Multimodality in online education: a comparative study

The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism...

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Vydané v:	Multimedia tools and applications Ročník 84; číslo 28; s. 33685 - 33718
Hlavní autori:	Immadisetty, Praneeta, Rajesh, Pooja, Gupta, Akshita, M. R., Dr. Anala, A., Dr. Soumya, Subramanya, Dr. K. N.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.08.2025 Springer Nature B.V
Predmet:	Classrooms Comparative studies Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Education Emotions Eye movements Feedback Learning Machine learning Multimedia Information Systems Online instruction Recognition Special Purpose and Application-Based Systems Students Support vector machines Teachers Track 5: Multimedia and Education Verbal communication Videoconferencing Decision-level fusion Non verbal cues Custom dataset Affect recognition Multimodal architecture
ISSN:	1573-7721, 1380-7501, 1573-7721
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Abstract	The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism to assess whether instructors can grasp the extent to which students understand the subject and their response to the educational stimuli. The current systems consider only a single cue with a lack of focus in the educational domain. Thus, there is a necessity for the measurement of an all-encompassing holistic overview of the students’ reaction to the subject matter. This paper highlights the need for a multimodal approach to affect recognition and its deployment in the online classroom while considering four cues, posture and gesture, facial, eye tracking and verbal recognition. In this paper, various machine learning models suitable for each cue are analyzed based on key metrics such as accuracy, ease of data collection, sensitivity, and limitations. The comparison of models revealed the most effective techniques for each cue. A multimodal approach derived from weighted majority voting is proposed by combining the top-performing models for each aspect of student engagement in online learning environments. This paper proposes a novel approach that integrates multiple modalities for affect recognition, aiming to improve the accuracy of student response assessments.
AbstractList	The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world. With a surge in the usage of online video conferencing platforms and tools to better gauge student understanding, there needs to be a mechanism to assess whether instructors can grasp the extent to which students understand the subject and their response to the educational stimuli. The current systems consider only a single cue with a lack of focus in the educational domain. Thus, there is a necessity for the measurement of an all-encompassing holistic overview of the students’ reaction to the subject matter. This paper highlights the need for a multimodal approach to affect recognition and its deployment in the online classroom while considering four cues, posture and gesture, facial, eye tracking and verbal recognition. In this paper, various machine learning models suitable for each cue are analyzed based on key metrics such as accuracy, ease of data collection, sensitivity, and limitations. The comparison of models revealed the most effective techniques for each cue. A multimodal approach derived from weighted majority voting is proposed by combining the top-performing models for each aspect of student engagement in online learning environments. This paper proposes a novel approach that integrates multiple modalities for affect recognition, aiming to improve the accuracy of student response assessments.
Author	Rajesh, Pooja M. R., Dr. Anala Subramanya, Dr. K. N. A., Dr. Soumya Immadisetty, Praneeta Gupta, Akshita
Author_xml	– sequence: 1 givenname: Praneeta orcidid: 0009-0004-8025-7777 surname: Immadisetty fullname: Immadisetty, Praneeta email: praneeta16.i@gmail.com organization: Computer Science and Engineering, R V College of Engineering – sequence: 2 givenname: Pooja surname: Rajesh fullname: Rajesh, Pooja organization: Computer Science and Engineering, R V College of Engineering – sequence: 3 givenname: Akshita surname: Gupta fullname: Gupta, Akshita organization: Computer Science and Engineering, R V College of Engineering – sequence: 4 givenname: Dr. Anala surname: M. R. fullname: M. R., Dr. Anala organization: Information Science and Engineering, R V College of Engineering – sequence: 5 givenname: Dr. Soumya surname: A. fullname: A., Dr. Soumya organization: Computer Science and Engineering, R V College of Engineering – sequence: 6 givenname: Dr. K. N. surname: Subramanya fullname: Subramanya, Dr. K. N. organization: Principal and Professor, R V College of Engineering
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Snippet	The commencement of the decade brought along with it a grave pandemic and in response the movement of education forums predominantly into the online world....
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SubjectTerms	Classrooms Comparative studies Computer Communication Networks Computer Science Data Structures and Information Theory Datasets Education Emotions Eye movements Feedback Learning Machine learning Multimedia Information Systems Online instruction Recognition Special Purpose and Application-Based Systems Students Support vector machines Teachers Track 5: Multimedia and Education Verbal communication Videoconferencing
Title	Multimodality in online education: a comparative study
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