Adaptive Educational Environments for Cognitive Skills Acquisition

1. Adaptive Educational Environments for Cognitive Skills Acquisition Ashok Patel Director, CAL Research De Montfort University Leicester, United Kingdom Tel/Fax: +44 116 257 7193 apatel@dmu.ac.uk Kinshuk Information Systems Dept, Massey University Palmerston North, New Zealand Tel: +64 6 350 5799 Ext 2090 Fax: +64 6 350 5725 kinshuk@massey.ac.nz

2. Introduction • Adaptive educational environments for cognitive skills in applied domains • Accommodation of both the ‘instuction’ and ‘construction’ of knowledge • Tutorial aims to provide better understanding of design principles for development of such environments • Design based on informed educational methodologies

3. Introduction Who are we?

4. Aspects of learning facilitated by adaptive educational environments

5. Major use of adaptive educational environments Life long learning Life long learners

6. Life-long “learning” • Continuous non-formal and informal learning activities (during and beyond student life!) • Motivation to learn is typically problem focused • Observation and practice are key elements. • Starts from birth!!

7. Life-long “learners” • Includes practitioners who have to learn continuously due to rapid changes in the technology employed for their work (just-in-time, learning-on-demand) • Learners generally do not have direct contact with subject experts • Learners traditionally pursued distance learning avenues.

8. Life-long learning and computers • Absence of human experts requires some kind of ‘filling the gap’ • A range of contemporary technological hardware, software and systems are sought to impart the domain competence to the learners • What is domain competence!

9. Domain competence = Domain Knowledge + Skills (Cognitive + Physical)

10. 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

11. 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 from mistakes. Examples : Computer simulation and virtual reality

12. 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.

13. 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.

14. 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.

15. How about computers? Discussion Which constituent of knowledge can be successfully facilitated through computers? Know-how Know-why Know-when Know-about

16. Constituents of Domain Competence Know-how (skill based) is easier to learn from making mistakes. It requires less reflection and hence most suitable for computer based learning.

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. TLTP Byzantium approach • TLTP Byzantium has used symbolic representations to explain the concepts by taking advantages of precise and concise mathematical notations. • Various features of the approach are: • Bottom-up approach • No centralised knowledge • Each Intelligent Tutoring Tool (ITT) is free standing

20. The Byzantium principles • use computers and human for what they are (currently) good at • employ useful software tools in the overall learning environment consisting of human teachers and education technologies

21. The Byzantium principles • add applied intelligence to the software tools to provide a degree of support to students, enabling them to work by themselves • understand the economics of the learning environment and be concerned with assessment and course management as they consume substantial human resource

22. Knowledge levels 1. Introductory application level, where the formation of a mental map of interrelated conceptual atoms takes place and the students learn how to use the basic tools of a discipline. 2. Advanced application level, where the vertical and horizontal integration takes place. 3. Actual application approximation level, where an attempt is made to stimulate the real world situation and students learn to understand the behavioural and environmental contexts of the procedural knowledge.

23. Intelligent Tutoring Tools (ITTs) • Mixed-initiative systems • All the knowledge is stored in two-fold knowledge base • Not intended to replace the human tutor, but to supplement • To be mixed and matched with other technologies (e.g. video) • Suitable for various configurations such as classroom, open and distance learning

24. Structure of an ITT 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.

25. Marginal costing relationships

26. Student-ITT Interaction • No fixed sequence of filling in the variables. • Any value can be entered in any variable, provided the whole network remains consistent. • Correct values are accepted even if the intermediate values are missing. • On entry of a wrong value and missing intermediate values, the student is advised to carry out necessary intermediate step(s).

27. Granularity Negotiation • Provision of intermediate variables in the interface. • Multiple screen interface where one screen carries forward the aggregation of the details in the previous screen. • Functional interface that zoom-in to show the constituents of a complex value presented as a single variable on the main interface e.g. formula in the Capital Investment Appraisal ITT.

28. 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

29. 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.

30. Feedback from tutoring module Context sensitive messages are generated to improve semantics and to prevent monotony. There are several possibilities: • If the student input is correct • If the student input is incorrect: • If the value can be derived directly from the work done by the student and/or from the given information • If enough information isn’t available to directly derive the value

31. Evaluation • Independent evaluation of Byzantium and another CAL package at University of Glasgow over three years period (Stoner & Harvey, 1999) • Student performance improved significantly since introduction of CAL • Improvement appeared to be mainly reflected in the students’ ability to complete numeric questions.

32. Evaluation: Student Feedback “Byzantium was useful because you could go over bits you were unsure about. It was better than a book because it was interactive. With the interactive questions you tend to pay more attention than you would to a book.” “Byzantium offers instant feedback, is more involving and you can do as many questions as you like.”

33. Evaluation: Student Feedback • 71% students showed a preference for Byzantium material while 8% indicated no particular preference. • The students wanted more tutoring systems like Byzantium, observing that it was good to use CAL if the tutoring software was good.

34. Future: Integrating the ITTs The ITTs can be integrated in two directions: • Vertical integration will allow holding and comparing results of different instances of an ITT, e.g. comparing four different springs. • Horizontal integration will allow use of multiple ITTs to solve a given problem, e.g. design of a typical safety valve using spring design, screw design and other ITTs.

35. Integrating the ITTs An individual ITT is, thus, an autonomous entity, connected in a decentralised network to make more sophisticated tutoring system. The integration can be achieved either: using an intelligent advanced application level interface or by software agents who would approach the ITTs searching for a solution of the problem in hand.

36. How this approach helps in cognitive skills?

37. Domain Skills Senso-motoric skills are externally visible. They can be easily acquired by visualising the processes and learn through observations. Cognitive skills run inside human mind. Their non-visible nature demands a more sophisticated learning process. Life long learners have more difficulty in acquiring cognitive skills!

38. What is the best suitable theoretical framework for facilitation of cognitive skills?

39. 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

40. 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

41. 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

42. 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

43. Cognitive apprenticeship Vs Guided discovery (guidance/instruction; observation of demonstrations to understand the concepts, exploration within limited boundaries, guidance/feedback …)

44. Example systems • Cognitive apprenticeship based learning environment (CABLE) • Interactive simulations based learning environment (InterSim)

45. CABLE

46. 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.

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

48. CABLE architecture • Contextual observation - for 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

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

50. CABLE