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We conducted a controlled study to investigate whether having students choose the concept on which to solve each practice problem in an adaptive tutor helped improve learning. We analyzed data from an adaptive tutor used by introductory programming students over three semesters. The tutor presented code-tracing problems, used pretest-practice-post-test protocol, and presented line-by-line explanation of the correct solution as feedback. We found that choice did not in-crease the amount of learning or pace of learning. But, it resulted in greater improvement in score on learned concepts, and the effect size was medium.
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This content will become publicly available on February 12, 2026
Facilitating Student's Learning Transfer in a Database Programming Class
Transferring programming skills learned in the classroom to diverse real-world scenarios is both essential and challenging in computing education. This experience report describes an approach to facilitate learning transfer by fostering adaptive expertise. Students were engaged in co-creating contextualized worked-out examples, including step-by-step solutions. Through three homework assignments in a Spring 2023 database programming course, we observed substantial improvements, where students generated detailed and accurate solutions and enriched their problem-solving contexts from simple phrases to detailed stories, drawn from 17 real-life scenarios. Our results also suggest that the peer assessment process cultivated a supportive learning environment and fostered adaptive expertise. We discuss the lessons learned and draw pedagogical implications for integrating student-generated contextualized materials in other programming courses.
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- PAR ID:
- 10588589
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400705311
- Page Range / eLocation ID:
- 1330 to 1336
- Format(s):
- Medium: X
- Location:
- Pittsburgh PA USA
- Sponsoring Org:
- National Science Foundation
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