Learner characteristics, behaviour, technology use and adoption of mobile learning among community health care trainees, Amref Health Africa, Kenya.

Abstract

Mobile technology has gained increased focus in academic circles as a way of enabling learning that is not confined to time and place. As the benefits of mobile learning (mLearning) are being clarified so too will researchers need to understand the determinants of its adoption by the end user. The adoption of mobile technology for teaching and learning largely depends on whether learners believe that it fits in their particular learning needs. However, despite the interest and the potential of mobile learning, researchers have a limited knowledge of the factors that may influence learner adoption. Investigating learner adoption of mobile learning is an essential issue in the expansion of mLearning. This research explores learner behaviour, technology use and adoption of mobile learning among trainees in the mHealth training programme. This study based its unit of analysis on the mHealth programme run by Amref Health Africa in Kenya. The study objectives were to determine the influence of learner characteristics, self-efficacy, attitude, technology use on adoption of mLearning. The study adopted a mixed methods research approach. This involved combining of qualitative and quantitative data in the study. The study was guided by the pragmatism paradigm. This paradigm was selected because it applies to mixed methods arguing that inquirers draw liberally from both quantitative and qualitative assumptions when they engage in the research. The target population of the study was the 3081 trainees of the two phases of mHealth programme. To achieve the expected threshold for a sample size, the researcher draws the sample size using the formula suggested by Yamane (1967) for calculating sample sizes. A 95% confidence level and P = 0.5 are assumed for the equation 𝑛=𝑁/1+𝑁(𝑒)2. The application of this formula yielded a sample of 354 for this study. The data was collected from six counties of the thirteen counties where the mHealth programme took place stratified as follows; urban (Nairobi and Kisumu) rural (Kakamega and Kitui) nomadic (Kajiado, and Samburu). The data was collected using by questionnaires, focused group discussions and interviews. The instruments were piloted and Cronbach's Alpha (α) used to test for internal consistency. The instruments were found to be reliable with a reliability index above .70. Validity of the instruments was checked by the supervisors and other research experts at the ODeL campus of the University of Nairobi. Both inferential and descriptive statistics were used for data analysis. Frequencies, percentages, means and standard deviations were used to describe key variable outcomes while regression analysis and Pearson correlation was conducted to test the hypothesis. The qualitative data was interpreted and presented thematically. The results showed that age and gender did not have a significant influence on adoption of mLearning while level of education, work experience and period of exposure to mLearning all influenced adoption. Overall, learner self-efficacy, attitude, behavioural intention and technology use had a positive and significant influence on adoption of mobile learning. Institutional factors were also found to have a significant influence on adoption of mLearning. The study concludes that adoption of mLearning is best influenced by collective determinants other than isolated determinants. The findings of this study are useful in providing guidance to mLearning content developers, researchers, practitioners and educators for designing mLearning courses that are learner friendly and thus may lead to higher adoption. The study recommends that institutions wishing to offer mLearning need to ensure that their mobile learning solutions address the determinants of adoption collectively for better results in adoption of mLearning.