EdCode: Towards Personalized Support at Scale for Remote Assistance in CS Education

Yan Chen, Gabriel Matute, April Wang, Jaylin Herskovitz, Sang Won Lee, Walter Lasecki, and Steve Oney

Programming support mechanisms, such as online discussion forums and in-person office hours, are important in CS education. However, it is challenging to provide personalized feedback at scale using these methods; online forums lack personalized assistance, and in-person support at office hours scales poorly. To address these challenges, we introduce EdCode, a remote support system that allows students to remotely interact with instructors to seek personalized assistance and allows instructors to scale their answers. We accomplish this by enabling students to communicate with instructors within their working context (through their IDE), and instructors to compose their answers using hypertext that references students’ code. These features help instructors provide personalized assistance remotely, in a way that resembles in-person support. In addition, instructors can curate and publish their answers for an entire class by selecting only the relevant part of the code referenced, thereby precluding plagiarism. We evaluated our approach with a series of usability studies in three different setups: instructor- focused, student-focused, and an end-to-end study. We were able to confirm the need for and potential benefits of EdCode in programming courses. Students found that the perceived quality of support from EdCode was comparable to that of answers from in-person office hours, and both students and instructors found publishing and viewing other students’ answers helpful.

VL/HCC 2020

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