Which virtual education methods do e‑students prefer? Design and validation of Virtual Education Preferences Questionnaire (VEPQ)

Abstract

Abstract Background With the prevalence of new technologies and evolving student learning styles, virtual teaching methods have become increasingly popular. As a result, more and more students are opting to learn online. However, one common concern is that they may feel disconnected from their teachers, leading to feelings of loneliness and doubts about the quality of education they are receiving. To address this issue, a study was conducted to gather data on students' preferences for virtual education and to validate a tool for measuring students' preferences for virtual education. Methods The research was conducted in a mixed method with a quantitative–qualitative sequence. A virtual education preferences questionnaire (VEPQ) for students with a total of 17 items was created and validated as part of the qualitative component by looking at the theoretical underpinnings and experts' opinions in the focus group. The scale of the six-point Likert questionnaire was from very high to very low. To validate the tool and determine preferences, exploratory factor analysis was used. A total of 155 samples answered the questions and the data were analyzed using SPSS-24 software. Results A total of 155 complete questionnaires were returned; among them, 110 (71%) were filled out by women, 73 (47.1%) by respondents between the ages of 36 and 45, 107 (69%) were already employed in paramedical fields, and 48 (31%) were enrolled in a medical school. The opinions of ten experts were used to confirm the face validity of the questionnaire. With CVI = 0.924 and CVR = 0.805, content validity was verified. Using the internal consistency method of the questions with a Cronbach's alpha coefficient of R = 0.824, the validity of the entire questionnaire was confirmed. Exploratory factor analysis revealed that a total of five components—self-directed projects (29.58%), e-content (13.00%), online presentation (10.97%), face-to-face interactions (9.12%), and text interactions (7.11%) had the highest load, with a total of 69.77% of the structure explaining virtual education preferences. The factor analysis test and the suitability of the sample are both confirmed by the value of KMO = 0.721 and the significance of Pvalue < 0.001. Conclusion It appears that the highly valid tool developed can be used to ascertain the educational preferences of students. Also, the high factor load of self-directed educational methods and e-content shows that independence and flexibility in time and place are more important for students.