학술논문

Open Social Student Modeling for Personalized Learning
Document Type
Periodical
Source
IEEE Transactions on Emerging Topics in Computing IEEE Trans. Emerg. Topics Comput. Emerging Topics in Computing, IEEE Transactions on. 4(3):450-461 Sep, 2016
Subject
Computing and Processing
Adaptation models
Adaptive systems
Computational modeling
Color
Databases
Context
Visualization
Adaptive hypermedia
personalized e-learning
visualization
user issues
Language
ISSN
2168-6750
2376-4562
Abstract
Open student modeling (OSM) is an approach to technology-based learning, which makes student models available to the learners for exploration. OSM is known for its ability to increase student engagement, motivation, and knowledge reflection. A recent extension of OSM known as open social student modeling (OSSM) complements cognitive aspects of OSM with social aspects by allowing students to explore models of peer students and/or an aggregated class model. In this paper, we introduce an OSSM interface, MasteryGrids, and report the results of a large-scale classroom study, which explored the impact of the social dimension of OSSM. Students in a database management course accessed nonrequired learning materials (examples and problems) via the MasteryGrids interface using either OSM or OSSM. The results revealed that OSSM-enhanced learning, especially for students with lower prior knowledge, compared with OSM. It also enhanced user attitude and engagement. Amount of student usage, efficiency of student usage, and student attitude varied depending on the combination of interface condition (OSM/OSSM), gender, and student social comparison orientation.