Evaluation of Physical Education Teaching Quality in Colleges Based on the Hybrid Technology of Data Mining and Hidden Markov Model In order to further improve the teaching quality evaluation accuracy of physical education(abbreviated as PE) curriculum in colleges, this study conducts an indeep research on the overall evaluation of PE teaching effect in colleges from the aspects of teachersxe2x80x99 teaching ability and studentsxe2x80x99 learning effect based on the hybrid technology of data mining and hidden Markov model. First of all, this study analyzes the development status of the teaching quality evaluation system of PE curriculum in colleges; Secondly, it analyzes the applicability of data mining technology and hidden Markov model to the evaluation of PE teaching quality in colleges, and proposes a mathematical model for evaluating the quality of PE teaching in colleges; Finally, this study carries out a series of experiments on the basis of mathematical models, and analyzes the experimental results in depth. The experimental analysis shows that the model proposed in this paper is helpful to improve the accuracy of PE teaching quality evaluation in colleges. The research results of this study provide a useful exploration for the integration of computing technology and language teaching. At the same time, it provides a reference path and implementation model for improving the teaching of PE for graduates in colleges through machine learning technology.

Analyzing Student Performance in Programming Education Using Classification Techniques In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 u0026 J48 Decision Tree Algorithms to analyze these data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm.  The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search method and the experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled Introduction to Computer Programming, while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students.