
Original Research
Although the rise of online learning is becoming predominant in English as a Foreign Language (EFL) settings, little is known about the distinct functions of grit and motivation to forecast the willingness of students to study online. This paper aims to uncover how grit and motivation predict online learning readiness in undergraduate EFL students independently and jointly. In a cross-sectional survey, a convenience sample of 103 undergraduates was exposed to three validated measures, EFL grit scale, EFL motivation scale, and online learning readiness scale. Multiple linear regression showed that motivation was a significant positive predictor of readiness to online learning (β =.480, p =.001), but grit was not (β =.133, p =.219). The model explained 32% of the readiness variance (R² = .320, F(2, 86) = 20.271, p < .001). These findings highlight the need for emphasizing the crucial role of motivation to make EFL students ready to succeed in online learning settings. Teachers and policy-makers are advised to focus on motivational techniques, including establishing goals, supporting autonomy, and relevance frames to promote better preparedness and involvement levels among learners in online EFL courses. Future longitudinal studies ought to be conducted on grit and motivation variables to enhance the results of the online learning process.
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Digital Education; Distance Learning; Grit; Motivation; Online Learning Readiness
Acknowledgements
We would like to thank the participants in this study.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
CRediT Authorship Contribution Statement
Mohamed Benhima: Conceptualization, Methodology, Software, Investigation, Data Curation, and Writing Original Draft
Bendaoud Nadif: Conceptualization, Investigation, Methodology, Resources, Supervision, Project Administration, and Supervision
Abdulaziz A. Abanomey: Writing, Review and Editing
Mohamed Nabil Abdelhady Ahmed: Validation
Abdelouahed Bouih: Formal analysis
Mohammed Mansouri: Visualization, formal analysis
Mohammed Zemrani: Writing, Review, Editing
Abdulnaser Fakhrou: Writing, Review, Editing
Generative AI Use Disclosure Statement
We acknowledge that we did not use AI in this manuscript.
Ethics Declarations
World Medical Association (WMA) Declaration of Helsinki–Ethical Principles for Medical Research Involving Human Participants
This research was accomplished in fulfillment with the World Medical Association the university (Approval No. 19/KER-LPPM/EC/2023). Every participant was informed about the process.
Competing Interests
We declare that we have no competing interests.
Data Availability
The data are available upon reasonable request.