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 an online questionnaire administered via Google Forms. The questionnaire captured demographic characteristics (e.g., gender, country, years of teaching experience, academic position, area of specialization, technology usage, and frequency of use of technology in teaching) along with construct-based measures 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. Responses were stored in an Excel file, systematically coded, cleaned, anonymized, and analyzed using Python. Descriptive statistics (e.g., mean, standard deviation, skewness, and kurtosis) are provided to support basic distribution checks and dataset reuse. Internal consistency was assessed using Cronbach’s alpha for each construct, and a correlation matrix is included to document associations among constructs and support reuse in comparative or modeling studies. As the dataset represents a single cross-sectional measurement window, it should be treated as baseline data that can support future longitudinal follow-up only if the same instrument is re-administered in additional waves.
Institutions
- Amrita Vishwa Vidyapeetham - Amritapuri CampusKerala, Amritapuri