Adaptive Clustering of Codes for Assessment in Introductory Programming Courses

Despite the importance of introductory programming disciplines, it is quite common to find problems related to academic students performance. In such environments, we easily find unmotivated students with some doubts and that do not understand basic programming concepts. Monitoring each of the students is not trivial because the number of students is high and, to do so, it would be necessary to observe many characteristics of each code submitted for practical activities. The teacher, even when helped by TAs (Teacher Assistants), is not able to perform the reviews quickly, for this activity requires a huge amount of time. Fast feedback is extremely important to enable the learning of any concept. In this research, we investigate an adaptive approach to cluster codes in order to minimize the effort of evaluation. The results vary from reasonable to perfect concordances, considering the semiautomatic evaluations obtained with the clustering and the expert evaluations.