Using image recognition to automatically assess programming tasks with graphical output

Abstract

There are thousands of participants in different programming MOOCs (Massive Open Online Courses) which means thousands of solutions have to be assessed. As it is very time-consuming to assess that amount of solutions manually, using automated assessment is essential. Since task requirements must be strict for the solutions to be automatically gradable, it often limits the types of different assignments and creativity. In order to promote more creativity we wanted to enable programming tasks with graphical output. In order to analyze and assess the creative tasks we developed, implemented and analyzed a system capable of assessing the graphical output of a solution program using image recognition. Image recognition is used to analyze the graphical output (image) produced by the solution program. The graphical output with a keyword attached to it is sent to an image recognition service provider that responds with a probability score. The solution is accepted or rejected based on the probability of a given object appearing in the image. The system was tested and evaluated in two runs of the MOOC “Introduction to Programming.” In the first run, we used the system to automatically assess the solutions of programming tasks on a predefined topic and in the second run on a topic chosen by the participant. The evaluation of the usefulness of the system and overview of participants’ feedback are presented as results. Suggestions for future improvements of the system and possible research are also listed.