A prescriptive specialized learning management system for academic feedback towards improved learning

Abstract

The dynamic nature of technological advances is causing changes in many fields and especially in tertiary education. Student numbers are increasing annually and institutions need to maintain education quality whilst ensuring student retention. A specialized learning management system (SLMS) was developed in this study that provides students with comprehensive feedback which will enable them to better manage their academic performance. It will also assist institutions/lecturers in identifying at-risk students early in a semester to facilitate retention. The system uses a prescriptive analytics engine implemented by means of mathematical modelling techniques together with an algorithmic approach to process academic student data in realtime. Feedback is delivered timely and is comprehensive in the sense that it presents students with individualized instructions towards improvement in a module. The system was implemented in a field test and evaluated according to validation criteria established from a literature study on related research efforts. A survey was conducted to measure user response in terms of the identified factors. The results showed that the SLMS conforms to the attributes essential to an action-recommender system and was favorably accepted by the target users