Explaining persistence in online courses in higher education: a difference-in-differences analysis

As the coronavirus disease 2019 (COVID-19) trajectory remains unknown, online learning (OL) has replaced face-to-face teaching strategies in education institutions. Research and training focus on harnessing educators, but less is understood at the students’ ends. This study examines the OL readiness components, self-regulation, enablers, and barriers to OL at home among medical students during the COVID-19 pandemic through an explanatory mixed-method study. We conducted a cross-sectional study with 178 students and an in-depth interview with 10 students from a public medical school in Malaysia. We found that while all students owned at least one learning device, 22.5% of the students did not have a learning space at home. 21.9% students did not have Wi-Fi access, and 11.2% did not receive mobile broadband coverage at home. Despite these barriers, students had a suitable OL self-regulation level. Significantly higher self-regulation was observed among pre-clinical year students, students with higher grades, and students who had designated learning space at home. We found that high-immediacy and low-bandwidth applications such as WhatsApp and Telegram, and YouTube as the most accessible and easiest platforms to navigate in OL. Our qualitative findings yielded a conceptual model of OL enablers at learners, educators, and institution levels. This framework may serve as one of the guides in faculty development planning and policymaking, especially in promoting a more socially inclusive OL.

Section: Discussionmentioning

confidence: 99%

Enablers and Barriers to Online Learning among Medical Students during COVID-19 Pandemic: An Explanatory Mixed-Method Study

Roslan

Halim

2021

“…Many institutions, especially those who had no emergency funds, are at risk of going bankrupt or preparing to shrink their operations and course offerings to affordable sizes next to suspensions or delays of their research activities [27]. As talks continue about a change of mentality and a revamp of traditional education to a modified online, hybrid [28] or even decentralized [29] version, students are demanding sizable reductions in tuitions and fees, and yet others have already started questioning the value of a college degree if it does not increase employment prospects [12]. For instance, there is news about British universities potentially facing a £760 million loss as a result of one in five students planning to defer their 2020 commencement until universities return to their normal operations.…”

Section: Framework Concepts and Elementsmentioning

confidence: 99%

Fostering Equality in Education: The Blockchain Business Model for Higher Education (BBM-HE)

Son-Turan

2022

This paper seeks to address which business model in higher education that fosters SDG 4, is adequate for the post-pandemic period. To that end, it introduces the “Blockchain Business Model for Higher Education” (BBM-HE) and a transformed business model canvas framework based on existing literature, concepts, theories and findings relating to most of the pressing issues in higher education from the present study. To determine these issues, secondary data is used in the qualitative research design by applying inductive content analysis techniques to online reports. The originality of this study lies in the “adaptive” perspective to the requirements of the post-pandemic higher education landscape, which consists of modifications to the core elements of higher education, the integration of blockchain technology into the entire system, and a stronger approach to sustainability practice through sustainability tokens. The envisaged model sets out to provide a roadmap for all stakeholders, but most importantly, “decentralized” higher education institutions of the future and the “employable skills-seeking” proactive students all over the world, as opposed to the former “solely degree-focused and affluent” consumers of educational offerings. This study contributes to higher education literature in terms of business models, blockchains, pandemics, and sustainability.

“…Structural equation modelling (SEM) was employed to analyse the relationships among the aforementioned variables. The SEM technique has previously been applied to the UTAUT model by various authors [51][52][53][54]. SEM is a multivariate analysis that integrates factor and path analysis [55] and allows researchers to test and estimate a set of hypothesised relationships between numerous independent and dependent variables, each of which can be assessed by a set of indicators based on a theoretical model [50,56].…”

Section: Statistical and Econometric Analysismentioning

confidence: 99%

Micro-Irrigation Technology Adoption in the Bekaa Valley of Lebanon: A Behavioural Model

Sabbagh

Gutierrez

2022

Potato crops are one of the main sources of income for farmers living in the Bekaa Valley of Lebanon. Given the high sensitivity of potatoes to water stress, water shortages can cause considerable losses in terms of potato yield and quality. To overcome this challenge, the use of water-saving technologies such as micro-irrigation systems are very important. However, the adoption of this technique remains quite low among potato farmers in the Bekaa region, who still use ordinary sprinkler systems. In this study, the unified theory of acceptance and use of technology (UTAUT) serves as the conceptual framework for investigating these farmers’ behaviour in adopting a new micro-irrigation system. To achieve this objective, we extended the UTAUT model by considering farmers’ risk perception of the use of a new micro-irrigation technology. The moderators tested were age, previous experience, voluntariness of use, gross unit margin and educational level. Examining the standard regression coefficients, i.e., the β weights, the results indicate that performance expectancy raised behavioural intention for investment in micro-irrigation (β = 0.29) while for effort expectancy the β weight value was 0.24. Overall, an increase of 1 standard deviation of the behavioural intention strongly impacted investment in micro-irrigation systems, β = 0.8 standard deviation of the effective adoption of the technology. Risk perception (β = −0.08) negatively affected farmers’ performance expectancy, i.e., the higher the perceived risk, the lower the perceived performance of the investment, which in turn affected their intention to use micro-irrigation systems. Age (β = 0.11) exerted a significant effect on effort expectancy. Finally in this paper, the policy implications of the results are discussed.