Domain Competence Know-when (and -where) Ä It has a contextual orientation. Ä It provides the temporal and spatial context for both the know-how and know-why. It is thus both action and/or reflection driven.

  16. Constituents of Domain Competence Know-about Ä It has an awareness orientation. Ä It includes above three types of knowledge in terms of know-what. Ä It also contains information about the environmental context of this knowledge.

  17. Instruction in knowledge context Ideally, an instructional system, designed for novice users, teach all knowledge constituents. But, know-why is difficult to handle mainly for two reasons: 1. It needs natural language interaction. 2. It needs use of metaphors, which are difficult to understand for a novice user. Know-how, on the other hand, is operational, and can be conveyed to the user more easily, even with symbolic representations.

  18. Instruction in knowledge context Traditional hypermedia based ITSs approach, in general, has been to teach the know-why aspect of knowledge with the help of explanations. The links provide stimulus to the user to know more about a particular topic. System works more as a friendly librarian and learning depends on the initiative of a student.

  19. Theoretical framework best suitable for facilitation of cognitive skills? Cognitive Apprentice Framework

  20. Cognitive apprenticeship framework • Modelling: Learners study the task pattern of experts to develop own cognitive model • Coaching: Learners solve tasks by consulting a tutorial component of the environment • Fading: Tutorial activity is gradually reduced in line with learners’ improving performance and problem solving competence

  21. Phases of Cognitive apprenticeship World knowledge (initial requirement) Observation of interactions among masters and peers Assisting in completion of tasks done by master Trying out on own by imitating

  22. Phases of Cognitive apprenticeship Getting feedback from master Getting advise for new things on the basis of results of imitation, comparing given solution with alternatives Reflection by student, resulting from master’s advice

  23. Phases of Cognitive apprenticeship • Repetition of process from 2 to 7 • Fading out guidance and feedback • Active participation, exploration and innovation come in • Assessment of generalisation of the tasks and concepts learnt during repetition process

  24. Example system • Cognitive apprenticeship based learning environment (CABLE)

  25. CABLE objectives Environment should facilitate: • acquisition of basic domain knowledge; • application of the basic domain knowledge in non-contextual and contextual scenarios to get skills of the discipline; and • generalisation of the domain knowledge to get competence of applying it in real world situations.

  26. CABLE architecture • Observation - for acquisition of concepts • Simple imitation - skills acquisition through articulation of concepts • Advanced imitation - generalisation and abstraction of already acquired concepts and for acquisition of skills of applying concepts in different contexts

  27. CABLE architecture • Contextual observation - deeper learning after imitation process results into the identification of gaps in learner’s current understanding of the domain knowledge • Interpretation of real life problems - for acquiring competence in such narrative problems as encountered in real life situations

  28. CABLE architecture • Mastery in skills - for repetitive training • Assessment - for measurement of overall progress

  29. CABLE

  30. Intelligent Tutoring Tools Structure A network of inter-related variables where the whole network remains constant. Example, partial network of 7 out of a total of 14 variables in marginal costing.

  31. Marginal costing relationships

  32. Structure of an ITT Knowledge Base 1. Variables 2. Relationships 3. Tolerances Modes - Student - Lecturer - Administrator Inference Engine Expert Model 1. Correct values 2. Derivation procedure (Local expert model) Random Question Generator Tutoring Module Student Model Dynamic Messaging System 1. Student input 2. Value status (filled or blank) 3. Derivation procedure 4. Interface preferences Feedback Input (student answer, position) (four levels) File Management User Interface module Context based link to textual description Marker Lecturer’s model answer to any lecturer generated narrative questions (Remote Expert Model) Add-ons 1. Calculator 2. Table Interface 3. Formula Interface } Application specific

  33. Tutoring Strategy of an ITT • Introduction of complexity in phased manner • Corrective, elaborative and evaluative aspects of student model are used for tutoring. • Learning process is broken down to very small steps through suitable interfaces. • ‘Road to London’ paradigm is adopted to eliminate the need for diagnostic, predictive and strategic aspects.

  34. CABLE Demo Future work on mental process modelling

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