Datasets Comparison

This dataset contains survey data collected from undergraduate students to examine the relationships between online learning misfit, technostress, academic performance, learning satisfaction, and learning motivation in technology-enhanced learning environments. The study aims to investigate how perceived misfit between students’ technological competencies and the demands of digital learning environments influences learning outcomes and whether technostress mediates these relationships. Data were collected through a structured questionnaire using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire includes five constructs: Online Learning Misfit (MF), Technostress (TS), Academic Performance (PE), Learning Satisfaction (SAT), and Learning Motivation (MO). The measurement items were adapted from validated scales reported in previous studies on person–environment fit, technology-enhanced learning, technostress, student satisfaction, motivation, and academic performance. The dataset also contains demographic information, including gender, year of study, academic major, and area of residence prior to university enrollment. The target population consists of university students, with particular emphasis on teacher education students who frequently engage in technology-enhanced learning activities. This dataset may be useful for researchers, educators, educational technology specialists, and policymakers interested in understanding the challenges associated with digital learning adoption in higher education. The data can support analyses using structural equation modeling (SEM), mediation analysis, psychometric validation, and comparative studies across student groups and educational contexts. The dataset contributes to the growing literature on technology-enhanced learning by providing empirical evidence on how online learning misfit and technostress influence students’ academic outcomes, satisfaction, and motivation in contemporary higher education settings.

Data were collected through a cross-sectional survey of undergraduate students enrolled in Vietnamese universities. A structured questionnaire was developed based on validated scales from previous studies on online learning misfit, technostress, academic performance, learning satisfaction, and learning motivation. The questionnaire consisted of two sections. The first section collected demographic information, including gender, year of study, academic major, and area of residence before university enrollment. The second section measured five latent constructs: Online Learning Misfit (MF), Technostress (TS), Academic Performance (PE), Learning Satisfaction (SAT), and Learning Motivation (MO). All items were assessed using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The survey was distributed electronically through online platforms. Participation was voluntary and anonymous. No personally identifiable information was collected. Completed responses were screened for missing values and response quality before analysis. The dataset can be analyzed using descriptive statistics, reliability analysis, confirmatory factor analysis, and structural equation modeling (SEM) or partial least squares structural equation modeling (PLS-SEM). Researchers may replicate the study by administering the same questionnaire to university students and examining the mediating role of technostress in the relationships between online learning misfit and learning outcomes. The dataset includes raw survey responses, variable coding information, and measurement items used in the study.

Educational Technology, Teacher Education, Higher Education, Online Learning

Creative Commons Attribution 4.0 International

This dataset contains survey data collected from undergraduate students to examine the relationships between online learning misfit, technostress, academic performance, learning satisfaction, and learning motivation in technology-enhanced learning environments. The study aims to investigate how perceived misfit between students’ technological competencies and the demands of digital learning environments influences learning outcomes and whether technostress mediates these relationships. Data were collected through a structured questionnaire using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire includes five constructs: Online Learning Misfit (MF), Technostress (TS), Academic Performance (PE), Learning Satisfaction (SAT), and Learning Motivation (MO). The measurement items were adapted from validated scales reported in previous studies on person–environment fit, technology-enhanced learning, technostress, student satisfaction, motivation, and academic performance. The dataset also contains demographic information, including gender, year of study, academic major, and area of residence prior to university enrollment. The target population consists of university students, with particular emphasis on teacher education students who frequently engage in technology-enhanced learning activities. This dataset may be useful for researchers, educators, educational technology specialists, and policymakers interested in understanding the challenges associated with digital learning adoption in higher education. The data can support analyses using structural equation modeling (SEM), mediation analysis, psychometric validation, and comparative studies across student groups and educational contexts. The dataset contributes to the growing literature on technology-enhanced learning by providing empirical evidence on how online learning misfit and technostress influence students’ academic outcomes, satisfaction, and motivation in contemporary higher education settings.

Data were collected through a cross-sectional survey of undergraduate students enrolled in Vietnamese universities. A structured questionnaire was developed based on validated scales from previous studies on online learning misfit, technostress, academic performance, learning satisfaction, and learning motivation. The questionnaire consisted of two sections. The first section collected demographic information, including gender, year of study, academic major, and area of residence before university enrollment. The second section measured five latent constructs: Online Learning Misfit (MF), Technostress (TS), Academic Performance (PE), Learning Satisfaction (SAT), and Learning Motivation (MO). All items were assessed using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The survey was distributed electronically through online platforms. Participation was voluntary and anonymous. No personally identifiable information was collected. Completed responses were screened for missing values and response quality before analysis. The dataset can be analyzed using descriptive statistics, reliability analysis, confirmatory factor analysis, and structural equation modeling (SEM) or partial least squares structural equation modeling (PLS-SEM). Researchers may replicate the study by administering the same questionnaire to university students and examining the mediating role of technostress in the relationships between online learning misfit and learning outcomes. The dataset includes raw survey responses, variable coding information, and measurement items used in the study.

Educational Technology, Teacher Education, Higher Education, Online Learning

Creative Commons Attribution 4.0 International