Modelling Students’ Adoption of E-Learning During the COVID-19 Pandemic

Jamalova, Maral; Balint, Csaba

doi:10.3991/ijet.v17i07.29243

Cited by 14 publications

(21 citation statements)

References 59 publications

(144 reference statements)

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“…Cronbach's alpha is an excellent tool to determine the consistency of the set of survey responses. This coefficient, ranging from 0 to 1, is used to determine the degree of reliability of the variables from the correlation analysis [42]. For this test, ordinal variables were considered.…”

Section: Validity Of the Data Collectedmentioning

confidence: 99%

Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education

et al. 2022

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Since mid-March 2020, due to the COVID-19 pandemic, higher education has been facing a very uncertain situation, despite the hasty implementation of information and communication technologies for distance and online learning. Hybrid learning, i.e., the mixing of distance and face-to-face learning, seems to be the rule in most universities today. In order to build a post-COVID-19 university education, i.e., one that is increasingly digital and sustainable, it is essential to learn from these years of health crisis. In this context, this paper aims to identify and quantify the main factors affecting mechanical engineering student performance in order to build a generalized linear autoregressive (GLAR) model. This model, which is distinguished by its simplicity and ease of implementation, is responsible for predicting student grades in online learning situations in hybrid environments. The thirty or so variables identified by a previously tested model in 2020–2021, in which distance learning was the exclusive mode of learning, were evaluated in blended learning spaces. Given the low predictive power of the original model, about ten new factors, specific to blended learning, were then identified and tested. The refined version of the GLAR model predicts student grades to within ±1 with a success rate of 63.70%, making it 28.08% more accurate than the model originally created in 2020–2021. Special attention was also given to students whose grade predictions were underestimated and who failed. The methodology presented is applicable to all aspects of the academic process, including students, instructors, and decisionmakers.

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Section: Validity Of the Data Collectedmentioning

confidence: 99%

Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education

et al. 2022

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“…The current paper mainly focuses on the influence of environmental and situational variables [12] on the performance of surveyed university students during the pandemic. Previous studies regarding the performance of students during the COVID-19 pandemic mostly use different models such as Technology Acceptance Model -TAM [13]- [15] and Unified Theory of Acceptance and Use of Technology -UTAUT [16]. Some other studies focused on the relationship between performance and study environment [17], psychological impact of COVID-19 [18], difference between face-to-face and online learning [19] and challenges in general [20].…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

“…Unfortunately, a limited number of e-learning related studies involve the mentioned moderators. Few studies dealt with the impact of age and/or gender on the formulation of behavioral intention [13], [49]- [51]. There are even less scientific works analysing the relationships between the environmental and situational factors and performance during COVID-19 pandemic.…”

Section: Moderation Effect Of Age and Gender On Students' Performancementioning

confidence: 99%

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Jamalova

Bálint²

2023

Int. J. Emerg. Technol. Learn.

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This study examines the impact of gender and age differences on the performance of students from different Hungarian universities and colleges in online learning during the third wave of COVID-19. The survey responses were assessed using Partial Least Squares estimation technique. The research model attempts to understand the influence of environmental and situational variables (i.e., compatibility, accessibility, perception of online self-efficacy, mobility) on performance and satisfaction with online education. Apart from mobility, other indicators have significant impact on respondents’ performance. However, moderating effect of age and gender almost do not influence the performance of surveyed Hungarian students. The results demonstrate that gender impacts the compatibility → performance pathway. The age of respondents has no effect on relationships between environmental and situational variables and performance.

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“…It combines the features of distance and online learning. Although it has many possibilities (flexibility, individual learning paths), its limitations and difficulties may also arise (low digital competence of the participants; lack of tools and personal connection) (Jamalova, 2022) (Bereczki et al, 2020) (Nedda & Bernadett, 2020). Creativity, innovation, autonomy, and problem-solving allow students to improve their skills.…”

Section: Introductionmentioning

confidence: 99%

Developing Digital Entrepreneurship Learning Model: 4-D Competencies-Based for Millennial Generation in Higher Education

Erdisna

Ridwan

Syahputra

2022

Utamax

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This present R&D study aims to develop a digital model of millennial entrepreneurship in universities attached to the Information System Design Analysis course with 4C competencies (Communication, Collaboration, Critical thinking, and Creativity). The problem is that students only make software to fulfill course assignments without thinking about the usefulness of the software they produce as a means of digital entrepreneurship. The research method used was a 4-D development model consisting of 4 stages: Definition, Design, Development, and Dissemination. The literacy model testing carried out showed that the average score was 0.840, which stated that the model was distributed as valid and doable to use. The average results of the practicality assessment of the operation of the Digital entrepreneurship literacy model grounded on the perception of the experimental class scholars were 87 in the practical order. The results show that the digital entrepreneurship model encourages students to open businesses using information technology. The development of this entrepreneurship digital learning model can train students to have an entrepreneurial spirit. In addition, it can improve students' skills in 21st-century literacy.

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Modelling Students’ Adoption of E-Learning During the COVID-19 Pandemic

Cited by 14 publications

References 59 publications

Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education

Grade Prediction Modeling in Hybrid Learning Environments for Sustainable Engineering Education

What Matters the Most?

Developing Digital Entrepreneurship Learning Model: 4-D Competencies-Based for Millennial Generation in Higher Education

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