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Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

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Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources. / Onah, Daniel Friday Owoichoche; Pang, Elaine Ling Ling ; Sinclair, Jane ; Uhomoibhi, James.

IMCL 2018: Mobile Technologies and Applications for the Internet of Things . ed. / Michael E. Auer; Thrayvoulos Tsiatsos. Cham : Springer, 2019. p. 14-21 (Advances in Intelligent Systems and Computing ; Vol. 909).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Onah, DFO, Pang, ELL, Sinclair, J & Uhomoibhi, J 2019, Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources. in ME Auer & T Tsiatsos (eds), IMCL 2018: Mobile Technologies and Applications for the Internet of Things . Advances in Intelligent Systems and Computing , vol. 909, Springer, Cham, pp. 14-21. https://doi.org/10.1007/978-3-030-11434-3_3

APA

Onah, D. F. O., Pang, E. L. L., Sinclair, J., & Uhomoibhi, J. (2019). Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources. In M. E. Auer, & T. Tsiatsos (Eds.), IMCL 2018: Mobile Technologies and Applications for the Internet of Things (pp. 14-21). (Advances in Intelligent Systems and Computing ; Vol. 909). Springer. https://doi.org/10.1007/978-3-030-11434-3_3

Vancouver

Onah DFO, Pang ELL, Sinclair J, Uhomoibhi J. Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources. In Auer ME, Tsiatsos T, editors, IMCL 2018: Mobile Technologies and Applications for the Internet of Things . Cham: Springer. 2019. p. 14-21. (Advances in Intelligent Systems and Computing ). https://doi.org/10.1007/978-3-030-11434-3_3

Author

Onah, Daniel Friday Owoichoche ; Pang, Elaine Ling Ling ; Sinclair, Jane ; Uhomoibhi, James. / Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources. IMCL 2018: Mobile Technologies and Applications for the Internet of Things . editor / Michael E. Auer ; Thrayvoulos Tsiatsos. Cham : Springer, 2019. pp. 14-21 (Advances in Intelligent Systems and Computing ).

Bibtex

@inbook{9612c9e49968493bacbcf188bb1463d5,
title = "Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources",
abstract = "Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of Massive Open Online Courses (MOOCs) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real-world conventional education setting. The educational pedagogy is tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issues in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. First, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Second, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course.",
keywords = "Learning analytics, MOOC, self-regulated learning",
author = "Onah, {Daniel Friday Owoichoche} and Pang, {Elaine Ling Ling} and Jane Sinclair and James Uhomoibhi",
year = "2019",
month = apr
day = "18",
doi = "10.1007/978-3-030-11434-3_3",
language = "English",
isbn = "9783030114336",
series = "Advances in Intelligent Systems and Computing ",
publisher = "Springer",
pages = "14--21",
editor = "Auer, {Michael E.} and Thrayvoulos Tsiatsos",
booktitle = "IMCL 2018",

}

RIS

TY - CHAP

T1 - Learning Analytics for Motivating Self-regulated Learning and Fostering the Improvement of Digital MOOC Resources

AU - Onah, Daniel Friday Owoichoche

AU - Pang, Elaine Ling Ling

AU - Sinclair, Jane

AU - Uhomoibhi, James

PY - 2019/4/18

Y1 - 2019/4/18

N2 - Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of Massive Open Online Courses (MOOCs) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real-world conventional education setting. The educational pedagogy is tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issues in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. First, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Second, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course.

AB - Nowadays, the digital learning environment has revolutionized the vision of distance learning course delivery and drastically transformed the online educational system. The emergence of Massive Open Online Courses (MOOCs) has exposed web technology used in education in a more advanced revolution ushering a new generation of learning environments. The digital learning environment is expected to augment the real-world conventional education setting. The educational pedagogy is tailored with the standard practice which has been noticed to increase student success in MOOCs and provide a revolutionary way of self-regulated learning. However, there are still unresolved questions relating to the understanding of learning analytics data and how this could be implemented in educational contexts to support individual learning. One of the major issues in MOOCs is the consistent high dropout rate which over time has seen courses recorded less than 20% completion rate. This paper explores learning analytics from different perspectives in a MOOC context. First, we review existing literature relating to learning analytics in MOOCs, bringing together findings and analyses from several courses. We explore meta-analysis of the basic factors that correlate to learning analytics and the significant in improving education. Second, using themes emerging from the previous study, we propose a preliminary model consisting of four factors of learning analytics. Finally, we provide a framework of learning analytics based on the following dimensions: descriptive, diagnostic, predictive and prescriptive, suggesting how the factors could be applied in a MOOC context. Our exploratory framework indicates the need for engaging learners and providing the understanding of how to support and help participants at risk of dropping out of the course.

KW - Learning analytics

KW - MOOC

KW - self-regulated learning

U2 - 10.1007/978-3-030-11434-3_3

DO - 10.1007/978-3-030-11434-3_3

M3 - Chapter

SN - 9783030114336

T3 - Advances in Intelligent Systems and Computing

SP - 14

EP - 21

BT - IMCL 2018

A2 - Auer, Michael E.

A2 - Tsiatsos, Thrayvoulos

PB - Springer

CY - Cham

ER -