Disciplinary Differences in Blended Learning Design: A Network Analytic Study

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

doi:10.1145/3375462

Disciplinary Differences in Blended Learning Design: A Network Analytic Study

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

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

Abstract / Description of output

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: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge
Publisher	Association for Computing Machinery (ACM)
Pages	579-588
Number of pages	10
ISBN (Print)	9781450377126
DOIs	https://doi.org/10.1145/3375462
Publication status	Published - 23 Mar 2020
Event	The 10th International Learning Analytics & Knowledge Conference - Frankfurt, Germany Duration: 23 Mar 2020 → 27 Mar 2020 Conference number: 10 https://lak20.solaresearch.org/

Conference

Conference	The 10th International Learning Analytics & Knowledge Conference
Abbreviated title	LAK20
Country/Territory	Germany
City	Frankfurt
Period	23/03/20 → 27/03/20
Internet address	https://lak20.solaresearch.org/

Keywords / Materials (for Non-textual outputs)

Faculty
epistemic network analysis
learning activity types

Access to Document

10.1145/3375462Licence: All Rights Reserved

learningSpaceModel_LAK_25_09_2019_manuscriptAccepted author manuscript, 866 KB

https://dl.acm.org/doi/abs/10.1145/3375462.3375488Licence: All Rights Reserved

Cite this

Whitelock-Wainwright, A., Tsai, Y.-S., Lyons, K., Khalif, S., Bryant, M., Ryan, K., & Gasevic, D. (2020). Disciplinary Differences in Blended Learning Design: A Network Analytic Study. In 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

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title = "Disciplinary Differences in Blended Learning Design: A Network Analytic Study",

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.",

keywords = "Faculty, epistemic network analysis, learning activity types",

author = "Alexander Whitelock-Wainwright and Yi-Shan Tsai and Kayley Lyons and Svetlana Khalif and Mike Bryant and Kris Ryan and Dragan Gasevic",

year = "2020",

month = mar,

day = "23",

doi = "10.1145/3375462",

language = "English",

isbn = "9781450377126",

pages = "579--588",

booktitle = "LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge",

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

address = "United States",

note = "The 10th International Learning Analytics & Knowledge Conference, LAK20 ; Conference date: 23-03-2020 Through 27-03-2020",

url = "https://lak20.solaresearch.org/",

}

Whitelock-Wainwright, A, Tsai, Y-S, Lyons, K, Khalif, S, Bryant, M, Ryan, K & Gasevic, D 2020, Disciplinary Differences in Blended Learning Design: A Network Analytic Study. in LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. Association for Computing Machinery (ACM), pp. 579-588, The 10th International Learning Analytics & Knowledge Conference, Frankfurt, Germany, 23/03/20. https://doi.org/10.1145/3375462

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. Association for Computing Machinery (ACM), 2020. p. 579-588.

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

TY - GEN

T1 - Disciplinary Differences in Blended Learning Design: A Network Analytic Study

AU - Whitelock-Wainwright, Alexander

AU - Tsai, Yi-Shan

AU - Lyons, Kayley

AU - Khalif, Svetlana

AU - Bryant, Mike

AU - Ryan, Kris

AU - Gasevic, Dragan

N1 - Conference code: 10

PY - 2020/3/23

Y1 - 2020/3/23

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.

KW - Faculty

KW - epistemic network analysis

KW - learning activity types

U2 - 10.1145/3375462

DO - 10.1145/3375462

M3 - Conference contribution

SN - 9781450377126

SP - 579

EP - 588

BT - LAK '20: Proceedings of the Tenth International Conference on Learning Analytics & Knowledge

PB - Association for Computing Machinery (ACM)

T2 - The 10th International Learning Analytics & Knowledge Conference

Y2 - 23 March 2020 through 27 March 2020

ER -

Disciplinary Differences in Blended Learning Design: A Network Analytic Study

Abstract / Description of output

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Cite this