Authors:
Maren Scheffel
;
Frank Beer
and
Martin Wolpers
Affiliation:
Fraunhofer Institute for Applied Information Technology FIT, Germany
Keyword(s):
Usage metadata, Attention analysis, Self-regulated learning, Self-reflection, Self-monitoring, Software framework, Personalised learning software, Personal learning environment.
Related
Ontology
Subjects/Areas/Topics:
Assessment Software Tools
;
Computer-Aided Assessment
;
Computer-Supported Education
;
e-Learning
;
e-Learning Platforms
;
Information Technologies Supporting Learning
;
Learning Organizations
;
Learning/Teaching Methodologies and Assessment
;
Lifelong Learning: Continuing Professional Training & Development
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Social Context and Learning Environments
;
Virtual Learning Environments
Abstract:
In order to successfully learn in a self-regulated way, self-monitoring of the learner and reflection of learning behaviour is required. We therefore introduce a framework that collects usage metadata from application programs and stores them as Contextualized Attention Metadata (CAM). We also present three approaches on how we exploit the collected CAM for further analysis such as object recommendation or learning activity classification in order to help the learner become aware of her learning behaviour, to self-reflect and to support her during her learning processes.