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EPIA Conference on Artificial Intelligence

EPIA 2019: Progress in Artificial Intelligence pp 27–34Cite as

Student Attention Evaluation System Using Machine Learning for Decision Making

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Abstract

The student attention evaluation is a very important feature that has a high influence on student results, so it’s necessary to consider. This paper focuses on the evaluation of the student attention level using a machine learning categorization model using several machine learning techniques. The Support Vector Machine, Nearest Neighbor, Naive Bayes, Neural Networks and Random Forest algorithms are applied to model an intelligent system which will evaluate the attention level of the students. Thirteen important features such as alltime, percApp (time in the application), age, grade, behavior biometrics of keyboard (kdt - key down time, tbk – time between keys) and behavior biometrics of mouse (cd - click duration, mv – mouse velocity, ma – mouse acceleration, ddc – duration distance clicks, dplbc – distance point to line between clicks, dbc – distance between clicks, and tbc – time between clicks) are taken for training and testing. Above mentioned machine learning techniques are compared in terms of accuracy rate.

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Acknowledge

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019.

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Authors and Affiliations

  1. CIICESI, ESTG, Polytechnic Institute of Porto, Felgueiras, Portugal

    Dalila Durães

  2. Algoritmi Research Centre/Department of Informatics, University of Minho, Braga, Portugal

    Dalila Durães

Authors
  1. Dalila Durães
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Correspondence to Dalila Durães .

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Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

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Durães, D. (2019). Student Attention Evaluation System Using Machine Learning for Decision Making. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-30241-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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