Multi-Level Analysis of Learning Management Systems’ User Acceptance Exemplified in Two System Case Studies

Parisa Shayan (), Roberto Rondinelli, Menno van Zaanen and Martin Atzmueller
Additional contact information
Parisa Shayan: School of Humanities and Digital Sciences, Cognitive Science and Artificial Intelligence, Tilburg University, 5037 AB Tilburg, The Netherlands
Roberto Rondinelli: Department of Economics and Statistics, University of Naples Federico II, Via Cintia 26, 80126 Naples, Italy
Menno van Zaanen: South African Centre for Digital Language Resources, North-West University, Potchefstroom 2520, South Africa
Martin Atzmueller: Semantic Information Systems Group, Osnabrück University, 49090 Osnabrück, Germany

Data, 2023, vol. 8, issue 3, 1-27

Abstract: There has recently been an increasing interest in Learning Management Systems (LMSs). It is currently unclear, however, exactly how these systems are perceived by their users. This article analyzes data on user acceptance for two LMSs (Blackboard and Canvas). The respective data are collected using a questionnaire modeled after the Technology Acceptance Model (TAM); it relates several variables that influence system acceptability, allowing for a detailed analysis of the system acceptance. We present analyses at two levels of the questionnaire data: questions and constructs (taken from TAM) as well as on different analysis levels using targeted methods. First, we investigate the differences between the above LMSs using statistical tests ( t -test). Second, we provide results at the question level using descriptive indices, such as the mean and the Gini heterogeneity index, and apply methods for ordinal data using the Cumulative Link Mixed Model (CLMM). Next, we apply the same approach at the TAM construct level plus descriptive network analysis (degree centrality and bipartite motifs) to explore the variability of users’ answers and the degree of users’ satisfaction considering the extracted patterns. In the context of TAM, the statistical model is able to analyze LMS acceptance on the question level. As we are also very much interested in identifying LMS acceptance at the construct level, in this article, we provide both statistical analysis as well as network analysis to explore the connection between questionnaire data and relational data. A network analysis approach is particularly useful when analyzing LMS acceptance on the construct level, as this can take the structure of the users’ answers across questions per construct into account. Taken together, these results suggest a higher rate of user acceptance among Canvas users compared to Blackboard both for the question and construct level. Likewise, the descriptive network modeling for Canvas indicates a slightly higher concordance between Canvas users than Blackboard at the construct level.

Keywords: Learning Management System; Technology Acceptance Model; Cumulative Link Mixed Model; descriptive network analysis (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2306-5729/8/3/45/pdf (application/pdf)
https://www.mdpi.com/2306-5729/8/3/45/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jdataj:v:8:y:2023:i:3:p:45-:d:1077114

Access Statistics for this article

Data is currently edited by Ms. Cecilia Yang

More articles in Data from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().