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Analysis and Prediction of Student Emotions While Doing Programming Exercises

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 11528)

Abstract

The modeling of student emotions has recently considerable interest in the field of intelligent tutoring systems. However, most approaches are applied in typical interaction models characterized by frequent communication or dialogue between the student and the tutoring model. In this paper, we analyze emotions while students are writing computer programs without any human or agent communication to induce displays of affect. We use a combination of features derived from typing logs, compilation logs, and a video of the students’ face while solving coding exercises and determine how they can be used to predict affect. We find that combining pose-based, face-based, and log-based features can train models that predict affect with good accuracy above chance levels and that certain features are discriminative in this task.

Keywords

  • Student modeling
  • Affective computing
  • Programming

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Notes

  1. 1.

    All students gave us the permission to publish their faces in academic publications.

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Acknowledgments

The authors would like to thank Mr. Fritz Flores, Mr. Manuel Toleran, and Mr. Kayle Tiu for assisting in the facilitation of the data collection process in the Philippines.

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Correspondence to Thomas James Tiam-Lee .

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Tiam-Lee, T.J., Sumi, K. (2019). Analysis and Prediction of Student Emotions While Doing Programming Exercises. In: Coy, A., Hayashi, Y., Chang, M. (eds) Intelligent Tutoring Systems. ITS 2019. Lecture Notes in Computer Science(), vol 11528. Springer, Cham. https://doi.org/10.1007/978-3-030-22244-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-22244-4_4

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