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711: Intelligent Tutoring Systems Week 4 – Representations.

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Presentation on theme: "711: Intelligent Tutoring Systems Week 4 – Representations."— Presentation transcript:

1 711: Intelligent Tutoring Systems Week 4 – Representations

2 2 Today  Reading discussion  Any demos?  Work time

3 3 READING DISCUSSION

4 4 Introduction: Multiple representations  Claim: Multiple representations are better than one  Justification: Cognitive Theory of Multimedia Learning Cognitive Load Theory Working memory; sensory channels; modality-specific limited capacity subsystems  But what influences whether MERs are effective or not?

5 5 Design Parameters  Dimensions of multi-representational systems: Number of representations Distribution of information across representations Form of the representational system Sequence of representations Support for translation between representations

6 6 Distribution of Information No redundancy. High redundancy  Simplifies  More representations  More complex representations  Fewer representations

7 7 Design Parameters  Dimensions of multi-representational systems: Number of representations Distribution of information across representations Form of the representational system Sequence of representations Support for translation between representations

8 8 Sequence of Representations  Simultaneously  Consecutively

9 9 Design Parameters  Dimensions of multi-representational systems: Number of representations Distribution of information across representations Form of the representational system Sequence of representations Support for translation between representations

10 10 Translation between Representations  System-provided  Student-provided Spontaneously Prompted

11 11 Functions of Representations  Computational offloading  Re-representation  Graphical constraining

12 12 Cognitive Tasks  Understanding the form of the representation  Understanding the relation between representation and domain  Understanding how to select representations  Understanding how to construct representations

13 13 Learning with Multiple Representations  Perceptual variability leads to abstraction  Cognitive flexibility  Successful learning from multiple representations depends on connection-making

14 14 Functions of Multiple Representations  Complementary roles  Constrain interpretation  Construct deeper understanding

15 15 Design Heuristics  Complementary representations require understanding of each representation Minimize co-presence Provide dynamic linking  Constraining representations afford concrete representations Understanding of constraining relation Co-presence required  Constructing understanding through representations Optimal level of superficial similarity High redundancy is helpful Co-presence required

16 16 Cognitive Load Theory

17 17 Implications for ITS design

18 18 DEMOS Is there anything you want me to show?

19 19 WORK TIME

20 20 Tasks to work on  Ask me about issues wrt mass production  Integrate multiple representations in at least one of your problems  Implement support for connection making between representations where appropriate

21 21 FOR NEXT WEEK

22 22 Tasks before next week  Continue working on the interfaces and behavior graphs for your problems  Finish mass production if you haven’t done so  Integrate multiple representations if you didn’t finish in class  Do the assigned readings  Post on Moodle (Sunday, 11:59pm) on how you would apply the readings to your own tutor  Comment on others’ posts


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