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Attrition in online learning is generally higher than in traditional settings, especially in large-scale online learning environments. A systematic analysis of individual differences in attrition and performance in 20 massive open online courses (N > 67, 000) revealed a geographic achievement gap and a gender achievement gap. Online learners in Africa, Asia, and Latin America scored substantially lower grades and were only half as likely to persist than those in Europe, Oceania, and Northern America. Women also exhibited lower persistence and performance than men. Yet more persistent learners were only marginally more satisfied with their achievement. The primary obstacle for most learners was finding time for the course, which was partly related to low levels of volitional control. Self-ascribed successful learners reported higher levels of goal striving, growth mindset, and feelings of social belonging than unsuccessful ones. Insights into why learners leave online courses inform models of attrition and targeted interventions to support learners achieve their goals.

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.

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Multimedia learning research has established several principles for the effective design of audiovisual instruction. The image principle suggests that showing the instructor’s face in multimedia instruction does not promote learning, because the potential benefits from inducing social responses are outweighed by the cost of additional cognitive processing. In an 8-week observational field study (N = 2,951), online learners chose to watch video lectures either with or without the instructor’s face. Although learners who saw the face reported having a better lecture experience than those who chose not to see the face, 35% watched videos without the face for self-reported reasons including avoiding distraction. Building on these insights, the authors developed a video presentation style that strategically shows the face to reduce distraction while preserving occasional social cues. A 10-week field experiment (N = 12,468) compared the constant with the strategic presentation of the face and provided evidence consistent with the image principle. Cognitive load and perceived social presence were higher in the strategic than in the constant condition, but learning outcomes and attrition did not differ. Learners who expressed a verbal learning preference experienced substantially lower attrition and cognitive load with the constant than the strategic presentation. The findings highlight the value of social cues for motivation and caution against one-size-fits-all approaches to instructional design that fail to account for individual differences in multimedia instruction.