Paulo Victor Borges Oliveira Lima
ABSTRACT
Problem-based learning is a methodology seen repeatedly in the literature for teaching algorithms and data structures. There are numerous online judge platforms available, where the student solves computational problems and their codes are automatically corrected by comparing whether the output response matches the expected feedback for that problem. Despite its popularity, there are few works that associate the use of emotion detection techniques as a diagnostic tool to assess the quality of these problems, leading to a more specific analysis of the successes, frustrations and joys of students in this task. This article presents a technique that defines a timeline describing the step-by-step of a student when solving a computational problem, creating notes about the instants of time in which feelings were perceived in all these stages, from reading the problem, to coding and sending the judge’s response online. In this context, a contribution of this article is the development of an API for detecting emotions in facial expressions, using Deep Learning techniques and convolutional neural networks. The API is accessible and scalable via HTTP endpoints and allows for easy integration into third-party applications. The first results show that real-time emotion detection can be a valuable tool to improve the user experience on online judge platforms. The technique makes it possible to stratify the diagnosis of problems, analyzing whether there is a presence or predominance of undesirable feelings in any of the stages, be it reading, coding or submission of the solution.
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Index Terms
- Automatic Emotion Detection in the Learning of Algorithms
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