Learner engagement measurement and classification in 1:1 learning

Sinem Aslan; Zehra Cataltepe; Itai Diner; Onur Dundar; Asli A. Esme; Ron Ferens; Gila Kamhi; Ece Oktay; Canan Soysal; Murat Yener

doi:10.1109/icmla.2014.111

Learner engagement measurement and classification in 1:1 learning

Sinem Aslan, Zehra Cataltepe, Itai Diner, Onur Dundar, Asli A. Esme, Ron Ferens, Gila Kamhi, Ece Oktay, Canan Soysal, Murat Yener

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

22 Scopus citations

Abstract

We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.

Original language	English
Title of host publication	Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Editors	Cesar Ferri, Guangzhi Qu, Xue-wen Chen, M. Arif Wani, Plamen Angelov, Jian-Huang Lai
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	545-552
Number of pages	8
ISBN (Electronic)	9781479974153
DOIs	https://doi.org/10.1109/icmla.2014.111
State	Published - 5 Feb 2014
Externally published	Yes
Event	2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 - Detroit, United States Duration: 3 Dec 2014 → 6 Dec 2014

Publication series

Name	Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014

Conference

Conference	2014 13th International Conference on Machine Learning and Applications, ICMLA 2014
Country/Territory	United States
City	Detroit
Period	3/12/14 → 6/12/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

classification
engagement detection
feature selection
machine learning

Access to Document

10.1109/icmla.2014.111

Cite this

Aslan, S., Cataltepe, Z., Diner, I., Dundar, O., Esme, A. A., Ferens, R., Kamhi, G., Oktay, E., Soysal, C., & Yener, M. (2014). Learner engagement measurement and classification in 1:1 learning. In C. Ferri, G. Qu, X. Chen, M. A. Wani, P. Angelov, & J.-H. Lai (Eds.), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 (pp. 545-552). Article 7033174 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/icmla.2014.111

Aslan, Sinem ; Cataltepe, Zehra ; Diner, Itai et al. / Learner engagement measurement and classification in 1:1 learning. Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. editor / Cesar Ferri ; Guangzhi Qu ; Xue-wen Chen ; M. Arif Wani ; Plamen Angelov ; Jian-Huang Lai. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 545-552 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014).

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title = "Learner engagement measurement and classification in 1:1 learning",

abstract = "We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.",

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author = "Sinem Aslan and Zehra Cataltepe and Itai Diner and Onur Dundar and Esme, {Asli A.} and Ron Ferens and Gila Kamhi and Ece Oktay and Canan Soysal and Murat Yener",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014 ; Conference date: 03-12-2014 Through 06-12-2014",

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doi = "10.1109/icmla.2014.111",

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Aslan, S, Cataltepe, Z, Diner, I, Dundar, O, Esme, AA, Ferens, R, Kamhi, G, Oktay, E, Soysal, C & Yener, M 2014, Learner engagement measurement and classification in 1:1 learning. in C Ferri, G Qu, X Chen, MA Wani, P Angelov & J-H Lai (eds), Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014., 7033174, Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, Institute of Electrical and Electronics Engineers Inc., pp. 545-552, 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014, Detroit, United States, 3/12/14. https://doi.org/10.1109/icmla.2014.111

Learner engagement measurement and classification in 1:1 learning. / Aslan, Sinem; Cataltepe, Zehra; Diner, Itai et al.
Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. ed. / Cesar Ferri; Guangzhi Qu; Xue-wen Chen; M. Arif Wani; Plamen Angelov; Jian-Huang Lai. Institute of Electrical and Electronics Engineers Inc., 2014. p. 545-552 7033174 (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Cataltepe, Zehra

AU - Diner, Itai

AU - Dundar, Onur

AU - Esme, Asli A.

AU - Ferens, Ron

AU - Kamhi, Gila

AU - Oktay, Ece

AU - Soysal, Canan

AU - Yener, Murat

PY - 2014/2/5

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N2 - We explore the feasibility of measuring learner engagement and classifying the engagement level based on machine learning applied on data from 2D/3D camera sensors and eye trackers in a 1:1 learning setting. Our results are based on nine pilot sessions held in a local high school where we recorded features related to student engagement while consuming educational content. We label the collected data as Engaged or NotEngaged while observing videos of the students and their screens. Based on the collected data, perceptual user features (e.g., body posture, facial points, and gaze) are extracted. We use feature selection and classification methods to produce classifiers that can detect whether a student is engaged or not. Accuracies of up to 85-95% are achieved on the collected dataset. We believe our work pioneers in the successful classification of student engagement based on perceptual user features in a 1:1 authentic learning setting.

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Aslan S, Cataltepe Z, Diner I, Dundar O, Esme AA, Ferens R et al. Learner engagement measurement and classification in 1:1 learning. In Ferri C, Qu G, Chen X, Wani MA, Angelov P, Lai JH, editors, Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 545-552. 7033174. (Proceedings - 2014 13th International Conference on Machine Learning and Applications, ICMLA 2014). doi: 10.1109/icmla.2014.111

Learner engagement measurement and classification in 1:1 learning

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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