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: [email protected]URL: http://fims-www.massey.ac.nz/~kinshuk/
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1 Congifurable, Incremental and Re- structurable Contributive Learning Environments Dr Kinshuk Information Systems Department Massey University, Private.
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Congifurable, Incremental and Re-structurable Contributive Learning
Environments
Dr KinshukInformation Systems Department
Massey University, Private Bag 11-222Palmerston North, New ZealandTel: +64 6 350 5799 Ext 2090
initialiser * Random problem generator * Prediction boundary initialiser * Prediction boundary updater
Student model
HyperITS server
Teacher model layer * Pedagogy base * Optional problem bank
Implementing teachers
Core interface and tutoring modules
Internet
Assessment data
Internet
Peers Peers
Internet
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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
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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
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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:a. ask the learner to try again;b. suggests the relationship to be
used;c. provides the calculation data;d. shows the full calculation, and
allows the learner to proceed.
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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.
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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.
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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.
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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