ERIC Number: EJ1460813
Record Type: Journal
Publication Date: 2025-Mar
Pages: 20
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0007-1013
EISSN: EISSN-1467-8535
Available Date: 2024-12-02
Providing Tailored Reflection Instructions in Collaborative Learning Using Large Language Models
Atharva Naik1; Jessica Ruhan Yin1; Anusha Kamath1; Qianou Ma1; Sherry Tongshuang Wu1; R. Charles Murray1; Christopher Bogart1; Majd Sakr1; Carolyn P. Rose1
British Journal of Educational Technology, v56 n2 p531-550 2025
The relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well-established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to design database access strategies. Forty-four teams were randomly assigned to three reflection conditions ([GEN] directive to generate a contrasting case to the student solution and evaluate their trade-offs in light of the principle, [CONT] directive to compare the student solution with a provided contrasting case and evaluate their trade-offs in light of a principle, and [NSI] a control condition with a non-specific directive for reflection evaluating the student solution in light of a principle). In the CONT condition, as an illustration of the use of LLMs to exemplify knowledge transformation beyond knowledge construction in the generation of an automated contribution to a collaborative learning discussion, an LLM generated a contrasting case to a group's solution to exemplify application of an alternative problem solving strategy in a way that highlighted the contrast by keeping many concrete details the same as those the group had most recently collaboratively constructed. While there was no main effect of condition on learning based on a content test, low-pretest student learned more from CONT than GEN, with NSI not distinguishable from the other two, while high-pretest students learned marginally more from the GEN condition than the CONT condition, with NSI not distinguishable from the other two.
Descriptors: Cooperative Learning, Reflection, College Students, Computer Science Education, Artificial Intelligence, Natural Language Processing, Problem Solving
Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://bibliotheek.ehb.be:2191/en-us
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: DSES2222762
Author Affiliations: 1Language Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA