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Interactive learning tasks and their technical requirements to multimedia learning systems

Interactive learning tasks and their technical requirements to multimedia learning systems. Sebastian Rudolph & Hermann Körndle TU Dresden. Outline. Part I – learning tasks definition and purposes design principles Part II – increase interactivity on the importance of appropriate feedback

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Interactive learning tasks and their technical requirements to multimedia learning systems

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  1. Interactive learning tasks and their technical requirements to multimedia learning systems Sebastian Rudolph & Hermann Körndle TU Dresden Bremen 22. 04. 2004

  2. Outline • Part I – learning tasks • definition and purposes • design principles • Part II – increase interactivity • on the importance of appropriate feedback • evaluating free answers via Conceptual Graphs

  3. Definition An interactive learning task fosters knowledge acquisitionand consolidation by • focussing the user‘s attention to central contents, • invoking, guiding, and enforcing a learning activity in the user, • in case of difficulties, providing appropriate help that enables the user to continue solving the task. It can be integrated into all phases of the learning process.

  4. Integration of tasks into the learning process Preparation • make requirements transparent • activate previous knowledge • guide attention Tasks • check learning success • scrutinize strategies • learning and practicing • apply strategies • use processing hints Evaluation Execution

  5. Construction decisions for complex interactive learning tasks • content • specification of the topic • content structure • operations • specification of the cognitive operations to perform • specification of the solution path • form • presentation component • reactive component • interaction channels • interaction • design of the interaction processes and feedback algorithms • specification of additional information

  6. Elaborated feedback: an example • field-tested task set for the advanced qualification in Latin • empiric error analyses • Example

  7. task with specific source of error task with same source of error right wrong wrong I. knowledge of response (KR) repeat right solution path right 1st correction attempt wrong • II. KR & knowledge about mistake (KM) • colored indication of error position • in case of systematic error: hint wrt. solution strategy • otherwise: hint by presenting worked-out example right repeat right solution path right 2nd correction attempt wrong • III.knowledge of correct response (KCR) und knowledge on how to proceed (KH) • colored indication of error position • juxtaposition of correct vs. wrong result • acoustic presentation of the correct solution procedure task with new source of error

  8. Complex tasks • combining „task primitives“, multimedial presentations and simulation environments enhances mental integration of the content • tasks referring to precisely defined and conceptually ordered terms could benefit from the application of ontologies • Example

  9. How to increase interactivity even further? • until now: tasks with restricted answer formats • advantage: automatic evaluation is easy • disadvantage: by presenting choices, solving by recognition is enabled • disadvantage: the answer‘s structure is prescribed • desirable: introduction of tasks with free answers • advantage: higher requirements to the student, who has to structure the answer him-/herself • disadvantage: automatic evaluation problematic

  10. What about Conceptual Graphs? • invented by John F. Sowa based on Ideas of Charles S. Peirce.[J. Sowa: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, 1983] • purpose: semantically oriented storage, representation and processing of knowledge • aim: kind of representation being equally interpretable and processable by humans and computers • similarities to semantic networks in cognition psychology

  11. Basic example „Sebastian works on conceptual graphs in the TOOLKIT project.“ person: Sebastian workOn topic: ConceptualGraphs Agnt Thm Inst Toolkit project: composed from: concepts concept type referent , consisting of and (optional) • conceptual relations • directed arcs

  12. Basic example „Sebastian works on conceptual graphs in the TOOLKIT project.“ person: Sebastian workOn topic: ConceptualGraphs Agnt Thm Inst Toolkit project: composed from concepts concept type referent , consisting of and (optional) • conceptual relations • directed arcs

  13. Basic example „Sebastian works on conceptual graphs in the TOOLKIT project.“ person: Sebastian workOn topic: ConceptualGraphs Agnt Thm Inst Toolkit project: composed from: concepts concept type referent , consisting of and (optional) • conceptual relations • directed arcs

  14. Basic example „Sebastian works on conceptual graphs in the TOOLKIT project.“ person: Sebastian workOn topic: ConceptualGraphs Agnt Thm Inst Toolkit project: composed from: concepts concept type referent , consisting of and (optional) • conceptual relations • directed arcs

  15. Basic example „Sebastian works on conceptual graphs in the TOOLKIT project.“ person: Sebastian workOn topic: ConceptualGraphs Agnt Thm Inst Toolkit project: composed from: concepts concept type referent , consisting of and (optional) • conceptual relations • directed arcs

  16. Complex example „Tom believes Mary wants to marry a sailor. “

  17. Wer/Was Wem/Was Wen/Was Sketch: How to handle results of tasks with free answers (1/2) parse... generate... Elektronen entziehen convert... Reaktionspartner Oxidationsmittel

  18. Oxidationsmittel Wer/Was Wer/Was Wem/Was wegnehmen Wen/Was Wen/Was Elektronen Sketch: How to handle results of tasks with free answers (2/2) collection of answers conceptual hierarchy CG-pool Ontology input graph Oxidationsmittel entziehen Elektronen Reaktionspartner

  19. Thank you!

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