Browsing by Author "Lindell, Rebecca"

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    Engaging Students in a Large-Enrollment Physics Class Using an Academically Focused Social Media Platform
    (AAPT, 2017) Gavrin, Andy; Lindell, Rebecca; Physics, School of Science
    There are many reasons for an instructor to consider using social media, particularly in a large introductory course. Improved communications can lessen the sense of isolation some students feel in large classes, and students may be more likely to respond to faculty announce-ments in a form that is familiar and comfortable. Furthermore, many students currently establish social media sites for their classes, without the knowledge or participation of their instructors. Such “shadow” sites can be useful, but they can also become distributors of misinformation, or venues for inappropriate or disruptive discussions. CourseNetworking (CN) is a social media platform designed for the academic environment. It combines many features common among learning management systems (LMS’s) with an interface that looks and feels more like Facebook than a typical academic system. We have recently begun using CN as a means to engage students in an introductory calculus-based mechanics class, with enrollments of 150-200 students per semester. This article presents basic features of CN, and details our initial experiences and observations.
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    Networks identify productive forum discussions
    (APS, 2018-07) Traxler, Adrienne; Gavrin, A.; Lindell, Rebecca; Physics, School of Science
    Discussion forums provide a channel for students to engage with peers and course material outside of class, accessible even to commuter and nontraditional populations. Forums can build classroom community and aid learning, but students do not always take up these tools. We use network analysis to compare three semesters of forum logs from an introductory calculus-based physics course. The networks show dense structures of collaboration that differ significantly between semesters, even though aggregate participation statistics remain steady. After characterizing network structure for each semester, we correlate students’ centrality—a numeric measure of network position—with final course grade. Finally, we use a backbone extraction procedure to clean up “noise” in the network and clarify centrality-grade correlations. We find that more central network positions are positively linked with course success in the two semesters with denser forum networks. Centrality is a more reliable indicator of grade than non-network measures such as postcount. Backbone extraction destroys these correlations, suggesting that the noise is in fact signal and further analysis of the discussion transcripts is required.