Psychometric validation of a mobile learning instrument for macroalgae instruction and perceptions of Indonesian prospective biology teachers

Digital learning in higher education holds considerable promise for enhancing instructional effectiveness, particularly for domain-specific topics such as macroalgae, which are abundant in Indonesia’s marine environments. To inform the design of contextually relevant mobile learning, a questionnaire is needed to capture the perceptions of prospective biology teachers’. This study developed a questionnaire to examine the factor structure and reliability of measuring perceptions of mobile learning for macroalgae, and analyzed the perceptions of the respondents. A cross-sectional survey was administered to 640 prospective biology teachers’ across multiple Indonesian universities (62.5% female and 37.5% male). The sample was partitioned into independent subsamples for analysis: descriptive (n = 120), exploratory factor analysis (EFA; n = 300), confirmatory factor analysis (CFA), and perception analysis (n = 220). EFA (principal axis factoring; varimax rotation) reduced an initial 32 items to a 17-item solution organised into four interpretable dimensions: Instructional Quality and Skills Development, Understanding and Learning Experience, Local Relevance and Empowerment, and Mobile Learning Readiness and Entrepreneurial Orientation. CFA (AMOS v23) confirmed the four-factor structure, and dimension-level indices met conventional criteria for reliability and convergent validity. Overall, respondents expressed strong support for contextualised, mobile-based learning and held a positive perception toward integrating digital technologies with local macroalgae biodiversity, as seen with the average values for each dimension being MLEO 13.29, LRE 12.34, ULE 12.92, and IQSD 32.39. The validated instrument offers a practical tool for needs-driven design of mobile learning resources tailored to prospective biology teachers. Future research should cross-validate the instrument in more geographically and institutionally diverse samples and evaluate the instructional effectiveness of the resulting mobile learning interventions.

Confirmatory factor analysis · Descriptive statistics · Educational technology · Exploratory factor analysis · Indonesian · Perception · Varimax rotation · E-Learning and COVID-19 · Innovations in Aquaponics and Hydroponics Systems · Mobile Learning in Education