1 Adaptive Learning Systems Associate Professor Kinshuk Information Systems Department Massey University, Private Bag Palmerston North, New Zealand Tel: Ext 2090 Fax: URL:

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.

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8 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.

9 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

10 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.

11 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

12 Domain competence And computers

13 Constituents of Domain Competence Know-why Know-how Know-how-not Know-why-not Know-when Know-when-not Know-what logical processes Know-about Easier to learn from mistakes 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 Action oriented and experiential Reflection oriented and abstract Difficult to learn from mistakes Trial and error Context oriented and both experiential and abstract Awareness oriented

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

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

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

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

18 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. Instruction in knowledge context

19 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. Instruction in knowledge context

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

21 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

22 Phases of Cognitive apprenticeship 1. World knowledge (initial requirement) 2. Observation of interactions among masters and peers 3. Assisting in completion of tasks done by master 4. Trying out on own by imitating

23 Phases of Cognitive apprenticeship 5. Getting feedback from master 6. Getting advise for new things on the basis of results of imitation, comparing given solution with alternatives 7. Reflection by student, resulting from master’s advice

24 Phases of Cognitive apprenticeship 8. Repetition of process from 2 to 7  Fading out guidance and feedback  Active participation, exploration and innovation come in 9. Assessment of generalisation of the tasks and concepts learnt during repetition process

25 Example system  Cognitive apprenticeship based learning environment (CABLE)

26 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. CABLE objectives

27 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

28 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

29 CABLE architecture  Mastery in skills - for repetitive training  Assessment - for measurement of overall progress

30 CABLE

31 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. Intelligent Tutoring Tools Structure

32 Marginal costing relationships

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

34 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.

35 CABLE Demo Future work on mental process modelling