Browsing by Author "Gavrin, A."

Now showing 1 - 5 of 5
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    Connecting students' homework to their participation in a course-based social network
    (2017) Gavrin, A.; Lindell, R.S.; Physics, School of Science
    This paper presents a comparison between students' efforts on homework (problem sets delivered and completed online using WebAssign) and their participation on a course-focused social media site. The social media platform, CourseNetworking (CN), has many features typical of Learning Management Systems (LMSs), but is distinct in several important ways. The interface is far more "student centric" than traditional LMSs, and is designed to increase engagement; most of the CN window is devoted to student-authored content. Also, the site measures and "gamifies" participation, using an algorithm that includes posts, completion of surveys, comments on other students' posts, and other actions. The setting for our efforts was an introductory calculus-based mechanics class enrolling approximately 150 students, most of whom were engineering majors. Course exams, problem sets, and labs followed a traditional model. Social media participation was not required, but it was encouraged and students could earn a small extra-credit bonus. We investigated correlations between social media "micropoints" and three variables associated with the homework: time on task, points earned, and assignments skipped. Our results show small to moderate correlations and statistical significance in all three cases. Pearson's correlation coefficients are r = 0.286, 0.444, and -0.436 for time on task, points earned, and assignments skipped, respectively. The associated p values are 1.2 × 10-3 for time on task, and p < 10-5 for the other two variables. Because the variables we measure are not normally distributed, we verify these results by also calculating Kendall's tau statistic. This analysis confirms both the size and significance of the correlations we observe. We do not suggest a causal connection; rather, our conclusion is that participation in the social network is a form of engagement with the class comparable to traditional measures of engagement such as homework effort and outcome. © American Society for Engineering Education, 2017.
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    Content analysis of instructor tools for building a learning community
    (American Association of Physics Teachers, 2018-12) Myers, Carissa; Traxler, Adrienne; Gavrin, A.; Physics, School of Science
    This work presents a content analysis of an online discussion forum accompanying a face-to-face introductory physics course. Content analysis is a quantitative method for analyzing text that uses a coding scheme to gain insight into student discussions. We explore the effects of "anchor" tasks, small weekly activities to help students engage with each other. The goal of this analysis was to examine how the distributions of codes are impacted by anchor versus non-anchor tasks, and different types of anchors. The result of this work was that the coding scheme was able to detect some differences between anchor and non-anchor threads, but further work should be done to observe behaviors that would require a more in-depth analysis of the text. This research is significant for physics education research (PER) because there is little PER using content analysis or studying online talk. This is a step towards identifying patterns in conversations between physics students and the tools that may help them have on topic conversations essential for their learning. Identifying such tools can aid instructors in creating effective online learning environments, and this project introduces "anchor" tasks as instructor tools for building a learning community.
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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.
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    Physics students’ reactions to an abrupt shift in instruction during the COVID-19 pandemic
    (2020) Gavrin, A.; Physics, School of Science
    We present a preliminary analysis of the effect of the COVID-19 pandemic on students in the context of a large enrollment introductory physics course. During the spring of 2020, ours was one of thousands of courses forced to change abruptly from face-to-face instruction to online delivery. We report the effects of this change on students through the lenses initially available to us, including our online homework system, an online forum, and the course evaluations. While preliminary, our results suggest that students were generally unhappy with the transition, but this disappointment did not translate into significantly reduced effort or success. Their primary concerns were not technological but stemmed from behavioral considerations either internal to themselves or external, from the instructor. Although the event probed here was the pandemic, our observations and conclusions may be applicable to other situations in which instruction changes suddenly due to natural or human-caused disasters.
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    Quantifying the linguistic persistence of high and low performers in an online student forum
    (2019-07) Myers, Carissa; Fox, Elizabeth; Traxler, Adrienne; Gavrin, A.; Physics, School of Science
    This work uses recurrence quantification analysis (RQA) to analyze the online forum discussion between students in an introductory physics course. Previous network and content analysis found differences in student conversations occurring between semesters of data from an introductory physics course; this led us to probe which concepts occur and persist within conversations. RQA is a dynamical systems technique to map the number and structure of repetitions for a time series. We treat the transcript of forum conversations as a time series to investigate and apply RQA techniques to it. We characterize the forum behaviors of high and low scoring students, such as their percentage of recurring topics and persistence of discussing a topic over time. We quantify how high scoring and low scoring students use online discussion forum and test whether different patterns exist for these groups. This work is the first adaptation of recurrence quantification methods from the field of psychology for physics education research. Using RQA, there was not a general, observable difference in how the two different groups, high- and low-scoring students, used the forum; however, there were differences when focusing in on and comparing one high-scoring student and one low-scoring student. This technique has the potential for analyzing other PER data such as interviews or student discussions.