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https://repository.isls.org//handle/1/633| Title: | Peer Tutor Matching for Introductory Programming: Data-Driven Methods to Enable New Opportunities for Help |
| Authors: | Diana, Nicholas Eagle, Michael Stamper, John Grover, Shuchi Bienkowski, Marie Basu, Satabdi |
| Issue Date: | Jul-2018 |
| Publisher: | International Society of the Learning Sciences, Inc. [ISLS]. |
| Citation: | Diana, N., Eagle, M., Stamper, J., Grover, S., Bienkowski, M., & Basu, S. (2018). Peer Tutor Matching for Introductory Programming: Data-Driven Methods to Enable New Opportunities for Help. In Kay, J. and Luckin, R. (Eds.) Rethinking Learning in the Digital Age: Making the Learning Sciences Count, 13th International Conference of the Learning Sciences (ICLS) 2018, Volume 3. London, UK: International Society of the Learning Sciences. |
| Abstract: | The number of students that can be helped in a given class period is limited by the time constraints of the class and the number of agents available for providing help. We use a classroom-replay of previously collected data to evaluate a data-driven method for increasing the number of students that can be helped. We use a machine learning model to identify students who need help in real-time, and an interaction network to group students who need similar help together using approach maps. By assigning these groups of struggling students to peer tutors (as well the instructor), we were able to more than double the number of students helped. |
| URI: | https://doi.dx.org/10.22318/cscl2018.1377 https://repository.isls.org//handle/1/633 |
| Appears in Collections: | ICLS 2018 |
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