Abstract
The focus of this research is to develop a new technology based on OLAP and SVM algorithm to evaluate the quality of university teaching. The main goal of this study is to find the best technology to measure the quality of teaching. This study will use descriptive statistics, correlation coefficient, regression analysis and cluster analysis and other data analysis techniques. The final results will be used to develop an effective evaluation system, which can measure the effectiveness of University Teachers’ contributions to students’ learning. In the research, we try to use OLAP and SVM algorithms to improve the quality of teaching evaluation. First, we used a large number of student data from different universities in China. We collect data from more than 20 universities every semester. Then, we build a large OLAP database based on this data set (about 1million records), which can be used as the input of SVM algorithm. Next, we designed three types of artificial neural network (ANN) models based on previous research. This method can be used for both undergraduate and graduate students. The results of this study will help universities evaluate their teaching quality, which will enable them to improve their teaching quality.
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Xu, M. (2023). University Teaching Quality Evaluation Technology Based on OLAP and SVM Algorithm. In: Hung, J.C., Chang, JW., Pei, Y. (eds) Innovative Computing Vol 2 - Emerging Topics in Future Internet. IC 2023. Lecture Notes in Electrical Engineering, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-99-2287-1_84
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DOI: https://doi.org/10.1007/978-981-99-2287-1_84
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