Investigating factors affecting on medical sciences students’ intention to adopt mobile learning

Abstract

AbstractBackgroundMobile learning (m-learning) provides a good opportunity for students’ lifelong learning. The design and implementation of effective and successful mobile learning requires identification of factors that affect m-learning. The aim of this study was to investigate the factors that affect the intention of students of medical sciences to adopt mobile learning based on theory of planned behavior (TPB).MethodsIn this cross-sectional study, 332 students of medical sciences were randomly selected. The study tool was a based a questionnaire that had been designed based on TPB model. Descriptive statistics (mean, standard deviation, frequency and percentage) were calculated. In order to determine the standardized factor loading and assess the study hypotheses, structural equation modeling was used. Composite reliability, average variance extracted, and standardized factor loading were used to determine the convergent validity.ResultsThe mean of mobile learning readiness was 3.59 ± 0.83. Among the TPB structures, the structures of attitude (β = 0.525) and behavioral control (β = 0.318) had positive and significant effect on the intention to adopt m-learning (P ≤ 0.01). However, the structure of subject norm did not have a significant effect on the intention to adopt m-learning. In general, attitude, behavioral control and subject norm structures were 0.675 determinants of the intention to adopt m-learning (r2 = 0.675).ConclusionsIn this study Mobile learning readiness of the students was at moderate level. Also the results indicated Positive and significant effect of attitude and behavioral control on the intention of students to accept m-learning. The TPB-based model was a suitable model for identifying psychological factors that affect the intention of students of medical sciences to adopt m-leaning. In order to increase the students’ acceptance of mobile learning, we suggest that, other psychological, behavioral, social, and cultural factors that affect the acceptance of m-learning should be identified. Educational programs are also suggested to be introduced to students to familiarize them with the m-learning and its application in learning process.