Extending the technology acceptance model to predict mobile learning adoption among tertiary education students in Botswana

A new paradigm shift from eLearning to mLearning will inevitably change the learning process. There is an immense proliferation of mobile technologies however in education these technologies are not fully utilised. Thus the key question that arises is that what are the factors which influence students to adopt mobile technologies in education? The purpose of the study is to extend and apply Technology Acceptance Model (TAM) as the theoretical framework to explain the determinants of mLearning adoption among tertiary education students in Botswana.

TAM is the most influential theory used to study the adoption of information systems. However, TAM has been criticised that it accounts for only 40% of the variance in intention to use. The study introduces mobile readiness, perceived mobility value, perceived privacy and perceived trust as external variab1es that reflect the student's belief in mobile learning adoption. The findings provide an in-depth knowledge derived from a theoretical model that assists in the successful adoption of mLearning. The study employed a mixed method strategy. Qualitative data was collected through group interviews followed by Quantitative data collected through a questionnaire. The empirical part of this study was conducted in July 2016. Four tertiary institutions were selected for this investigation.

lt was concluded that perceived trust, mobile readiness, perceived privacy and perceived mobility value are crucial factors influencing students to adopt mLearning technologies. The findings support the view that attitude and perceived usefulness are the key determinants of mLearning adoption. The findings imply that it is vital to sensitize students on the usefulness of these mLearning technologies before actual adoption as it helps to develop a positive attitude among the students. Future work should work on building a mLearning theory that encompasses all the dimensions of mLearning. The use of data mining tools should also be used to uncover complex patterns on the data.