Talks and Poster Presentations (with Proceedings-Entry):
M. Hochmeister, J. Daxböck, J. Kay:
"The Effect of Predicting Expertise in Open Learner Modeling";
Talk: European Conference on Technology Enhanced Learning,
- 09-21-2012; in: "21st Century Learning for 21st Century Skills - 7th European Conference of Technology Enhanced Learning, EC-TEL 2012",
A. Ravenscroft et al. (ed.);
Springer-Verlag Berlin Heidelberg,
Learnerīs self-awareness of the breadth and depth of their expertise is crucial for self-regulated learning. Further, of learners report self-knowledge assessments to teaching systems, this can be used to adapt teaching to them. These reasons make it valuable to enable learners to quickly and easily create such models and to improve them. Following the trend to open these models to learners, we present an interface for in- teractive open learner modeling using expertise predictions so that these assist learners in reflecting on their self-knowledge while building their models. We report study results showing that predictions (1) increase the size of learner models significantly, (2) lead to a larger spread in self-assessments and (3) influence learnersī motivation positively.
Prediction,Expertise,Open Learner Model,Self-assessment, Metacognition, Adaptive Educational Systems
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.