Investigating the Effective Factors on Mobile Learning in Medical Education Based on FRAME Model

Introduction: With regard to an increase in use of modern communication technologies including mobile facilities and their application in learning and training, taking quality and users’ needs into account is a fundamental matter. In this article, an attempt has been made to investigate the factors influencing mobile learning from the perspective of M.S. and Ph.D. medical sciences students studying at Isfahan University of Medical Sciences based on FRAME model. Methods: In this descriptive study, the sample included 150 graduates M.Sc. and Ph.D. students of allied health medicine(Nursing , Midwifery and Hygiene fields ) in Esfahan University of Medical Sciences in 2013 that were selected through stratified random sampling. Data gathering tool was researcher-made questionnaire including 29 questions based on FRAME model. Face and content validity of the tool was confirmed and the reliability of internal consistency index (Cronbach’s Alpha) was 0.84 and finally we used descriptive statistics for data analysis. Results: The results showed that in learner component, the highest mean (3.85±1.08) related to learner's attitude towards mobile phone as a learning tool. In social component, the highest mean (3.24±1.04) belonged to the use of mobile phones for interaction with classmates. In interaction learning component, the highest mean (2.48±0.97) related to use of this device for transfer of ideas and information with other learners. In the usability of the device component, the highest mean (3.37±1.46) related to the use of cell phones for educational and research activities throughout the day. In social technology component, the highest mean (3.90±0.92) related to the use of electronic books in education. Finally in tool component, the results showed that learners prefer a flat touch screen mobile phone with high processing speed and having more than 3 GB memory in average size . Conclusion: With regard to the obtained results, designers of M-learning tools should take the users’ preferences into account and produce those tools, maximizing the efficacy of such learning type.