Reserch On Algorithm Knowledge Feature Model Based On Autonomous Mental Development

There are lots of source codes on the Internet, which are resources valuable for programming tutoring. In school programming training, for instance, teachers may want to know how many solutions students could use to solve a given problem and which solution is the most popular. For this reason, we propose a method which is based on self-organizing maps to discover the mainstream solution for each problem by mining its corresponding source codes submitted by learners. We believe that the problem that has many solutions is much suitable for programming learners and that the mainstream solution should be recommended.1510source codes submitted by40students for60problems were mined in our experiment. The results show that each problem has3.26solutions on average and that90%problems have their unique mainstream solutions.Algorithm recognition is concerned with program understanding. In the past decades, several approaches have been studied in this area, but most of them are based on a library where predefined templates are stored. Such template-based approaches encounter an obstacle that it is difficult to know how many templates are required to recognize a given algorithm in advance. To avoid this obstacle, we apply the idea of autonomous mental development (AMD) to algorithm recognition.