A cross platform algorithm for learning management systems

Abstract

Technological ubiquity has proliferated in our midst to the extend it is part of our daily lives; academics not exempted. The needs for connectivity, convergence of technological infrastructure and cross platform interoperability have necessitated the development of cross platform algorithms to aggregate information and manage tasks in our environments. This algorithm examined the design features of two learning management systems Claroline and ATutor; designed, developed, implemented, and evaluated the functionality of a cross platform algorithm in those two learning environments. The successful evaluation of the functionality of the algorithm was of immense benefit to e-commerce platforms. The development approach was agile with strict adherence to extreme programming software development life cycle. The study environment were the two learning management systems configured at Kibabii University, in such a way that a team of Information Technology students utilized the cross platform algorithm enhanced Claroline while the Computer Science team worked with the plain ATutor platform. Questionnaires were distributed to both groups of students to evaluate and ascertain the system’s functionality. 72.8% of the respondent’s returned a positive rating. In conclusion the system worked well with the need to incorporate more features from the two LMSs, such as notifications from chats and forums on topical issues trending and are of interest to the user according to his/her defined profile. It is our recommendation that the functionality be extrapolated into the mobile bound applications such as WhatsApp, since the environment today is moving towards the mobile environment.