A recommender for improving the student academic performance

There is a growing awareness among researchers about the apparent variations in the academic performance of students in tertiary institutions. Although, many studies have employed traditional statistical methods in identifying the factors responsible for the disparity, the statistical tool for setting a yardstick is yet to be established. Machine learning techniques have been employed as a paradigm in the modeling of students' academic performance in higher learning. However, they could be the springboard for improving prediction of students' academic performance. This work therefore aimed at designing a framework of intelligent recommender system, based on background factors, which can predict students' first year academic performance and recommend necessary actions for improvement. (C) 2015 The Authors. Published by Elsevier Ltd.