Big data collaborative filtering-based framework for improving education policies
Amal Alhosban, Charlotte Tang · 2017
The development of educational policies that improve educational systems needs intelligent automated tools for decision making. This need has motivated the development of a new system which can help teachers, schools, and decision makers in enhancing students' performance. The primary concern here is to monitor the students and help the teachers in building action plans that improve students' performance and solve their problems. In this paper, we propose a novel system using collaborative filtering based technology with students' medical history, economic situation, and the concept of `neighbors' in order to solve the students' performance related problems. The system combines collaborative filtering with clustering techniques to predict students' root factors (or causes) that may affect their future performance. We present experimental results from a large dataset that includes more than 10,000 students from 250 different schools. We deployed the new system for a period of 6 months and compared the results with the traditional approach. The system showed improvements in teachers' performance which reflected positively on students' performance.