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Supporting Learners in Adaptive Learning Environments through the enhancement of the Student Model
HCII 2009 – San Diego, CA (USA) - 22 July 2009
Supporting Learners in Adaptive Learning
Environments through the enhancement of the
Luca Mazzola & Riccardo Mazza
USI - University of Lugano, Switzerland
Faculty of Communication Sciences
Institute for Communication Technologies
● Open Learner Model
● The 6 research directions
● The 11 dimensions for the analysis
● Star plots
● Next steps
●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
Open Learner Model
● Open Learner Model:
● internally used as base for every adaptivity
● opened to inspection for learner/instructor
● On presentation of data
● On modification of the internal model
● OLM as:
● Useful source of information
● Promote reflection as learning (metacognition)
●Presentation of OLM (regardless of specific model)
● 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
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
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.
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
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
- 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