Massively Open Online Courses (MOOCs) have gained attention recently because of their great potential to reach learners. Substantial empirical study has focused on student persistence and their interactions with the course materials. However, most MOOCs include a rich textual dialogue forum, and these textual interactions are largely unexplored. Automatically understanding the nature of discussion forum posts holds great promise for providing adaptive support to individual students and to collaborative groups. This paper presents a study that applies unsupervised student understanding models originally developed for synchronous tutorial dialogue to MOOC forums. We use a clustering approach to group similar posts, compare the clusters with manual annotations by MOOC researchers, and further investigate clusters qualitatively. This paper constitutes a step toward applying unsupervised models to asynchronous communication, which can enable massive-scale automated discourse analysis and mining to better support students' learning.
Abstract.A key challenge in the design of tutorial dialogue systems is identifying tutorial strategies that can effectively balance the tradeoffs between cognitive and affective student outcomes. This balance is problematic because the precise nature of the interdependence between cognitive and affective strategies is not well understood. Furthermore, previous studies suggest that some cognitive and motivational goals are at odds with one another because a tutorial strategy designed to maximize one may negatively impact the other. This paper reports on a tutorial dialogue study that investigates motivational strategies and cognitive feedback. It was found that the choice of corrective tutorial strategy makes a significant difference in the outcomes of both student learning gains and self-efficacy gains.
Affective and cognitive processes form a rich substrate on which learning plays out. Affective states often influence progress on learning tasks, resulting in positive or negative cycles of affect that impact learning outcomes. Developing a detailed account of the occurrence and timing of cognitive-affective states during learning can inform the design of affective tutorial interventions. In order to advance understanding of learning-centered affect, this paper reports on a study to analyze a video corpus of computer-mediated human tutoring using an automated facial expression recognition tool that detects fine-grained facial movements. The results reveal three significant relationships between facial expression, frustration, and learning: 1) Action Unit 2 (outer brow raise) was negatively correlated with learning gain, 2) Action Unit 4 (brow lowering) was positively correlated with frustration, and 3) Action Unit 14 (mouth dimpling) was positively correlated with both frustration and learning gain. Additionally, early prediction models demonstrated that facial actions during the first five minutes were significantly predictive of frustration and learning at the end of the tutoring session. The results represent a step toward a deeper understanding of learning-centered affective states, which will form the foundation for data-driven design of affective tutoring systems.
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