Engaging with massive online courses

By Ashton Anderson, Dan Huttenlocher, Jon M. Kleinberg, Jure Leskovec. The Web Conference, 2014

561 citations recorded by the source corpus.

The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively unexplored and poorly understood. In a MOOC, each student's complete interaction with the course materials takes place on the Web, thus providing a record of learner activity of unprecedented scale and resolution. In this work, we use such trace data to develop a conceptual framework for understanding how users currently engage with MOOCs. We develop a taxonomy of individual behavior, examine the different behavioral patterns of high- and low-achieving students, and investigate how forum participation relates to other parts of the course. We also report on a large-scale deployment of badges as incentives for engagement in a MOOC, including randomized experiments in which the presentation of badges was varied across sub-populations. We find that making badges more salient produced increases in forum engagement.

Subfields: Artificial Intelligence, Computer Science Applications, Education

View Paper: https://www.semanticscholar.org/paper/473597802980ea3e43c11905398287f9ecb4daef

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Citations in the corpus

Gamification in Education: A Systematic Mapping Study (2015, background): “, 2013), for learning, time management, and carefulness (Hakulinen & Auvinen, 2014), for contributing to threads and reading/voting on content (Anderson et al., 2014), or for performance and fun (Bartel & Hagel, 2014).”
Neural Cognitive Diagnosis for Intelligent Education Systems (2019, background): “Speciﬁcally, in intelligent education systems (Anderson et al. 2014; Burns et al. 2014), cognitive diagnosis aims to discover the states of students in the learning process, such as their proﬁciencies on speciﬁc knowledge concepts (Liu et al. 2018).”
Fine-Grained Similarity Measurement between Educational Videos and Exercises (2020, background): “As two main types of educational resources, millions of teaching videos and exercises have been generated and collected for learners of all ages [2].”
Investigating the effects of gamification-enhanced flipped learning on undergraduate students’ behavioral and cognitive engagement (2018, background): “Anderson, Huttenlocher, Kleinberg, and Leskovec (2014) used badges to reinforce participants ’ positive behavior in a massive open online course, and found that the accumulation of badges motivated students to read and vote 10 times more than students in non-gami ﬁ ed conditions.”
Behavior-Based Grade Prediction for MOOCs Via Time Series Neural Networks (2017, background): “Some research has focused on understanding student motivation and engagement across learning modes, e.g., [12], [13].”
Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy (2020, background): “Research has demonstrated that activities that encouraged online and social presence, enhance and build learner confidence and increase performance are critical factors in engagement (Anderson et al. 2014; Dixson 2015).”
EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction (2019, background): “venience, these systems have attracted great attentions from both educators and general publics [1], [2], [3].”