Using image recognition to automatically assess programming tasks with graphical output
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Using image recognition to automatically assess programming tasks with graphical output Eerik Muuli 1 & Eno Tõnisson 1 Tauno Palts 1 & Reelika Suviste 1
Lepp 1 & Piret Luik 1 1 1 & Kaspar Papli & Merilin Säde & Marina
&
Received: 22 January 2020 / Accepted: 4 May 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords MOOC . Programming . Automatic assessment . Image recognition
1 Introduction The number of MOOCs (Massive Open Online Courses) and their participants is increasing year by year (Shah 2018). Teaching and studying programming via
* Eerik Muuli [email protected] Extended author information available on the last page of the article
Education and Information Technologies
MOOCs have become very popular (Gardner and Brooks 2018). High-quality tasks are required in order to provide the best environment for learning. Due to the large amount of participants and task solutions automated assessment is essential in programming MOOCs (Alcarria et al. 2018; Huisman et al. 2016; Pieterse 2013). The produced output and/or the program’s code can be checked in order to automatically assess programming tasks. The assessable output is usually textual and, in our MOOC, “Introduction to programming,” most of the tasks have textual output as well. As programs with graphical output or graphical user interface provide impressivelooking results and are attractive to students (Douce et al. 2005), we decided to add some programming assignments that would produce a graphical output from the code
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