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Towards Better Affect Detectors: Detecting Changes Rather Than States

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Artificial Intelligence in Education (AIED 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10948))

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Abstract

Affect detection in educational systems has a promising future to help develop intervention strategies for improving student engagement. To improve the scalability, sensor-free affect detection that assesses students’ affective states solely based on the interaction data between students and computer-based learning platforms has gained more and more attention. In this paper, we present our efforts to build our affect detectors to assess the affect changes instead of affect states. First, we developed an affect-change model to represent the transitions between the four affect states; boredom, frustration, confusion and engagement concentration with ASSISTments dataset. We then reorganized and relabeled the dataset to develop the affect-change detector. The data science platform (e.g., RapidMiner) was adopted to train and evaluate the detectors. The result showed significant improvements over previously reported models.

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Authors and Affiliations

  1. University of Maryland, Baltimore County, Baltimore, MD, 21050, USA

    Varun Mandalapu & Jiaqi Gong

Authors
  1. Varun Mandalapu
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  2. Jiaqi Gong
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Corresponding author

Correspondence to Jiaqi Gong .

Editor information

Editors and Affiliations

  1. Carnegie Mellon University, Pittsburgh, PA, USA

    Carolyn Penstein Rosé

  2. University of Technology, Sydney, NSW, Australia

    Roberto MartĂ­nez-Maldonado

  3. University of Duisburg-Essen , Duisburg, Germany

    H. Ulrich Hoppe

  4. UCL Institute of Education, London, UK

    Rose Luckin

  5. UCL Institute of Education, London, UK

    Manolis Mavrikis

  6. UCL Institute of Education, London, UK

    Kaska Porayska-Pomsta

  7. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  8. University of Sussex, Brighton, UK

    Benedict du Boulay

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Mandalapu, V., Gong, J. (2018). Towards Better Affect Detectors: Detecting Changes Rather Than States. In: Penstein Rosé, C., et al. Artificial Intelligence in Education. AIED 2018. Lecture Notes in Computer Science(), vol 10948. Springer, Cham. https://doi.org/10.1007/978-3-319-93846-2_36

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  • DOI: https://doi.org/10.1007/978-3-319-93846-2_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93845-5

  • Online ISBN: 978-3-319-93846-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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