research-article

Descriptive Network Modeling and Analysis for Investigating User Acceptance in a Learning Management System Context

Authors:

Roberto Rondinelli,

Menno van Zaanen,

Martin AtzmuellerAuthors Info & Claims

ABIS '19: Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond

Pages 7 - 13

https://doi.org/10.1145/3345002.3349288

Published: 12 September 2019 Publication History

Abstract

Learning Management Systems (LMSs) play a significant role in educational technology. In this paper, we analyze different approaches in order to investigate the acceptance of an LMS. Utilizing questionnaire information structured on the Technology Acceptance Model (TAM), we apply descriptive network modeling and analysis complementing basic statistical analysis in order to identify specific patterns in the user data. We present the applied analysis methodology in detail, and demonstrate the connection to user modeling:here, descriptive statistics indicate student satisfaction with the usage (acceptance level) as a whole; network analysis indicates the level of variability w.r.t. the user questions, while specific patterns or motifs show the satisfaction levels for the different networks.

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Cited By

Blanchard JHott JBerry VCarroll REdmison BGlassey RKarnalim OPlancher BRussell SQuille KAlshaigy BBecker BQuille KLaakso M(2022)Stop Reinventing the Wheel! Promoting Community Software in Computing EducationProceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3571785.3574129(261-292)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1145/3571785.3574129
Shayan Pvan Zaanen MAtzmueller M(2022)Predicting User Dropout from Their Online Learning BehaviorDiscovery Science10.1007/978-3-031-18840-4_18(243-252)Online publication date: 6-Nov-2022
https://doi.org/10.1007/978-3-031-18840-4_18

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Descriptive Network Modeling and Analysis for Investigating User Acceptance in a Learning Management System Context

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cover image ACM Conferences

ABIS '19: Proceedings of the 23rd International Workshop on Personalization and Recommendation on the Web and Beyond

September 2019

39 pages

ISBN:9781450368964

DOI:10.1145/3345002

Program Chairs:
Mirjam Augstein
University of Applied Sciences Upper Austria, Hagenberg, Austria
,
Eelco Herder
Radboud University, Nijmegen, the Netherlands
,
Wolfgang Wörndl
Technical University of Munich, Germany
,
Enes Yigitbas
Universität Paderborn, Germany

Copyright © 2019 ACM.

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Publication History

Published: 12 September 2019

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German Research Foundation (DFG)

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HT '19

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HT '19: 30th ACM Conference on Hypertext and Social Media

September 17, 2019

Hof, Germany

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Cited By

Blanchard JHott JBerry VCarroll REdmison BGlassey RKarnalim OPlancher BRussell SQuille KAlshaigy BBecker BQuille KLaakso M(2022)Stop Reinventing the Wheel! Promoting Community Software in Computing EducationProceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3571785.3574129(261-292)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1145/3571785.3574129
Shayan Pvan Zaanen MAtzmueller M(2022)Predicting User Dropout from Their Online Learning BehaviorDiscovery Science10.1007/978-3-031-18840-4_18(243-252)Online publication date: 6-Nov-2022
https://doi.org/10.1007/978-3-031-18840-4_18

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