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

Topics: Applications: Fuzzy Systems in Robotics, Fuzzy Image, Speech and Signal Processing, Vision and Multimedia, Pattern Recognition, Financial and Medical Applications, Fuzzy Information Retrieval and Data Mining, Big Data and Cloud Computing, Industrial and Real World Applications, System Identification and Fault Detection, Natural Language Processing, Security Systems; Applications: Games and Entertainment Technologies, Evolutionary Robotics, Evolutionary Art and Design, Industrial and Real World applications, Computational Economics and Finance; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World applications, Financial Applications, Neural Prostheses and Medical Applications, Neural based Data Mining and Complex Information Processing, Neural Network Software and Applications, Applications of Deep Neural networks, Robotics and Control Applications; Learning Paradigms and Algorithms; Support Vector Machines and Kernel Methods

Authors: Mario Garcia Valdez 1 ; Amaury Hernandez Aguila 1 ; Juan-J. Merelo 2 and Alejandra Mancilla Soto 1

Affiliations: 1 Instituto Tecnológico de Tijuana, Mexico ; 2 University of Granada, Spain

ISBN: 978-989-758-274-5

Keyword(s): Affective Computing, Neural Networks, Learning Analytics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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 assignment s, 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. (More)

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Paper citation in several formats:
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

@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},
}

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

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