Generating Expression Evaluation Learning Problems from Existing Program Code
Oleg Sychev, Nikita Penskoy, Artem Prokudin · 2022 International Conference on Advanced Learning Technologies (ICALT) · 2022
When developing automated assessments and intelligent tutoring systems, a lot of routine effort goes into developing the bank of learning problems. Problem generation is the way to automate this process. In this paper, we present a method of generating expression-related problems for teaching introductory programming courses. The problems are generated from open-source software code which allows keeping learning problems similar to the production code the students should learn to analyze and write. Generated problems are automatically classified by their difficulties and the knowledge they need to solve, represented as sets of possible errors. This allows seamless integration with adaptive learning algorithms. The evaluation showed that the generated problems are indistinguishable from human-authored problems and suitable for use in the educational process.