Abstract
Open online learning environments attract an audience with diverse motivations who interact with structured courses in several ways. To systematically describe the motivations of these learners, we developed the Online Learning Enrollment Intentions (OLEI) scale, a 13-item questionnaire derived from open-ended responses to capture learners' authentic perspectives. Although motivations varied across courses, we found that each motivation predicted key behavioral outcomes for learners (N = 71, 475 across 14 courses).
From learners' motivational and behavioral patterns, we infer a variety of needs that they seek to gratify by engaging with the courses, such as meeting new people and learning English. To meet these needs, we propose multiple design directions, including virtual social spaces outside any particular course, improved support for local groups of learners, and modularization to promote accessibility and organization of course content. Motivations thus provide a lens for understanding online learners and designing online courses to better support their needs.
Funder
Stanford Graduate Fellowship
Publisher
Association for Computing Machinery (ACM)
Subject
Human-Computer Interaction
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