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

<p>This open access repository houses the dataset utilized in the research article:</p><p>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</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p>&nbsp;</p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p>&nbsp;</p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p>&nbsp;</p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p>&nbsp;</p>