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Human Expert Labeling Process: Valence-Arousal Labeling for Students’ Affective States

  • Sinem Aslan
  • Eda Okur
  • Nese AlyuzEmail author
  • Asli Arslan Esme
  • Ryan S. Baker
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 804)

Abstract

Affect has emerged as an important part of the interaction between learners and computers, with important implications for learning outcomes. As a result, it has emerged as an important area of research within learning analytics. Reliable and valid data labeling is a key tenet for training machine learning models providing such analytics. In this study, using Human Expert Labeling Process (HELP) as a baseline labeling protocol, we investigated an optimized method through several experiments for labeling student affect based on Circumplex Model of Emotion (Valence-Arousal). Using the optimized method, we then had the human experts label a larger quantity of student data so that we could test and validate this method on a relatively larger and different dataset. The results showed that using the optimized method, the experts were able to achieve an acceptable consensus in labeling outcomes as aligned with affect labeling literature.

Keywords

Affective state labeling Circumplex Model of Emotion Inter-rater agreement Intelligent tutoring systems Affective computing 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sinem Aslan
    • 1
  • Eda Okur
    • 1
  • Nese Alyuz
    • 1
    Email author
  • Asli Arslan Esme
    • 1
  • Ryan S. Baker
    • 2
  1. 1.Intel CorporationHillsboroUSA
  2. 2.University of PennsylvaniaPhiladelphiaUSA

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