As MOOCs grow in popularity, the relatively low completion rates of learners has been a central criticism. This focus on completion rates, however, reflects a monolithic view of disengagement that does not allow MOOC designers to target interventions or develop adaptive course features for particular subpopulations of learners. To address this, we present a simple, scalable, and informative classification method that identifies a small number of longitudinal engagement trajectories in MOOCs. Learners are classified based on their patterns of interaction with video lectures and assessments, the primary features of most MOOCs to date.In an analysis of three computer science MOOCs, the classifier consistently identifies four prototypical trajectories of engagement. The most notable of these is the learners who stay engaged through the course without taking assessments. These trajectories are also a useful framework for the comparison of learner engagement between different course structures or instructional approaches. We compare learners in each trajectory and course across demographics, forum participation, video access, and reports of overall experience. These results inform a discussion of future interventions, research, and design directions for MOOCs. Potential improvements to the classification mechanism are also discussed, including the introduction of more fine-grained analytics.

Researchers describe with increasing confidence what they observe participants doing in massive open online courses (MOOCs). However, our understanding of learner activities in open courses is limited by researchers' extensive dependence on log file analyses and clickstream data to make inferences about learner behaviors. Further, the field lacks an empirical understanding of how people experience MOOCs and why they engage in particular activities in the ways that they do. In this paper, we report three findings derived by interviewing 13 individuals about their experiences in MOOCs. We report on learner interactions in social networks outside of MOOC platforms, notetaking, and the contexts that surround content consumption. The examination and analysis of these practices contribute to a greater understanding of the MOOC phenomenon and to the limitations of clickstream-based research methods. Based on these findings, we conclude by making pragmatic suggestions for pedagogical and technological refinements to enhance open teaching and learning.

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. ACM Reference Format:René F. Kizilcec and Emily Schneider. 2015. Motivation as a lens to understand online learners: Toward data-driven design with the OLEI scale.

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