@article{gitinabard_heckman_barnes_lynch_2022, title={Designing a Dashboard for Student Teamwork Analysis}, DOI={10.1145/3478431.3499377}, abstractNote={Classroom dashboards are designed to help instructors effectively orchestrate classrooms by providing summary statistics, activity tracking, and other information. Existing dashboards are generally specific to an LMS or platform and they generally summarize individual work, not group behaviors. However, CS courses typically involve constellations of tools and mix on- and offline collaboration. Thus, cross-platform monitoring of individuals and teams is important to develop a full picture of the class. In this work, we describe our work on Concert, a data integration platform that collects data about student activities from several sources such as Piazza, My Digital Hand, and GitHub and uses it to support classroom monitoring through analysis and visualizations. We discuss team visualizations that we have developed to support effective group management and to help instructors identify teams in need of intervention.}, journal={PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1}, author={Gitinabard, Niki and Heckman, Sarah and Barnes, Tiffany and Lynch, Collin}, year={2022}, pages={446–452} }
 @article{gitinabard_xu_heckman_barnes_lynch_2019, title={How Widely Can Prediction Models Be Generalized? Performance Prediction in Blended Courses}, volume={12}, ISSN={["1939-1382"]}, url={https://doi.org/10.1109/TLT.2019.2911832}, DOI={10.1109/TLT.2019.2911832}, abstractNote={Blended courses that mix in-person instruction with online platforms are increasingly common in secondary education. These platforms record a rich amount of data on students’ study habits and social interactions. Prior research has shown that these metrics are correlated with students performance in face-to-face classes. However, predictive models for blended courses are still limited and have not yet succeeded at early prediction or cross-class predictions, even for repeated offerings of the same course. In this paper, we use data from two offerings of two different undergraduate courses to train and evaluate predictive models of student performance based on persistent student characteristics including study habits and social interactions. We analyze the performance of these models on the same offering, on different offerings of the same course, and across courses to see how well they generalize. We also evaluate the models on different segments of the courses to determine how early reliable predictions can be made. This paper tells us in part how much data is required to make robust predictions and how cross-class data may be used, or not, to boost model performance. The results of this study will help us better understand how similar the study habits, social activities, and the teamwork styles are across semesters for students in each performance category. These trained models also provide an avenue to improve our existing support platforms to better support struggling students early in the semester with the goal of providing timely intervention.}, number={2}, journal={IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Gitinabard, Niki and Xu, Yiqiao and Heckman, Sarah and Barnes, Tiffany and Lynch, Collin F.}, year={2019}, pages={184–197} }