Context-dependent feedback prioritisation in exploratory learning revisited
Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review
The open nature of exploratory learning leads to situations when feedback is needed to address several conceptual difficulties. Not all, however, can be addressed at the same time, as this would lead to cognitive overload and confuse the learner rather than help him/her. To this end, we propose a personalised context-dependent feedback prioritisation mechanism based on Analytic Hierarchy Process (AHP) and Neural Networks (NN). AHP is used to define feedback prioritisation as a multi-criteria decision-making problem, while NN is used to model the relation between the criteria and the order in which the conceptual difficulties should be addressed. When used alone, AHP needs a large amount of data from experts to cover all possible combinations of the criteria, while the AHP-NN synergy leads to a general model that outputs results for any such combination. This work was developed and tested in an exploratory learning environment for mathematical generalisation called eXpresser.
| Original language | English |
|---|---|
| Title of host publication | User modeling, adaption and personalization: 19th international conference, UMAP 2011, Girona, Spain, July 11-15, 2011. proceedings |
| Editors | J. Konstan, R. Conejo, J. Marzo, N. Oliver |
| Place of Publication | Berlin |
| Publisher | Springer |
| Pages | 62-74 |
| Number of pages | 13 |
| Volume | 6787 |
| Edition | 6787 |
| ISBN (Print) | 9783642223624 |
| Publication status | Published - 2011 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Number | 6787 |
Documents
- UMAP.pdf
Accepted author manuscript (Post-print), 405 KB, PDF document
Related information
Projects
User modelling in Exploratory Learning Environments
Project: Research
ID: 92004
