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Mining of Keystroke and Mouse Dynamics to Increase the Engagement of Students with Programming Assignments

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Computational Intelligence (IJCCI 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 829))

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Abstract

The aim of the experiments described in this paper is to evaluate the use of keyboard and mouse dynamics as an appropriate non-obtrusive sensory input for an system that is sensitive to the affective state of its user. Our motivation for starting this research line has been the lack of tools and methodologies for taking into account this affective state in learning environments. In an ideal situation, when instructors have to choose from a collection of programming assignments, they should consider the studentś affective state and skills in order to select a learning resource with the appropriate level of difficulty. However, neither the data or the ability to process it are present in current learning management systems. This work tries to address this problem, by focused on the capture and pre-processing of data that is going to be fed to several machine learning techniques with the objective of classifying the affective states of users with different levels of expertise when learning a new programming language. We capture student data from a web-based platform, a learning management system where students interact with programming exercises. We introduce in this paper a series of pre-processing techniques that are able to convert data from keyboard and mouse dynamics captured from students as they were coding basic Python programming assignments into feature vector, that have been later used for the classification into five affective states: boredom, frustration, distraction, relaxation and engagement. The following classification algorithms have been evaluated: k-nearest neighbors,feed forward neural networks, naïve Bayes classifier, J-48 tree induction algorithm, deep learning, random forest, gradient boosted trees and naïve Bayes Kernel). The best accuracy was around 78% and was achieved by the tree induction algorithms. Results show that data gathered from ready-available, non-obtrusive sensors can be used successfully as another input to hybrid classification models in order to predict an individualś affective states, and these in turn can be used to make students more engaged and thus learn more efficiently.

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Acknowledgements

This work has been supported in part by: de Ministerio español de Economía y Competitividad under project TIN2014-56494-C4-3-P (UGR-EPHEMECH), DeepBio (TIN2017-85727-C4-2-P) and by CONACYT PEI Project No. 220590.

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Authors and Affiliations

  1. Department of Graduate Studies, Instituto Tecnologico de Tijuana, Tijuana, Mexico

    Mario Garcia Valdez, Amaury Hernandez Aguila & Alejandra Mancilla Soto

  2. Geneura Team and CITIC, University of Granada, Granada, Spain

    Juan-J. Merelo

Authors
  1. Mario Garcia Valdez
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  2. Juan-J. Merelo
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  3. Amaury Hernandez Aguila
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  4. Alejandra Mancilla Soto
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Corresponding author

Correspondence to Mario Garcia Valdez .

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Editors and Affiliations

  1. IUT Sénart, Université Paris-Est Créteil (UPEC), Créteil, France

    Christophe Sabourin

  2. Department of Computer Architecture and Technology, University of Granada, Granada, Spain

    Juan Julian Merelo

  3. Université Paris-Est Créteil (UPEC), Créteil, France

    Kurosh Madani

  4. University of Reading, Reading, UK

    Kevin Warwick

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Valdez, M.G., Merelo, JJ., Aguila, A.H., Soto, A.M. (2019). Mining of Keystroke and Mouse Dynamics to Increase the Engagement of Students with Programming Assignments. In: Sabourin, C., Merelo, J.J., Madani, K., Warwick, K. (eds) Computational Intelligence. IJCCI 2017. Studies in Computational Intelligence, vol 829. Springer, Cham. https://doi.org/10.1007/978-3-030-16469-0_3

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