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Analysis of Semestral Progress in Higher Technical Education with HMM Models

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12744))

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Abstract

Supporting educational processes with Hidden Markov Models (HMMs) has great potential. In this paper, we explore the possibility of identifying students’ learning progress with HMMs. Students’ grades are used to train the HMMs to find out if the analysis of obtained models lets us detect patterns emerging from student’s results. We also try to predict the final students’ results on the basis of their partial grades. A new, classification approach for this problem, using properties of HMMs is proposed: High and Low State Model (HLSM).

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Acknowledgment

This publication was supported by the Department of Graphics, Computer Vision and Digital Systems, under statue research project (Rau6, 2021), Silesian University of Technology (Gliwice, Poland). The authors would like to thank for involvement in data collection especially to Prof. J. Polańska, Prof. S. Deorowicz, Prof. K. Simiński, Dr. F. Binczyk, Dr. A. Papież and Dr. J. Żyła.

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Authors and Affiliations

  1. Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Ewa Lach, Damian Grzechca, Andrzej Polański, Jerzy Rutkowski & Michał Staniszewski

Authors
  1. Ewa Lach
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  2. Damian Grzechca
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  3. Andrzej Polański
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  4. Jerzy Rutkowski
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  5. Michał Staniszewski
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Corresponding author

Correspondence to Ewa Lach .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Lach, E., Grzechca, D., Polański, A., Rutkowski, J., Staniszewski, M. (2021). Analysis of Semestral Progress in Higher Technical Education with HMM Models. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12744. Springer, Cham. https://doi.org/10.1007/978-3-030-77967-2_18

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  • DOI: https://doi.org/10.1007/978-3-030-77967-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77966-5

  • Online ISBN: 978-3-030-77967-2

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