Enhancing Student Engagement via Reduction of Frustration with Programming Assignments using Machine Learning

Mario Garcia Valdez, Amaury Hernandez Aguila, Juan-J. Merelo, Alejandra Mancilla Soto

2017

Abstract

Learning to program is often regarded as a difficult task. When selecting an appropriate programming exercise, experienced instructors gauge a student´s affective state and skills to then assign an activity with the appropriate level of difficulty. This work is focused on the prediction of the affective states of programmers with different levels of expertise when learning a new programming language. For this, an interactive webbased programming platform is proposed. The platform is designed to collect data from the studentsínteraction for data analysis. Current work is focused on the prediction of affective states using non-obtrusive sensors. Specifically, the aim of this research is to evaluate the use of keyboard and mouse dynamics as an appropriate sensory input for an affective recognition system. The proposed method uses feature vectors obtained by mining data generated from both keyboard and mouse dynamics of students as they work in basic Python programming assignments, which were used to train different classification algorithms to classify learners into five different affective states: boredom, frustration, distraction, relaxation and engagement. Accuracy achieved was around 75% with J48 obtaining the best results, proving that data gathered from non-obtrusive sensors can successfully be used as another input to classification models in order to predict an individual´s affective states.

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Paper Citation


in Harvard Style

Garcia Valdez M., Hernandez Aguila A., Merelo J. and Mancilla Soto A. (2017). Enhancing Student Engagement via Reduction of Frustration with Programming Assignments using Machine Learning.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 297-304. DOI: 10.5220/0006502102970304


in Bibtex Style

@conference{ijcci17,
author={Mario Garcia Valdez and Amaury Hernandez Aguila and Juan-J. Merelo and Alejandra Mancilla Soto},
title={Enhancing Student Engagement via Reduction of Frustration with Programming Assignments using Machine Learning},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={297-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006502102970304},
isbn={978-989-758-274-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Enhancing Student Engagement via Reduction of Frustration with Programming Assignments using Machine Learning
SN - 978-989-758-274-5
AU - Garcia Valdez M.
AU - Hernandez Aguila A.
AU - Merelo J.
AU - Mancilla Soto A.
PY - 2017
SP - 297
EP - 304
DO - 10.5220/0006502102970304