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Luca Mazzola & Riccardo Mazza

USI - University of Lugano, SwitzerlandFaculty of Communication Sciences

Institute for Communication Technologies

HCII 2009 – San Diego, CA (USA) - 22 July 2009

Supporting Learners in Adaptive Learning Environments through the enhancement of the

Student Model

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Agenda

● Introduction ● Open Learner Model● Idea● The 6 research directions● The 11 dimensions for the analysis● Star plots● Conclusion● Next steps

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Introduction

● PLE (Personalized Learning Environments) as enabling tools for computer-supported learning

● Key aspects:● Adaptation / Personalization (1)● Adaptivity (2)

● (2) based on UM → LM → OLM → GLM

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Open Learner Model

● Open Learner Model:● internally used as base for every adaptivity● opened to inspection for learner/instructor

● Scrutable● Interactive

● On presentation of data● On modification of the internal model

● OLM as:● Useful source of information● Promote reflection as learning (metacognition)

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Idea● Presentation of OLM (regardless of specific model) has :

● Impact on cognitive load● Impact on real understanding of model

Aims: make the learning curve progressive and make more comprehensible its interpretation

●Which dimensions have the higher positive impact?● 6 dimensions identified● 11 dimensions for analysis/ranking

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Research directions

P1. Positioning the learner respect to class or to a group

P2. Introduction of innovative graphical interfaces

P3. Representation of temporal evolution of the model

P4. Use of adaptive representations of OLM

P5. Definition global student model integrating different

autonomous student models from different courses

P6. Define a metric to measure distance between students

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11 Analysis question (1/2)

7 Dimensions on effectiveness and difficulty:●Level of enhancement for socialization among students.●Effort required for the knowledge extraction and for reasoning over the data.●Computational complexity to maintain the model.●Difficulties in identifying one or more metrics.●Granularity of representation of the problem space (continuous, stepped or discrete)●Amount of data required to have a reliable model.●Difficulty in identifying the most useful data to collect and the level of aggregation.

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11 Analysis question (2/2)

4 Dimensions on interaction and user experience:● Novelties and benefits by the new graphical interface.● Impact on the cognitive load by the new information presented and the new way of presentation.● Complexity of the rules that drive the creation of the model and the speed of convergence to stable state.● Level of interactivity and interaction type (continuous, stepped, passive or composed)

Rated on a scale (5-based) by a pool of experts: → creation of star plot → relative ranking

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Star plots

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Conclusion

We are working on:● introducing graphical interfaces adapted to the learners' characteristics [P4]● trying to integrate some support for social aspects, such as the positioning of learners in the class or group [P1]

The definition of metrics [P6] could be an extension

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Next steps

- Validate some mockups for adapted presentation of learner model

- Implement an architecture able to adapt presentation to different learner characteristics

- Searching for a way to introduce metrics to allow the proximity analysis and visualization

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GRAPPLE project - 7th EU FP http://www.grapple-project.org

http://www.newmine.org

http://www.red-ink.ch

Questions?

luca.mazzola@lu.unisi.ch