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Emotion Trajectory and Student Performance in Engineering Education: A Preliminary Study

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Research Challenges in Information Science (RCIS 2024)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 513))

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Abstract

In this study, we aim to establish the connection between the emotional trajectory of students during a pedagogical sequence and their performances. The project aims to develop an affective and intelligent tutoring system for detecting students facing difficulties and helping them. We designed this experimentation during the 2022–2023 academic year with students in a French engineering school. We collected and analyzed two primary data sources: student results from the Learning Management System (LMS) and images captured by students’ webcams during their learning activities.

It is known that basic (primary) emotions (like fear or disgust) do not reflect student affective states when facing pedagogical issues (like misunderstanding or proudness). Since such “academic emotions” are not easy to define and detect, we changed the paradigm and used a 2D dimensional model that describes better the wide spectrum of emotion encountered. Moreover, it allows to build a temporal emotion trajectory reflecting the student’s emotional trajectory.

Firstly, we observed a correlation between these trajectories and academic results. Secondly, we found that high-performing student trajectories are significantly different from the others. These preliminary results, support the idea that emotions are pivotal in distinguishing highly performing students from their less successful counterparts. This is the first step to assess students’ profiles and proactively identify those at risk of failure in a human learning context.

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Acknowledgments

The authors express their sincere gratitude to Mr. Loïc ROUSSEL, Managing Director of ESIEA, Mr. Jérôme DA RUGNA, Director of Pedagogy and Research, Mr. Jean-Pierre AUBIN, lecturer at ESIEA, as well as to the ESIEA students who generously participated in this study. Their valuable contribution made it possible to collect the raw educational data resulting from students' interactions with the Moodle platform.

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Authors and Affiliations

  1. Learning, Data and Robotics (LDR) Lab, ESIEA, Paris, France

    Edouard Nadaud, Antoun Yaacoub, Siba Haidar & Lionel Prevost

  2. Centre de Recherche en Informatique (CRI), Université Paris 1 Panthéon Sorbonne, Paris, France

    Bénédicte Le Grand

Authors
  1. Edouard Nadaud
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  2. Antoun Yaacoub
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  3. Siba Haidar
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  4. Bénédicte Le Grand
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  5. Lionel Prevost
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Corresponding author

Correspondence to Edouard Nadaud .

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Editors and Affiliations

  1. NOVA University Lisbon, Caparica, Portugal

    João Araújo

  2. University of Castilla La Mancha, Albacete, Albacete, Spain

    Jose Luis de la Vara

  3. University of Minho, Guimarães, Portugal

    Maribel Yasmina Santos

  4. Institut Mines-Télécom Business School, Evry, France

    Saïd Assar

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Nadaud, E., Yaacoub, A., Haidar, S., Le Grand, B., Prevost, L. (2024). Emotion Trajectory and Student Performance in Engineering Education: A Preliminary Study. In: Araújo, J., de la Vara, J.L., Santos, M.Y., Assar, S. (eds) Research Challenges in Information Science. RCIS 2024. Lecture Notes in Business Information Processing, vol 513. Springer, Cham. https://doi.org/10.1007/978-3-031-59465-6_25

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  • DOI: https://doi.org/10.1007/978-3-031-59465-6_25

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  • Publisher Name: Springer, Cham

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