An empirical investigation of the effectiveness of eLearning strategies in higher education: a Rasch model for Saudi Arabia

Barefah, A 2019, An empirical investigation of the effectiveness of eLearning strategies in higher education: a Rasch model for Saudi Arabia, Doctor of Philosophy (PhD), Business IT and Logistics, RMIT University.

Document type: Thesis
Collection: Theses

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Title An empirical investigation of the effectiveness of eLearning strategies in higher education: a Rasch model for Saudi Arabia
Author(s) Barefah, A
Year 2019
Abstract The adoption of eLearning in higher education (HE) is growing in the educational technology literature. However, high-impact, effective, eLearning applications are rarely reported. How learners interact with the technical components during the learning process is yet to be fully understood. The interactivity is a principle element for the design and evaluation of digital learning environments. Its nature and effects in relation to learners' performance is critical for the selection of eLearning strategies and the articulation of ePedagogical practices. Thus, this thesis aims to investigate the ePedagogical environment to understand the nature of the interactive effects of instructional delivery modes and individual's cognitive preferences on learners' performance. The investigation is driven by measuring the effects of the interplay between the two key variables representing each dimension.

Data were obtained through a series of 2x3 factorial quasi-experiments which were conducted at four HE institutions in the Kingdom of Saudi Arabia. The empirical studies examined the performance of 162 female undergraduates who had different cognitive preferences and received instruction through one of three delivery modes: i) face-to-face, ii) fully computerised and iii) blended modes. The participants were in their third year of the undergraduate Management of Information Systems program and were required to enrol in the course Information Systems Analysis and Design for the first time. Three instruments were utilised for the data collection; i) the cognitive style analysis test (CSA), ii) the pre-test and iii) the post-test. The validity and reliability of the cognitive performance measures (the pre-and-post-tests) were established under the auspices of the Rasch model using the QUEST interactive test analysis system software.

The cognitive performance measurement scale was found to be unidimensional with a strong item fit statistic, ranging from 0.81 to 1.00, indicating that the test-items have measured the intended underlying construct. The subsequent performance analyses were conducted using Cohen's effect size to compare the extent of differences between the experimental groups. The results confirmed the interactional effects of delivery modes and learners' cognitive preference on their cognitive performance. It was also found that participants were able to achieve their best performance under the computerised delivery mode, which had the highest effect size of ES= 0.29 among the F-2-F and blended modes.

This thesis enhances the conceptualisation of the interactivity between the human and technological dimensions of the eLearning environment. It highlights the nature and level of the interaction, information that may be utilised to improve the design and evaluation practices of personalised HE learning. The study also demonstrates that the application of the Rasch measurement approach is effective for agile instructional design and performance measurement. It proposes an empirically validated ISD model that guides the effective design of digital learning environments for the Saudi Arabian HE sector.
Degree Doctor of Philosophy (PhD)
Institution RMIT University
School, Department or Centre Business IT and Logistics
Subjects Information Systems Theory
Higher Education
Curriculum and Pedagogy Theory and Development
Conceptual Modelling
Computer-Human Interaction
Human Information Behaviour
Education Assessment and Evaluation
Keyword(s) eLearning
Rasch model
Higher education
Saudi Arabia
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Created: Mon, 29 Apr 2019, 08:50:56 EST by Adam Rivett
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