SPSE: A Smart Phone-Based Student Evaluation

Abstract

Data center in the university collects lots of students’ achievement data. It is very important to improve the teaching performance through in-depth analyzing those students’ data. However, traditional methods only pay attention to the analysis of static students’ data such as mid-term and final scores, and ignore the analysis of students’ daily behavior data. Data mining of students’ daily behavior data becomes a key step to avoid students’ failure and further improve students’ performance. This paper proposes a smart phone-based method for evaluating students’ performance (SPSE). First, a static student score is calculated using fuzzy-based method for evaluating student academic performance. Then, we use Affinity Propagation clustering algorithm to analyze WiFi data and time stamp collected by student smart phones in order to obtain student locations and learning status. Based on that we set dynamic students’ behavior scores. Finally, we combine the two scores to comprehensively evaluate students’ performance.