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Avoiding Bias in Students’ Intrinsic Motivation Detection

  • Pedro Bispo SantosEmail author
  • Caroline Verena Bhowmik
  • Iryna Gurevych
Conference paper
  • 113 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12149)

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).

Keywords

Affective computing Fairness in AI Behavioral analytics Facial expressions Intrinsic motivation Nonverbal signals Educational psychology 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pedro Bispo Santos
    • 1
    Email author
  • Caroline Verena Bhowmik
    • 2
  • Iryna Gurevych
    • 1
  1. 1.Ubiquitous Knowledge Processing (UKP) LabDarmstadtGermany
  2. 2.Department of PsychologyUniversity of Koblenz-LandauKoblenzGermany

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