Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK

Hafsa Al Ansari; Rupert Ward; Richard Hill

doi:10.1109/ICALT52272.2021.00140

Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK

Hafsa Al Ansari, Rupert Ward, Richard Hill

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

2 Citations (Scopus)

Abstract

This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.

Original language	English
Title of host publication	Proceedings 21st IEEEE International Conference on Advanced Learning Technologies
Subtitle of host publication	ICALT 2021
Editors	Maiga Chang, Nian-Shing Chen, Demetrios G Sampson, Ahmed Tlili
Publisher	IEEE
Pages	442-444
Number of pages	3
ISBN (Electronic)	9781665441063
ISBN (Print)	9781665431163
DOIs	https://doi.org/10.1109/ICALT52272.2021.00140
Publication status	Published - 2 Aug 2021
Event	21st IEEE International Conference on Advanced Learning Technologies - Online, Virtual Duration: 12 Jul 2021 → 15 Jul 2021 Conference number: 21 https://tc.computer.org/tclt/icalt2021/

Publication series

Name	Proceedings (IEEE International Conference on Advanced Learning Technologies)
Publisher	IEEE
ISSN (Print)	2161-3761
ISSN (Electronic)	2161-377X

Conference

Conference	21st IEEE International Conference on Advanced Learning Technologies
Abbreviated title	ICALT 2021
City	Virtual
Period	12/07/21 → 15/07/21
Internet address	https://tc.computer.org/tclt/icalt2021/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICALT52272.2021.00140Licence: Unspecified

Cite this

Al Ansari, H., Ward, R., & Hill, R. (2021). Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK. In M. Chang, N-S. Chen, D. G. Sampson, & A. Tlili (Eds.), Proceedings 21st IEEEE International Conference on Advanced Learning Technologies: ICALT 2021 (pp. 442-444). [9499817] (Proceedings (IEEE International Conference on Advanced Learning Technologies)). IEEE. https://doi.org/10.1109/ICALT52272.2021.00140

Al Ansari, Hafsa ; Ward, Rupert ; Hill, Richard. / Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK. Proceedings 21st IEEEE International Conference on Advanced Learning Technologies: ICALT 2021. editor / Maiga Chang ; Nian-Shing Chen ; Demetrios G Sampson ; Ahmed Tlili. IEEE, 2021. pp. 442-444 (Proceedings (IEEE International Conference on Advanced Learning Technologies)).

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title = "Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK",

abstract = "This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.",

keywords = "Learning analytics, Motivation, Self-determination theory, Learning design, Self-Regulated Learning",

author = "{Al Ansari}, Hafsa and Rupert Ward and Richard Hill",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 21st IEEE International Conference on Advanced Learning Technologies , ICALT 2021 ; Conference date: 12-07-2021 Through 15-07-2021",

year = "2021",

month = aug,

day = "2",

doi = "10.1109/ICALT52272.2021.00140",

language = "English",

isbn = "9781665431163",

series = "Proceedings (IEEE International Conference on Advanced Learning Technologies)",

publisher = "IEEE",

pages = "442--444",

editor = "Maiga Chang and Nian-Shing Chen and Sampson, {Demetrios G} and Ahmed Tlili",

booktitle = "Proceedings 21st IEEEE International Conference on Advanced Learning Technologies",

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url = "https://tc.computer.org/tclt/icalt2021/",

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Al Ansari, H, Ward, R & Hill, R 2021, Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK. in M Chang, N-S Chen, DG Sampson & A Tlili (eds), Proceedings 21st IEEEE International Conference on Advanced Learning Technologies: ICALT 2021., 9499817, Proceedings (IEEE International Conference on Advanced Learning Technologies), IEEE, pp. 442-444, 21st IEEE International Conference on Advanced Learning Technologies , Virtual, 12/07/21. https://doi.org/10.1109/ICALT52272.2021.00140

Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK. / Al Ansari, Hafsa; Ward, Rupert ; Hill, Richard.

Proceedings 21st IEEEE International Conference on Advanced Learning Technologies: ICALT 2021. ed. / Maiga Chang; Nian-Shing Chen; Demetrios G Sampson; Ahmed Tlili. IEEE, 2021. p. 442-444 9499817 (Proceedings (IEEE International Conference on Advanced Learning Technologies)).

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

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Y1 - 2021/8/2

N2 - This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.

AB - This study investigates enhancing student learning and performance by exploring student motivation through the use of learning analytics. A mixed-methods approach will be used to collect data from Computer Science students within the UK higher education sector. The collected data will be analyzed using thematic analysis to develop a theoretical framework that will be tested subsequently using structural equation modeling. The identification of student motivation factors helps tutors and learning analysts to better understand student learning motivation and adapt their learning practices accordingly.

KW - Learning analytics

KW - Motivation

KW - Self-determination theory

KW - Learning design

KW - Self-Regulated Learning

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DO - 10.1109/ICALT52272.2021.00140

M3 - Conference contribution

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T3 - Proceedings (IEEE International Conference on Advanced Learning Technologies)

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Al Ansari H, Ward R , Hill R. Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK. In Chang M, Chen N-S, Sampson DG, Tlili A, editors, Proceedings 21st IEEEE International Conference on Advanced Learning Technologies: ICALT 2021. IEEE. 2021. p. 442-444. 9499817. (Proceedings (IEEE International Conference on Advanced Learning Technologies)). doi: 10.1109/ICALT52272.2021.00140

Developing a Learning Analytics Model to Explore Computer Science Student Motivation in the UK

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