An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder
Gerhard Johann Hagerer, Laura Lahesoo, Miriam Ansch\"utz, Stephan, Krusche, Georg Groh

TL;DR
This study analyzes how introducing autograders in large programming courses affects student perceptions, suggesting improvements in interaction, course quality, and satisfaction, based on pre- and post-implementation evaluation data.
Contribution
It provides a qualitative analysis of the impact of autograders on student evaluations, offering hypotheses for future quantitative research.
Findings
Improved tutor-student interactions
Enhanced overall course quality
Increased student satisfaction
Abstract
Commonly, introductory programming courses in higher education institutions have hundreds of participating students eager to learn to program. The manual effort for reviewing the submitted source code and for providing feedback can no longer be managed. Manually reviewing the submitted homework can be subjective and unfair, particularly if many tutors are responsible for grading. Different autograders can help in this situation; however, there is a lack of knowledge about how autograders can impact students' overall perception of programming classes and teaching. This is relevant for course organizers and institutions to keep their programming courses attractive while coping with increasing students. This paper studies the answers to the standardized university evaluation questionnaires of multiple large-scale foundational computer science courses which recently introduced…
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Taxonomy
TopicsOnline Learning and Analytics · Teaching and Learning Programming · Innovative Teaching Methods
