A location-based privacy-preserving m-learning model to enhance distance education in Kenya

Abstract

Developments in mobile learning have seen the adoption of high-power, location-aware mobile gadgets in distance education. Unauthorized user’s location data collected by these devices could hamper sustainable adoption of m-learning systems. There is the need, therefore, to develop a secure location-based privacy-preserving model to evaluate learners’ behavioral intention to use location-aware mobile systems for distance education. The study employed descriptive design, and using a questionnaire, data were collected from a population enrolled for distance learning. Using SPSS version 20 and WarpPLS 5.0, data were statistically analyzed to validate or refute the intended objectives. The model would provide the university management with informed approach to consider privacy-preserving aspects in m-learning implementation. It will provide enlightened guidance to mobile-learning-application developers on the need to cater for learners’ privacy.