Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment

This open access repository houses the dataset utilized in the research article: Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666 The repository includes these files: "submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns: "user_id": The unique identifier for each student who submitted the assignment. "task_id": The unique identifier for each task that received submissions. "submission_seconds": The number of seconds since the first user accessed the initial task's description within the system. "correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect). "subject": The specific subject matter that the task addresses. "subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns: "subject_id": The unique identifier for each subject. "subject": The actual name of the subject. This research was funded in whole or in part by the National Science Centre, Poland, grant number: 2021/05/X/ST6/01786