Automatic Rules Generation for Teaching Strategies Improvement

Abstract

Data mining of students' scores can optimize teaching strategies and improve the teaching quality continuously. However, there are two problems in the current researches of teaching data mining. Firstly, most researchers focus on students' performance among multiple courses, but these data are relatively rough, and with little help for improving specific teaching strategies (e.g., using different teaching and assessment methods) of a single course. Secondly, students' performance not only depends on their efforts, but also is affected by the effectiveness of the assessment methods made by teachers, so it is impossible to have accurate improvement without considering factors from both teacher and students' sides. This paper takes the students' scores of computer network course as the sample, uses k-means to cluster students' scores from different assessment methods based on fine grained teaching contents, in order to get more accurate teaching improvement strategies later. In addition, an improved Apriori algorithm is proposed with the quality of assessment methods taken into consideration, for association rules selection and teaching strategies improvement. According to the experiments, our method can take into account the diversity of course assessment methods and the influence of both teaching and learning factors, and the accuracy of the improved rules is 18.8% higher than that current Apriori algorithm based on interest.