Towards building incremental affect models in self-directed learning scenarios

Added Title

International Conference on Computers in Education (21st : 2013)
ICCE 2013

College

College of Computer Studies

Department/Unit

Advance Research Institute for Informatics, Computing and Networking

Document Type

Conference Proceeding

Source Title

Proceedings of the 21st International Conference on Computers in Education, ICCE 2013

First Page

170

Last Page

172

Publication Date

1-1-2013

Abstract

Self-reflection and self-evaluation are effective processes for identifying good learning behavior. These are essential in self-directed learning scenarios because students have to be responsible for their own learning. Although students benefit from doing fine-grained analysis of their own behavior, which we observed in our previous work, asking them to perform tasks such as analysis and making annotations are tedious and take significant amount of time and effort. In this paper, we present our work on the development of incremental affect models that can be used to minimize effort in analyzing and annotating behavior. Incremental models have an added benefit of adaptability to new information, which can be used by future systems to provide up-to-date affect-related feedback in real time.

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Disciplines

Computer Sciences

Keywords

Self-managed learning; Affect (Psychology); Self-culture

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