Factors influencing first-year students’ intention to continuously use the Skills Assessment Manager Online Learning Environment at the Central University of Technology, Free State - Nortje, Hester Aletta (Central University of Technology, 2022)
Introduction: With the rapid development of digital technologies, many tertiary education institutions have introduced online learning environments (OLEs) as a component of blended learning. These OLEs bring about possibilities to expand the student population beyond the limitations of the physical boundaries of traditional classroom-based learning. OLEs create interactive and engaging environments in which students have flexible access to learning materials and are able to study more independently in relation to time and place. Advances in new technologies in online learning have increased ubiquitous access, ease of use, functionality, and flexibility. Serious concerns with OLEs are a decline in student persistence in engagement with an OLE, and the likelihood of students dropping out of a course. Students who disengage from OLEs often fail to reap the benefits of the course content. Therefore, an understanding of the factors that influence students’ intention to continuously engage with an OLE will provide information on how to optimise the OLE so that students can achieve maximum learning success. This study was, thus, undertaken to identify the factors that influence students’ intention to continuously use the skills assessment manager (SAM) OLE implemented at the Central University of Technology, Free State, for the delivery of the module Basic Digital Literacy to all first-year students at the university. Methods: In this three-phase study, a positivist approach was followed. In Phase 1, a literature search was undertaken to identify literature with relevant theories and factors that could be used to devise measurement models for the assessment of a structural model comprising of potential factors and the target factor Continuous Use Intention. In the structural model, linkages between potential factors and the target factor, Continuous Use Intention, were indicated. Thereafter, 13 relational hypotheses between factors were formulated for testing. In Phase 2, the factors were operationalised by specifying measurement models contained in a questionnaire with specific items (observable variables) relating to each factor (latent variables). To gather measurement data, the questionnaire was administered to 1 003 students, of whom 793 responded. The data were analysed using partial least squares structural equation modelling with the program SmartPLS, to assess the validity and reliability of the measurement models. In Phase 3, the structural model was validated and assessed, after which the relational hypotheses were tested using SmartPLS. Thereafter, the actual factors that influence students’ intention to continuously use the OLE were identified. Results: Because the structural model was free of collinearity issues, it could be assessed. Twelve of the 13 relational hypotheses were significant at 𝛼𝛼 = 0.05. These 12 relationships were used to construct a revised structural model showing six actual factors in relationships with Continuous Use Intention. The actual factors, Flow, Satisfaction and Perceived Usefulness, explained 47.5% of Continuous Use Intention. The actual factors, Confirmation, Perceived Ease of Use and Perceived Usefulness explained 63.6% of Satisfaction. Information Quality, Confirmation, and Perceived Ease of Use explained 52.4% of Perceived Usefulness. Information Quality, and Confirmation explained 47.2% of Perceived Ease of Use. Information Quality explained 35.9% of Confirmation. Significance: By identifying factors that influence students’ intention to continuously engage with the SAM OLE while studying Basic Digital Literacy, these factors will inform decisions to optimise the OLE to maximise student learning success.
Thesis (Master: Information Technology)--Central University of Technology
Keywords: Continuous use intention; Structural model; Measurement models; Partial least squares (PLS) structural equation modelling (SEM); Online learning environment (OLE); Skills assessment manager (SAM); SmartPLS