Development of a Reading Material Recommender System Based On Design Science Research Approach

Date
2019-01-08
Authors
Eryilmaz, Evren
Thoms, Brian
Lee, Kuo-Hao
de Castro, Melissa
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Using design science research (DSR), we outline the construction and evaluation of a recommender system incorporated into an existing computer-supported collaborative learning environment. Drawing from Clark’s communication theory and a user-centered design methodology, the proposed design aims to prevent users from having to develop their own conversational overload coping strategies detrimental to learning within large discussions. Two experiments were carried out to investigate the merits of three collaborative filtering recommender systems. Findings from the first experiment show that the constrained Pearson Correlation Coefficient (PCC) similarity metric produced the most accurate recommendations. Consistently, users reported that constrained PCC based recommendations served best to their needs, which prompted users to read more posts. Results from the second experiment strikingly suggest that constrained PCC based recommendations simplified users’ navigation in large discussions by acting as implicit indicators of common ground, freeing users from having to develop their own coping strategies.
Description
Keywords
Learning in Digital and Social Media, Digital and Social Media, Computer-supported collaborative learning, conversation overload, design science research, user-centered design
Citation
Rights
Access Rights
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.