Disciplinary differences in blended learning design: a network analytic study

Alexander Whitelock-Wainwright; Yi Shan Tsai; Kayley Lyons; Svetlana Kaliff; Mike Bryant; Kris Ryan; Dragan Gaševic

doi:10.1145/3375462.3375488

Disciplinary differences in blended learning design: a network analytic study

Alexander Whitelock-Wainwright, Yi Shan Tsai, Kayley Lyons, Svetlana Kaliff, Mike Bryant, Kris Ryan, Dragan Gaševic

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

6 Citations (Scopus)

Abstract

Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016-2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.

Original language	English
Title of host publication	LAK'20
Subtitle of host publication	Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
Editors	Maren Scheffel, Vitomir Kovanović, Niels Pinkwart, Katrien Verbert
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	579-588
Number of pages	10
ISBN (Electronic)	9781450377126
DOIs	https://doi.org/10.1145/3375462.3375488
Publication status	Published - 2020
Event	International Learning Analytics & Knowledge Conference 2020 - Frankfurt, Germany Duration: 23 Mar 2020 → 27 Mar 2020 Conference number: 10th https://lak20.solaresearch.org (Website) https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3375462 (Website)

Conference

Conference	International Learning Analytics & Knowledge Conference 2020
Abbreviated title	LAK 2020
Country/Territory	Germany
City	Frankfurt
Period	23/03/20 → 27/03/20
Internet address	https://lak20.solaresearch.org (Website) https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3375462 (Website)

Keywords

Epistemic network analysis
Faculty
Learning activity types

Access to Document

10.1145/3375462.3375488

Cite this

Whitelock-Wainwright, A., Tsai, Y. S., Lyons, K., Kaliff, S., Bryant, M., Ryan, K., & Gaševic, D. (2020). Disciplinary differences in blended learning design: a network analytic study. In M. Scheffel, V. Kovanović, N. Pinkwart, & K. Verbert (Eds.), LAK'20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (pp. 579-588). Association for Computing Machinery (ACM). https://doi.org/10.1145/3375462.3375488

Whitelock-Wainwright, Alexander ; Tsai, Yi Shan ; Lyons, Kayley et al. / Disciplinary differences in blended learning design : a network analytic study. LAK'20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. editor / Maren Scheffel ; Vitomir Kovanović ; Niels Pinkwart ; Katrien Verbert. New York NY USA : Association for Computing Machinery (ACM), 2020. pp. 579-588

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abstract = "Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016-2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.",

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doi = "10.1145/3375462.3375488",

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booktitle = "LAK'20",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

note = "International Learning Analytics & Knowledge Conference 2020, LAK 2020 ; Conference date: 23-03-2020 Through 27-03-2020",

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Whitelock-Wainwright, A, Tsai, YS, Lyons, K, Kaliff, S, Bryant, M, Ryan, K & Gaševic, D 2020, Disciplinary differences in blended learning design: a network analytic study. in M Scheffel, V Kovanović, N Pinkwart & K Verbert (eds), LAK'20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. Association for Computing Machinery (ACM), New York NY USA, pp. 579-588, International Learning Analytics & Knowledge Conference 2020, Frankfurt, Hesse, Germany, 23/03/20. https://doi.org/10.1145/3375462.3375488

Disciplinary differences in blended learning design: a network analytic study. / Whitelock-Wainwright, Alexander; Tsai, Yi Shan; Lyons, Kayley et al.
LAK'20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. ed. / Maren Scheffel; Vitomir Kovanović; Niels Pinkwart; Katrien Verbert. New York NY USA: Association for Computing Machinery (ACM), 2020. p. 579-588.

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

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T1 - Disciplinary differences in blended learning design

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AU - Whitelock-Wainwright, Alexander

AU - Tsai, Yi Shan

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AU - Kaliff, Svetlana

AU - Bryant, Mike

AU - Ryan, Kris

AU - Gaševic, Dragan

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N2 - Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016-2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.

AB - Learning design research has predominately relied upon survey- and interview-based methodologies, both of which are subject to limitations of social desirability and recall. An alternative approach is offered in this manuscript, whereby physical and online learning activity data is analysed using Epistemic Network Analysis. Using a sample of 6,040 course offerings from 10 faculties across a four year period (2016-2019), the utility of networks to understand learning design is illustrated. Specifically, through the adoption of a network analytic approach, the following was found: universities are clearly committed to blended learning, but there are considerable differences both between and within disciplines.

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KW - Learning activity types

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A2 - Scheffel, Maren

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Y2 - 23 March 2020 through 27 March 2020

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Whitelock-Wainwright A, Tsai YS, Lyons K, Kaliff S, Bryant M, Ryan K et al. Disciplinary differences in blended learning design: a network analytic study. In Scheffel M, Kovanović V, Pinkwart N, Verbert K, editors, LAK'20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. New York NY USA: Association for Computing Machinery (ACM). 2020. p. 579-588 doi: 10.1145/3375462.3375488

Disciplinary differences in blended learning design: a network analytic study

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Conference

Keywords

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