EdCode: Towards Personalized Support at Scale for Remote Assistance in CS Education
Yan Chen, Jaylin Herskovitz, Gabriel Matute, April Yi Wang, Sang Won Lee, Walter S. Lasecki, Steve Oney
University of Michigan Virginia Tech
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摘要与影响
Programming support methods, like discussion fo-rums and office hours, are important in CS education, but difficult to scale. In this paper, we introduce EdCode, a system that allows students to seek remote instructional support within their IDE in a way that resembles in-person support. It also allows instructors to provide contextualized responses by referencing students' code, and curate and publish their answers for an entire class by selecting only the relevant part of the code referenced, thereby helping to avoid plagiarism. We evaluated EdCode with a series of usability studies and identified benefits and challenges for its use in programming courses. Students found that the perceived quality of support from EdCode was comparable to that of support from in-person office hours, and both students and instructors found publishing and viewing other students' answers helpful.
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计算机 / AIOnline Learning and Analytics
Innovative Teaching and Learning Methods · Teaching and Learning Programming
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