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Congifurable, Incremental and Re-structurable Contributive Learning Environments

Congifurable, Incremental and Re-structurable Contributive Learning Environments. Dr 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

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Congifurable, Incremental and Re-structurable Contributive Learning Environments

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  1. Congifurable, Incremental and Re-structurable Contributive Learning Environments Dr 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. Reusability • Benefits are widely known • However, early promises of time and cost savings hae not materialised • In software reuse, only trivial pieces of code can be used in another context without much effort

  3. CIRCLE Architecture • Only way to increase usability and in the process automatically increase the reusability, is to allow: • implementing teacher to contributethrough: • configuring the learning space • Incrementally adding and re-structuring • scope and functionality of IES components • Early adoption: HyperITS

  4. HyperITS • No pre-defined sequence of operations • Concepts linked in an interrelationship network • Inconsistency results in graded feedback leading the learner gradually to the point of start • Mis-conceptions and missing conceptions are identified.

  5. HyperITS • Emphasis on cognitive skills development • Uses cognitive apprenticeship approach to provide cognitive skills: • Observation • Imitation • Dynamic feedback by learning • Interpretation of data • Static feedback from testing

  6. HyperITS • Granular design • Domain concepts are acquired in the context of their inter-relaed concepts • Interfaces are brought up to give: • Another perspective on the data set • Fine grained interface to give details of a coarse grained presentation • Fine grained basic application to revise steps at a more advanced level

  7. HyperITS • Process modelling • Overcoming the shortcomings of overlay model • Understanding learner’s mental processes • Allows finding optimal and sub-optimal paths in learning process

  8. HyperITS Architecture

  9. Knowledge representation

  10. Knowledge repres. framework

  11. Domain Layer • Static domain content provided by the designing teacher: • Concepts, the smallest learning units • Relationships among concepts • Priorities associated with the relationships • Custom operator definitions • Constraints on backward chaining, if desired

  12. Teacher Model Layer • Consists of the pedagogy base reflecting various tutoring strategies and scaffolding provided by the implementing teacher • Optional problem bank created by the implementing teacher to situate the concepts in a particular context • The teacher can also provide additional diverse contexts

  13. Contextual Layer • Contains the current goals and structural information of current tasks: • system’s solution to current problem; • system’s problem solving approach; • immediate goals. • This information is dynamically updated along with the learner’s progress in problem solving.

  14. Initialization functionality • Domain representation initialiser • initialises the system according to the current learning goal for all types of problems. • Random problem generator • randomly selects concepts to treat as independents and creates their instances by randomly generating values within specified boundaries.

  15. Initialization functionality • Prediction boundary initialiser • initialises the boundaries for the overlay model (how far student’s solution can go from expert solution). • These boundaries are used later to evaluate a learner’s action.

  16. If independent variable introduced • Contextual dependency finder • identifies the dependent concepts that can be derived within in the current state of the problem space. • Dependency activator (client side) • activates the instances of the contextually dependent concepts and invokes the dependency calculator at server to update their current status in the expert solution.

  17. If independent variable introduced • Dependency calculator (server side) • provides values for the dependent concepts based on domain layer and pedagogy base to update the expert solution. • This functionality allows a learner to adopt a different route to the solution than the one currently adopted by the system.

  18. Setting validation bounds for dependent variables • Prediction boundary updater • updates the prediction boundaries used in comparing a learner’s solution with the expert solution. The updater fine-tunes the system’s initial prediction boundaries to match the route to solution adopted by a learner.

  19. Validation of learner’s input to dependent variables • Discrepancy evaluator • evaluates the validity of a learner’s attempt by matching it with the expert solution within the prediction boundaries.

  20. Validation of learner’s input to dependent variables • Dynamic feedback generator • provides context-based feedback to the learner. The messages are generated dynamically to improve semantics and to prevent monotony. • Granular approach is used in identifying the source of error and for providing feedback.

  21. Validation of learner’s input to dependent variables • Dynamic feedback generator • i.Basic misconceptions, where the learner fails to derive a variable due to misconceptions about the critical concepts. In such cases, graded scaffolding is used: • ask the learner to try again; • suggests the relationship to be used; • provides the calculation data; • shows the full calculation, and allows the learner to proceed.

  22. Validation of learner’s input to dependent variables • Dynamic feedback generator • ii. Missing conceptions, when learner unsuccessfully tries to derive a variable that requires derivation of intermediate variables  the error arising from missing knowledge about intermediate relationships. • System suggests learner to derive the intermediate concept first.

  23. Validation of learner’s input to dependent variables • Dynamic feedback generator • iii. If learner unsuccessfully tries to derive some complex concepts, system advises the learner to use a finer grain interface. • The finer grain interface deconstructs the complex concept into components to capture the misconceptions at a fine grain level.

  24. Evaluating learner’s process of deriving solution • Local optimiser • identifies the possible relationships and determines the best relationship to use based on the priorities specified in the domain layer. • It allows the system to identify any sub-optimal approach adopted by the learner.

  25. Finally.. Adequate technologies are rapidly emerging that can be harnessed for deploying the CIRCLE Architecture. For example: Distributed Component Object Model (DCOM) for Microsoft development tools or Remote Method Invocation (RMI) for Java

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