Supporting Learners in Adaptive Learning Environments through the enhancement of the Student Model
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This document summarizes a presentation on supporting learners in adaptive learning environments through enhancements to the student model. It discusses open learner models and 6 proposed research directions to make the learning curve more progressive and the interpretation of models more comprehensible. These include positioning learners, innovative interfaces, temporal evolution, adaptive representations, integrating models across courses, and defining metrics. 11 dimensions for analyzing the research directions are identified relating to effectiveness, complexity, data needs, and user experience. Star plots will be used to relatively rank the directions. Current work involves adapted interfaces and social positioning metrics, with future steps validating interfaces and introducing proximity metrics and visualization.
![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
10](https://image.slidesharecdn.com/hcii091-090721145823-phpapp02/85/Supporting-Learners-in-Adaptive-Learning-Environments-through-the-enhancement-of-the-Student-Model-10-320.jpg)
Supporting Learners in Adaptive Learning Environments through the enhancement of the Student Model
- 1. HCII 2009 – San Diego, CA (USA) - 22 July 2009 Supporting Learners in Adaptive Learning Environments through the enhancement of the Student Model Luca Mazzola & Riccardo Mazza USI - University of Lugano, Switzerland Faculty of Communication Sciences Institute for Communication Technologies 1
- 2. Agenda ● Introduction ● Open Learner Model ● Idea ● The 6 research directions ● The 11 dimensions for the analysis ● Star plots ● Conclusion ● Next steps 2
- 3. 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 3
- 4. 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) 4
- 5. 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 5
- 6. 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 6
- 7. 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. 7
- 8. 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 8
- 9. Star plots 9
- 10. 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 10
- 11. 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 11
- 12. 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 12