A Cross-National Dataset on Teacher Perceptions and Adoption of Virtual Laboratories in Higher Education
Description
The dataset comprises faculty perceptions regarding the adoption, implementation, and pedagogical impact of virtual laboratories in higher education from six countries in the Global South. With responses from 1738 teachers from Bangladesh, India, Kenya, Malaysia, the Republic of Maldives, and Sri Lanka, it captures various constructs measuring key factors such as training and professional development, usage behavior, behavioral intention to use, institutional support, perceived ease of use, perceived usefulness, user proficiency and self-efficacy, cognitive load, and learning outcomes in addition to their demographic information (such as gender, country, years of teaching experience, academic position, area of specialization, technology usage, and frequency of use of technology in teaching). Data were analyzed using Python to generate descriptive statistics, skewness, kurtosis, and reliability measures. The dataset is provided in .csv format, enabling further statistical and cross-cultural analysis to explore technology-enhanced laboratory practices in diverse educational contexts.
Files
Steps to reproduce
The dataset was generated through the distribution of an online questionnaire administered via Google Forms. The questionnaire captured demographic characteristics (such as gender, country, years of teaching experience, academic position, area of specialization, technology usage, and frequency of use of technology in teaching) along with constructs related to Training and Professional Development, Usage Behaviour, Behavioural Intention to Use, Institutional Support, Perceived Ease of Use, Perceived Usefulness, Self-Efficacy, Cognitive Load, and Learning Outcomes. The collected responses were stored in an Excel file, systematically coded, and analyzed using Python. Descriptive statistical analyses were performed to compute measures such as mean, standard deviation, skewness, and kurtosis, providing insights into data distribution and normality. Reliability analysis was conducted using Cronbach’s alpha to assess the internal consistency of the constructs. Furthermore, a correlation matrix was generated to examine interrelationships among the identified factors, followed by analysis of variance (ANOVA) to determine significant differences across demographic groups. The cleaned and anonymized dataset enables researchers to replicate the analysis or extend it for comparative, cross-cultural, or longitudinal studies on experimental learning adoption in higher education.
Institutions
- Amrita Vishwa Vidyapeetham - Amritapuri Campus