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.

Analysis and Visualization of Patterns in Answers to Programming Problems It is not an easy task for teachers to make studentsu0027 answers for programming exercises. In scoring such a student program, the teacher needs to evaluate not only the degree of program achievement but also which way the program achieve the requirements. In order to solve such problems, we propose a supporting method to evaluate the program using prog2vec, which is software that converts a program into its feature vector representation. Specifically, our method estimates whether a programming problem needs certain ways to achieve its purpose or not. The input of the method is a set of the programs that are made as answer for the problem. If the method estimates that specific ways are needed to achieve the given problem, it also visualizes these programing ways in structural graph representation. In this way, our proposed method helps teachers that have a lot of and various answers for a programming problem to define evaluation criteria for those answers.