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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Avoiding Bias in Students’ Intrinsic Motivation Detection
verfasst von : Pedro Bispo Santos, Caroline Verena Bhowmik, Iryna Gurevych
Erschienen in: Intelligent Tutoring Systems
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Abstract
Intrinsic motivation is the psychological construct that defines our reasons and interests to perform a set of actions. It has shown to be associated with positive outcomes across domains, especially in the academic context. Therefore, understanding and identifying peoples’ levels of intrinsic motivation can be crucial for professionals of many domains, e.g. teachers aiming to offer better support to students’ learning processes and enhance their academic outcomes. In a first attempt to tackle this issue, we propose an end-to-end approach for recognition of intrinsic motivation, using only facial expressions as input. Our results show that visual cues from students’ facial expressions are an important source of information to detect their levels of intrinsic motivation (AUC \(=0.570\), \(F_1=0.556\)). We also show how to avoid potential bias that might be present in datasets. When dividing the training samples per gender, we achieved a substantial improvement for both genders (AUC \(=0.739\) and \(F_1=0.852\) for male students, AUC \(=0.721\) and \(F_1=0.723\) for female students).