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A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States

Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affecti...

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Detalles Bibliográficos
Autores principales: Hasnine, Mohammad Nehal, Nguyen, Ho Tan, Tran, Thuy Thi Thu, Bui, Huyen T. T., Akçapınar, Gökhan, Ueda, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181135/
https://www.ncbi.nlm.nih.gov/pubmed/37177447
http://dx.doi.org/10.3390/s23094243
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author Hasnine, Mohammad Nehal
Nguyen, Ho Tan
Tran, Thuy Thi Thu
Bui, Huyen T. T.
Akçapınar, Gökhan
Ueda, Hiroshi
author_facet Hasnine, Mohammad Nehal
Nguyen, Ho Tan
Tran, Thuy Thi Thu
Bui, Huyen T. T.
Akçapınar, Gökhan
Ueda, Hiroshi
author_sort Hasnine, Mohammad Nehal
collection PubMed
description Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment.
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spelling pubmed-101811352023-05-13 A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States Hasnine, Mohammad Nehal Nguyen, Ho Tan Tran, Thuy Thi Thu Bui, Huyen T. T. Akçapınar, Gökhan Ueda, Hiroshi Sensors (Basel) Article Students’ affective states describe their engagement, concentration, attitude, motivation, happiness, sadness, frustration, off-task behavior, and confusion level in learning. In online learning, students’ affective states are determinative of the learning quality. However, measuring various affective states and what influences them is exceedingly challenging for the lecturer without having real interaction with the students. Existing studies primarily use self-reported data to understand students’ affective states, while this paper presents a novel learning analytics system called MOEMO (Motion and Emotion) that could measure online learners’ affective states of engagement and concentration using emotion data. Therefore, the novelty of this research is to visualize online learners’ affective states on lecturers’ screens in real-time using an automated emotion detection process. In real-time and offline, the system extracts emotion data by analyzing facial features from the lecture videos captured by the typical built-in web camera of a laptop computer. The system determines online learners’ five types of engagement (“strong engagement”, “high engagement”, “medium engagement”, “low engagement”, and “disengagement”) and two types of concentration levels (“focused” and “distracted”). Furthermore, the dashboard is designed to provide insight into students’ emotional states, the clusters of engaged and disengaged students’, assistance with intervention, create an after-class summary report, and configure the automation parameters to adapt to the study environment. MDPI 2023-04-24 /pmc/articles/PMC10181135/ /pubmed/37177447 http://dx.doi.org/10.3390/s23094243 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hasnine, Mohammad Nehal
Nguyen, Ho Tan
Tran, Thuy Thi Thu
Bui, Huyen T. T.
Akçapınar, Gökhan
Ueda, Hiroshi
A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States
title A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States
title_full A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States
title_fullStr A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States
title_full_unstemmed A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States
title_short A Real-Time Learning Analytics Dashboard for Automatic Detection of Online Learners’ Affective States
title_sort real-time learning analytics dashboard for automatic detection of online learners’ affective states
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181135/
https://www.ncbi.nlm.nih.gov/pubmed/37177447
http://dx.doi.org/10.3390/s23094243
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