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From plans to actions: A process model for why feedback features influence feedback implementation

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Yong Wu at Beijing University of Posts and Telecommunications
Yong Wu
Christian D Schunn at University of Pittsburgh
Christian D Schunn
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Implementing peer feedback in revisions is a complex process involving first planning to fix problems and then actual implementing feedback through revisions. Both phases are influenced by features of the peer feedback itself, but potentially in different ways, and yet prior research has not examined their separate role in planning or the mediating role of planning in the relationship of feedback features and implementation. We build on a process model to investigate whether feedback features had differing relationships to plans to ignore or act on feedback versus actual implementation of feedback in the revision, and whether planning mediated the relationship of feedback features and actual implementation. Source data consisted of peer feedback comments received, revision plans made, and revisions implemented by 125 US high school students given a shared writing assignment. Comments were coded for feedback features and implementation in the revision. Multiple regression analyses revealed that having a comment containing a specific solution or a general suggestion predicted revision plans whereas having a comment containing an explanation predicted actual implementation. Planning mediated the relationship to actual implementation for the two feedback features predicting plans, suggestion and solution. Implications for practice are discussed.
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Vol.:(0123456789)
Instructional Science (2021) 49:365–394
https://doi.org/10.1007/s11251-021-09546-5
1 3
ORIGINAL RESEARCH
From plans toactions: Aprocess model forwhy feedback
features influence feedback implementation
YongWu1,2 · ChristianD.Schunn2
Received: 3 March 2019 / Accepted: 24 May 2021 / Published online: 25 June 2021
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
Implementing peer feedback in revisions is a complex process involving first planning to
fix problems and then actual implementing feedback through revisions. Both phases are
influenced by features of the peer feedback itself, but potentially in different ways, and
yet prior research has not examined their separate role in planning or the mediating role
of planning in the relationship of feedback features and implementation. We build on a
process model to investigate whether feedback features had differing relationships to plans
to ignore or act on feedback versus actual implementation of feedback in the revision, and
whether planning mediated the relationship of feedback features and actual implementa‑
tion. Source data consisted of peer feedback comments received, revision plans made, and
revisions implemented by 125 US high school students given a shared writing assignment.
Comments were coded for feedback features and implementation in the revision. Multi‑
ple regression analyses revealed that having a comment containing a specific solution or
a general suggestion predicted revision plans whereas having a comment containing an
explanation predicted actual implementation. Planning mediated the relationship to actual
implementation for the two feedback features predicting plans, suggestion and solution.
Implications for practice are discussed.
Keywords Feedback features· Implementation· Peer review· Planning· Revision
Introduction
Peer feedback involves students exchanging information about their performance with
the aim of narrowing the gap of their current performance and the desired performance
(Panadero etal., 2018; Shute, 2008). Peer feedback is increasingly included in a variety
of educational settings for different purposes (e.g., summative and/or formative purposes,
collaborative learning) (Kluger & DeNisi, 1996; Topping, 1998; van Gennip etal., 2010).
* Yong Wu
yongwu@pitt.edu
1 Center forResearch onTechnology‑Enhanced Language Education, School ofHumanities, Beijing
University ofPosts andTelecommunications, Beijing100876, China
2 Learning Research andDevelopment Center, University ofPittsburgh, 3939 O’Hara Street,
Pittsburgh, PA15217, USA
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Previous research showed that three of the four (weaknesses identification, suggestion, and example) were indeed common elements of peer feedback. Weaknesses were identified in between 24 and 45% of comments (Nelson & Schunn, 2009;Patchan et al., 2016;Wu & Schunn, 2020); general suggestions or specific solutions were identified in between 26 and 55% of comments (Jin et al., 2022;Nelson & Schunn, 2009;Patchan et al., 2018;Wu & Schunn, 2021b); and examples were identified in between 25 and 42% of comments (Nelson & Schunn, 2009;Patchan et al., 2016Patchan et al., , 2018. However, it is especially interesting that suggestions and examples actually were associated with decreases in comment length. ...
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Peer feedback can be highly effective for learning, but only when students give detailed and helpful feedback. Peer feedback systems often support student reviewers through instructor-generated comment prompts that include various scaffolding features. However, there is little research in the context of higher education on which features tend to be used in practice nor to which extent typical uses impact comment length and comment helpfulness. This study explored the relative frequencies of twelve specific features (divided into metacognitive, motivational, strategic, and conceptual scaffolds) that could be included as scaffolding comment prompts and their relationship to comment length and helpfulness. A large dataset from one online peer review system was used, which involved naturalistic course data from 281 courses at 61 institutions. The degree of presence of each feature was coded in the N = 2883 comment prompts in these courses. Since a given comment prompt often contained multiple features, statistical models were used to tease apart the unique relationship of each comment prompt feature with comment length and helpfulness. The metacognitive scaffolds of prompts for elaboration and setting expectations, and the motivational scaffolds of binary questions were positively associated with mean comment length. The strategic scaffolds of requests for strength identification and example were positively associated with mean comment helpfulness. Only the conceptual scaffold of subdimension descriptions were positively associated with both. Interestingly, instructors rarely included the most useful features in comment prompts. The effects of comment prompt features were larger for comment length than comment helpfulness. Practical implications for designing more effective comment prompts are discussed.
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... Students' perceptions of AWE feedback affect their engagement with AWE feedback (Zhang, 2020). When students perceive the feedback as being detailed and instructive, they are more inclined to incorporate it into their writing (Wu & Schunn, 2021). Students may choose not to respond to AWE feedback if they perceive the feedback counters their beliefs (Swain, 2006). ...
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... With regard to the contribution to the peer feedback research, these LLMs can be integrated with existing peer feedback implementation/learning improvement models (e.g., Wu & Schunn, 2020b;Wu & Schunn, 2021;Fong & Schallert, 2023). This integration will make it possible to evaluate peer feedback in real-time, providing immediate insights and predictions about learning improvements and knowledge gains (Lin et al., 2024), which is more timely compared to traditional post-class assessments. ...
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Peer feedback is a pedagogical strategy for peer learning. Despite recent indications of Large Language Models (LLMs) ' potential for content analysis, there is limited empirical exploration of their application in supporting the peer feedback process. This study enhances the analytical approach to peer feedback activities by employing state-of-the-art LLMs for automated peer feedback feature detection. This research critically compares three models—GPT-3.5 Turbo, Gemini 1.0 Pro, and Claude 3 Sonnet— to evaluate their effectiveness in automated peer feedback feature detection. The study involved 69 engineering students from a Singapore university participating in peer feedback activities on the online platform Miro. A total of 535 peer feedback instances were collected and human-coded for eleven features, resulting in a dataset of 5,885 labeled samples. These features included various cognitive and affective dimensions, elaboration, and specificity. The results indicate that GPT-3.5 Turbo is the most effective model, offering the best combination of performance and cost-effectiveness. Gemini 1.0 Pro also presents a viable option with its higher throughput and larger context window, making it particularly suitable for educational contexts with smaller sample sizes. Conversely, Claude 3 Sonnet, despite its larger context window, is less competitive due to higher costs and lower performance, and its lack of support for training and fine-tuning with researchers' data weakens its learning capabilities. This research contributes to the fields of Al in education and peer feedback by exploring the use of LLMs for automated analysis. It highlights the feasibility of employing and fine-tuning existing LLMs to support pedagogical design and evaluations from a process-oriented perspective.
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... For example, students may refuse to accept peer feedback because they do not perceive its accuracy or find it hard to comprehend (Zhan, 2021). Therefore, recent research emphasizes ways of motivating students to incorporate the feedback they receive (Wu & Schunn, 2021;Wichmann, Funk, & Rummel, 2018). ...
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The study examined the influence of feedback features on revision uptake in dialogic peer feedback activities, and the moderating effect of self-efficacy and prior knowledge on this relationship. Data were collected over a 10-week course at a comprehensive university in China, involving 29 students and resulting in 242 revision-oriented comments. To understand peer feedback features, we analyzed the feedback received by students in terms of cognition (identification, explanation, suggestion, or solution) and affect (positive, negative, positive-and-negative, or neutral). Binary logistic regression analysis revealed that: (1) explanation, suggestion and positive-and-negative evaluation negatively predicted revision uptake; (2) self-efficacy had a significant positive effect on revision uptake, and also played a role in moderating the relationship between explanation and uptake; (3) although prior knowledge could not directly predict revision uptake, it moderated the relationship between positive-and-negative evaluation and feedback uptake. These findings have instructional implications for designing and organizing peer feedback activities.
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... The effectiveness of peer feedback depends on its quality. High-quality feedback, characterized by praise, problem identification, solutions, and actionable advice, is more likely to be implemented by students (Wu & Schunn, 2020, 2021Banihashem et al., 2022). However, challenges include distrust in peers' competence to provide reliable feedback. ...
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Feedback plays a pivotal role in language acquisition and writing skill development. Despite its effectiveness in some contexts, traditional teacher-to-student assessment faces considerable limitations, particularly in large higher education classrooms where personalized feedback is scarce. In such settings, peer feedback has emerged as a viable and promising alternative. However, student scepticism towards its effectiveness presents a significant obstacle to its broader adoption. Negative attitudes, often rooted in doubts about peers’ competence or the value of their comments, can undermine the potential benefits of peer review. This paper revisits the findings of a quasi-experimental study conducted among 60 first-year students at Ibn Zohr University in Agadir, Morocco, which examined the impact of peer reviewing on writing development. A follow-up survey was employed to assess participants’ levels of trust in the feedback provided by peers. Based on the findings of this survey and their implications within the context of the study, this paper aims to offer pedagogical recommendations for improving the adoption and success of peer feedback through initial peer review training. Effective trust-building strategies are discussed, focusing on two fundamental types: communication trust and competence trust.
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... Moreover, existing digital tools developed to facilitate peer feedback generation were not based on a comprehensive definition of effective feedback features in writing. According to the literature (Kerman et al., 2022;Patchan et al., 2016;Wu & Schunn, 2021), writing feedback features include (1) affective (inclusion of positive emotions such as praise or compliments and negative emotions such as anger or disappointments), (2) description (summary statement of the essay), (3) identification (identification and localisation of the problem in the essay), (4) justification (elaborations and justifications of the identified problem) and (5) constructive (inclusion of recommendations and action plans for further improvements). However, in designing and developing digital tools for supporting peer feedback generation, many studies have overlooked essential features, such as neglecting the affective aspect, despite its known importance. ...
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... Peer feedback is in relation to peer review. Peer review, in a broader perspective, consists of rating and scores (i.e., peer assessment) and written or oral comments (i.e., peer feedback) (Wu & Schunn, 2021). Although studies (Boud et al., 1999;Boud & Bearman, 2024) pointed out that scoring in peer assessment inhibits cooperation, provision of comments in peer feedback enhances cooperation and collaboration to achieve the goal. ...
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... Critically, the quality of the feedback produced is key to the learning for both provider and recipient (K. Cho & MacArthur, 2011;van Popta et al., 2017;Wu & Schunn, 2021a, 2023aYu & Schunn, 2023). Nonetheless, investigations into the quality of peer feedback have shown that students often provide short, vague, and nonconstructive comments during peer feedback activities (Hovardas et al., 2014). ...
Which experiences are consistently associated with other-regulation of peer feedback length?
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Peer assessment has proven to have positive learning outcomes. Importantly, peer assessment is a social process and some claim that the use of anonymity might have advantages. However, the findings have not always been in the same direction. Our aims were: (a) to review the effects of using anonymity in peer assessment on performance, peer feedback content, peer grading accuracy, social effects and students’ perspective on peer assessment; and (b) to investigate the effects of four moderating variables (educational level, peer grading, assessment aids, direction of anonymity) in relation to anonymity. A literature search was conducted including five different terms related to peer assessment (e.g., peer feedback) and anonymity. Fourteen studies that used a control group or a within group design were found. The narrative review revealed that anonymous peer assessment seems to provide advantages for students’ perceptions about the learning value of peer assessment, delivering more critical peer feedback, increased self-perceived social effects, a slight tendency for more performance, especially in higher education and with less peer assessment aids. Some conclusions are that: (a) when implementing anonymity in peer assessment the instructional context and goals need to be considered, (b) existent empirical research is still limited, and (c) future research should employ stronger and more complex research designs.
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