Understanding tutor learning: Knowledge-building and knowledge-telling in peer tutors' explanations and questions
Abstract
Prior research has established that peer tutors can benefit academically from their tutoring experiences. However, although tutor learning has been observed across diverse settings, the magnitude of these gains is often underwhelming. In this review, the authors consider how analyses of tutors’ actual behaviors may help to account for variation in learning outcomes and how typical tutoring behaviors may create or undermine opportunities for learning. The authors examine two tutoring activities that are commonly hypothesized to support tutor learning: explaining and questioning. These activities are hypothesized to support peer tutors’ learning via reflective knowledge-building, which includes self-monitoring of comprehension, integration of new and prior knowledge, and elaboration and construction of knowledge. The review supports these hypotheses but also finds that peer tutors tend to exhibit a pervasive knowledge-telling bias. Peer tutors, even when trained, focus more on delivering knowledge rather than developing it. As a result, the true potential for tutor learning may rarely be achieved. The review concludes by offering recommendations for how future research can utilize tutoring process data to understand how tutors learn and perhaps develop new training methods.
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Effective tutors enhance learning outcomes by prompting students to generate their own responses during tutoring sessions.
“Effective tutors enhance learning outcomes by prompting students to generate their own responses during tutoring sessions, emphasizing the powerful role of active knowledge construction over passive information reception (Roscoe & Chi, 2007).”
From Assigning AI: Seven Approaches for Students, with Prompts (2023) · cites this paper as Roscoe & Chi, 2007
Cites
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“These results are paralleled by research on “teaching efficacy” (Tschannen-Moran & Hoy, 2001) showing that classroom teachers’ sense of efficacy in teaching is associated with more persistence and openness to innovation in the classroom.”
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“Prior findings suggest that without this human selection, LMs tend to default to repetitive strategies, whereas experienced educators employ a richer and more diverse set of strategies in their teaching (Wang et al., 2024a).”
Shahriar, T., & Matsuda, N. (2024) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-64299-9_2 · Cited by 4 · see citing works
- Authors
- Roscoe, R. D., & Chi, M. T. H.
- Affiliations
- University of Pittsburgh (US)
- University of Pittsburgh
- ORCID
- Roscoe, Rod D.
- Venue
- Review of Educational Research
- Published
- 2007-12
- Volume
- 77
- Issue
- 4
- Pages
- 534-574
- ISSN
- 0034-6543 (ISSN-L)
- 1935-1046
- OpenAlex
- W2105507325
- Cited by
- 647
- References
- 5
- Citations by year
- 2026: 43 · 2025: 43 · 2024: 32 · 2023: 31 · 2022: 32 · 2021: 58 · 2020: 35 · 2019: 40 · 2018: 45 · 2017: 22 · 2016: 39 · 2015: 46 · 2014: 35 · 2013: 32 · 2012: 43
- FWCI
- 26.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Review of Educational Research (publishedVersion)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Social Sciences › Education › Evaluation of Teaching Practices
- Keywords
- peer tutoring, knowledge construction, elaboration, tutor training
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