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Predicting Student Performance Using Machine Learning in fNIRS Data

Increasing student involvement in classes has always been a challenge for teachers and school managers. In online learning, some interactivity mechanisms like quizzes are increasingly used to engage students during classes and tasks. However, there is a high demand for tools that evaluate the effici...

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Autores principales: Oku, Amanda Yumi Ambriola, Sato, João Ricardo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892769/
https://www.ncbi.nlm.nih.gov/pubmed/33613215
http://dx.doi.org/10.3389/fnhum.2021.622224
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author Oku, Amanda Yumi Ambriola
Sato, João Ricardo
author_facet Oku, Amanda Yumi Ambriola
Sato, João Ricardo
author_sort Oku, Amanda Yumi Ambriola
collection PubMed
description Increasing student involvement in classes has always been a challenge for teachers and school managers. In online learning, some interactivity mechanisms like quizzes are increasingly used to engage students during classes and tasks. However, there is a high demand for tools that evaluate the efficiency of these mechanisms. In order to distinguish between high and low levels of engagement in tasks, it is possible to monitor brain activity through functional near-infrared spectroscopy (fNIRS). The main advantages of this technique are portability, low cost, and a comfortable way for students to concentrate and perform their tasks. This setup provides more natural conditions for the experiments if compared to the other acquisition tools. In this study, we investigated levels of task involvement through the identification of correct and wrong answers of typical quizzes used in virtual environments. We collected data from the prefrontal cortex region (PFC) of 18 students while watching a video lecture. This data was modeled with supervised learning algorithms. We used random forests and penalized logistic regression to classify correct answers as a function of oxyhemoglobin and deoxyhemoglobin concentration. These models identify which regions best predict student performance. The random forest and penalized logistic regression (GLMNET with LASSO) obtained, respectively, 0.67 and 0.65 area of the ROC curve. Both models indicate that channels F4-F6 and AF3-AFz are the most relevant for the prediction. The statistical significance of these models was confirmed through cross-validation (leave-one-subject-out) and a permutation test. This methodology can be useful to better understand the teaching and learning processes in a video lecture and also provide improvements in the methodologies used in order to better adapt the presentation content.
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spelling pubmed-78927692021-02-20 Predicting Student Performance Using Machine Learning in fNIRS Data Oku, Amanda Yumi Ambriola Sato, João Ricardo Front Hum Neurosci Human Neuroscience Increasing student involvement in classes has always been a challenge for teachers and school managers. In online learning, some interactivity mechanisms like quizzes are increasingly used to engage students during classes and tasks. However, there is a high demand for tools that evaluate the efficiency of these mechanisms. In order to distinguish between high and low levels of engagement in tasks, it is possible to monitor brain activity through functional near-infrared spectroscopy (fNIRS). The main advantages of this technique are portability, low cost, and a comfortable way for students to concentrate and perform their tasks. This setup provides more natural conditions for the experiments if compared to the other acquisition tools. In this study, we investigated levels of task involvement through the identification of correct and wrong answers of typical quizzes used in virtual environments. We collected data from the prefrontal cortex region (PFC) of 18 students while watching a video lecture. This data was modeled with supervised learning algorithms. We used random forests and penalized logistic regression to classify correct answers as a function of oxyhemoglobin and deoxyhemoglobin concentration. These models identify which regions best predict student performance. The random forest and penalized logistic regression (GLMNET with LASSO) obtained, respectively, 0.67 and 0.65 area of the ROC curve. Both models indicate that channels F4-F6 and AF3-AFz are the most relevant for the prediction. The statistical significance of these models was confirmed through cross-validation (leave-one-subject-out) and a permutation test. This methodology can be useful to better understand the teaching and learning processes in a video lecture and also provide improvements in the methodologies used in order to better adapt the presentation content. Frontiers Media S.A. 2021-02-05 /pmc/articles/PMC7892769/ /pubmed/33613215 http://dx.doi.org/10.3389/fnhum.2021.622224 Text en Copyright © 2021 Oku and Sato. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Human Neuroscience
Oku, Amanda Yumi Ambriola
Sato, João Ricardo
Predicting Student Performance Using Machine Learning in fNIRS Data
title Predicting Student Performance Using Machine Learning in fNIRS Data
title_full Predicting Student Performance Using Machine Learning in fNIRS Data
title_fullStr Predicting Student Performance Using Machine Learning in fNIRS Data
title_full_unstemmed Predicting Student Performance Using Machine Learning in fNIRS Data
title_short Predicting Student Performance Using Machine Learning in fNIRS Data
title_sort predicting student performance using machine learning in fnirs data
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892769/
https://www.ncbi.nlm.nih.gov/pubmed/33613215
http://dx.doi.org/10.3389/fnhum.2021.622224
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