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Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms

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Artificial Intelligence in Education (AIED 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13355))

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Abstract

Flipped classroom (FC) courses, where students complete pre-class activities before attending interactive face-to-face sessions, are becoming increasingly popular. However, many students lack the skills, resources, or motivation to effectively engage in pre-class activities. Profiling students based on their pre-class behavior is therefore fundamental for teaching staff to make better-informed decisions on the course design and provide personalized feedback. Existing student profiling techniques have mainly focused on one specific aspect of learning behavior and have limited their analysis to one FC course. In this paper, we propose a multi-step clustering approach to model student profiles based on pre-class behavior in FC in a multi-dimensional manner, focusing on student effort, consistency, regularity, proactivity, control, and assessment. We first cluster students separately for each behavioral dimension. Then, we perform another level of clustering to obtain multi-dimensional profiles. Experiments on three different FC courses show that our approach can identify educationally-relevant profiles regardless of the course topic and structure. Moreover, we observe significant academic performance differences between the profiles.

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Notes

  1. 1.

    Grades range from 1 to 6, with 6 being the best and 4 being the passing grade.

  2. 2.

    Correcting for multiple comparisons via a Benjamini-Hochberg (BH) procedure.

  3. 3.

    Shapiro-Wilk test for FP: W = 0.97, \(p =\) 5.6e−05; and PC: W = 0.97, \(p =\) 3.1e−03. Kruskal-Wallis Test for FP: \({\chi }^2\)(4) = 21.8, \(p =\) 2.2e−04; PC: \({\chi }^2\)(3) = 13.4, \(p =\) 3.8e−03.

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Author information

Authors and Affiliations

  1. EPFL, Lausanne, Switzerland

    Paola Mejia-Domenzain, Christian Giang & Tanja Käser

  2. University of Cagliari, Cagliari, Italy

    Mirko Marras

  3. University of Applied Sciences and Arts of Southern Switzerland, Locarno, Switzerland

    Christian Giang

Authors
  1. Paola Mejia-Domenzain
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  2. Mirko Marras
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  3. Christian Giang
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  4. Tanja Käser
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Corresponding author

Correspondence to Paola Mejia-Domenzain .

Editor information

Editors and Affiliations

  1. Ateneo De Manila University, Quezon, Philippines

    Maria Mercedes Rodrigo

  2. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

    Noburu Matsuda

  3. Durham University, Durham, UK

    Alexandra I. Cristea

  4. University of Leeds, Leeds, UK

    Vania Dimitrova

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Mejia-Domenzain, P., Marras, M., Giang, C., Käser, T. (2022). Identifying and Comparing Multi-dimensional Student Profiles Across Flipped Classrooms. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. AIED 2022. Lecture Notes in Computer Science, vol 13355. Springer, Cham. https://doi.org/10.1007/978-3-031-11644-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-11644-5_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-11644-5

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