View in EDS HTML Full Text PDF Full Text

Feedback Features and Revision Uptake in Dialogic Peer Feedback: The Moderating Effect of Self-Efficacy and Prior Knowledge

Saved in:
Bibliographic Details
Title: Feedback Features and Revision Uptake in Dialogic Peer Feedback: The Moderating Effect of Self-Efficacy and Prior Knowledge
Language: English
Authors: Keru Li, Yanyan Li (ORCID 0000-0002-8558-5911), Yansu Wang, Yunshan Chen, Wanqing Hu
Source: Instructional Science: An International Journal of the Learning Sciences. 2025 53(1):49-69.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 21
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, College Students, Peer Evaluation, Feedback (Response), Self Efficacy, Prior Learning, Error Correction, Positive Reinforcement, Negative Reinforcement, Learning Processes, Instructional Design
Geographic Terms: China
DOI: 10.1007/s11251-024-09690-8
ISSN: 0020-4277
1573-1952
Abstract: 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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1460935
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwF6QmR7fewVNQsE48bg6g2AAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDLkDZXMuFn-NOVQSNAIBEICBm5tYHjDL5FZTx2eUjs0if-ua2G3wxXV5TeYr53DMbgQBC7uZTfnbr5Lsx0QdnWPFukIADHQ0RYL9Ka5r7Jz7dszTSUCqpb6jPNqGgcOFvcNL4L2i1J-mYI-4wyZ9qtqovlfG3rgE_qmN0jXm63eJ9U2sI1xrRYLcAi9PvW4oQ2KFtHr32rEhuiLRQxxVCgzZzCRKzmRIQzGP0517
Text:
  Availability: 1
  Value: <anid>AN0183175095;isl01feb.25;2025Feb24.04:33;v2.2.500</anid> <title id="AN0183175095-1">Feedback features and revision uptake in dialogic peer feedback: the moderating effect of self-efficacy and prior knowledge </title> <p>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: (<reflink idref="bib1" id="ref1">1</reflink>) explanation, suggestion and positive-and-negative evaluation negatively predicted revision uptake; (<reflink idref="bib2" id="ref2">2</reflink>) self-efficacy had a significant positive effect on revision uptake, and also played a role in moderating the relationship between explanation and uptake; (<reflink idref="bib3" id="ref3">3</reflink>) 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.</p> <p>Keywords: Feedback features; Revision uptake; Self-efficacy; Prior knowledge; Psychology and Cognitive Sciences Psychology</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0183175095-2">Introduction</hd> <p>Feedback is increasingly conceptualized as a dialogue–a contingent process of information exchange and negotiation (Abdu Saeed Mohammed & Abdullah Alharbi, [<reflink idref="bib1" id="ref4">1</reflink>]). Studies have found that receiving and providing feedback to peers can enhance students' learning (Huisman et al., [<reflink idref="bib16" id="ref5">16</reflink>]). In particular, dialogic feedback can increase opportunities for interaction between the feedback provider and receiver. This increased interaction helps to overcome limitations of feedback practices in higher education, such as difficulties in acting upon feedback (Filius et al., [<reflink idref="bib13" id="ref6">13</reflink>]).</p> <p>Although dialogic peer feedback is gaining traction, challenges remain in the field. Discerning the most relevant feedback to incorporate can benefit students' learning (Topping, [<reflink idref="bib40" id="ref7">40</reflink>]), but the process of translating feedback into action is complex and challenging (Price et al., [<reflink idref="bib31" id="ref8">31</reflink>]). For example, students may refuse to accept peer feedback because they do not perceive its accuracy or find it hard to comprehend (Zhan, [<reflink idref="bib52" id="ref9">52</reflink>]). Therefore, recent research emphasizes ways of motivating students to incorporate the feedback they receive (Wu & Schunn, [<reflink idref="bib49" id="ref10">49</reflink>]; Wichmann, Funk, & Rummel, [<reflink idref="bib44" id="ref11">44</reflink>]).</p> <p>Numerous studies have examined the association between feedback features and revision uptake. However, the findings were often inconsistent. For example, some studies found a significant association between explanatory comments and students' incorporation of them (Huisman et al., [<reflink idref="bib16" id="ref12">16</reflink>]; Wu & Schunn, [<reflink idref="bib48" id="ref13">48</reflink>]), whereas others found a weak association (Nelson & Schunn, [<reflink idref="bib26" id="ref14">26</reflink>]). The relationship between feedback feature and revision uptake is likely moderated by individual factors because individual characteristics are the key elements predicting students' implementation of peer feedback. Previous research has considered individual factors as a possible explanation for the impact of feedback on responses (Nease et al., [<reflink idref="bib25" id="ref15">25</reflink>]), such as self-efficacy (Day et al., [<reflink idref="bib9" id="ref16">9</reflink>]) and prior knowledge (Fyfe & Rittle-Johnson, [<reflink idref="bib14" id="ref17">14</reflink>]). Although research emphasizes self-efficacy's effect on peer feedback uptake (Wingate, [<reflink idref="bib45" id="ref18">45</reflink>]), this effect has been given minimal attention to date, leaving room for further exploration (Tsao, [<reflink idref="bib41" id="ref19">41</reflink>]). Similarly, Noroozi et al. ([<reflink idref="bib27" id="ref20">27</reflink>]) suggested a need to further explore the influence of prior knowledge during peer feedback activities. To sum up, the relationship between feedback features and revision uptake has not been consistent in previous studies, and there has been limited examination of this relationship from the perspective of learner characteristics. Therefore, when designing and implementing peer feedback, teachers are faced with limited guidance on how to prompt students to prioritize providing specific types of feedback to peers with different individual characteristics (Wu & Schunn, [<reflink idref="bib48" id="ref21">48</reflink>]). The present study attempts to address this by examining feedback features and moderators (i.e., writing self-efficacy and prior knowledge) associated with revision uptake across a dialogic peer feedback, proposing instructional implications based on the observed relationship.</p> <hd id="AN0183175095-3">Literature review</hd> <p></p> <hd id="AN0183175095-4">Dialogic peer feedback</hd> <p>Peer feedback is a common type of feedback, defined as a process in which learners communicate about the work or performance of their peers using relevant criteria, focusing primarily on rich detailed comments (Liu & Carless, [<reflink idref="bib23" id="ref22">23</reflink>]). Peer feedback helps facilitate learner-centered processes and collaborative learning (Choi et al., [<reflink idref="bib8" id="ref23">8</reflink>]), and allows individuals to learn from giving and receiving feedback (Huisman et al., [<reflink idref="bib16" id="ref24">16</reflink>]; Sapouna, [<reflink idref="bib34" id="ref25">34</reflink>]). However, when feedback recipients passively receive the information, peer feedback activity becomes a one-way transmission of diagnostic information. With this one-way transmission, the problems of not fully understanding feedback (Filius et al., [<reflink idref="bib13" id="ref26">13</reflink>]), not receiving feedback in time (Steen-Utheim & Wittek, [<reflink idref="bib38" id="ref27">38</reflink>]), and failing to incorporate it (Er et al., [<reflink idref="bib11" id="ref28">11</reflink>]) become more prominent. Therefore, in recent years, many researchers have recognized the importance of 'dialogic peer feedback' and defined it as involving collaborative meaning-making and the evaluative judgement of work quality (Wood, [<reflink idref="bib47" id="ref29">47</reflink>]; Filius et al., [<reflink idref="bib13" id="ref30">13</reflink>]). Through this co-construction process, students can more easily understand peer feedback (Schillings et al., [<reflink idref="bib35" id="ref31">35</reflink>]).</p> <p>Although previous studies have highlighted the importance of dialogic peer feedback for learning processes and outcomes, students still face challenges in producing high-quality feedback. For example, it is not easy for students to maintain a productive dialogue around feedback, and they may encounter obstacles in an open dialogue without systematic guidance and structure (Er et al., [<reflink idref="bib11" id="ref32">11</reflink>]). Prior studies have endeavored to enhance productive peer dialogue. For instance, G.E and Land ([<reflink idref="bib50" id="ref33">50</reflink>]) employed strategies such as question prompts and online collaboration tools to promote high-level peer dialogue. In contrast to the above strategies that focus on the critical role of teachers, the regulation scripts developed by Cheng et al. ([<reflink idref="bib7" id="ref34">7</reflink>]) encourage learners' spontaneous adjustment of learning strategies in dialogic peer assessment. The scripts use question prompts to facilitate learner participation in meaningful dialogue, guide them to establish shared goals, and monitor their collaboration process. Thus, the present study took the above challenges into account alongside the suggestion by Cheng et al. ([<reflink idref="bib7" id="ref35">7</reflink>]) to support dialogic peer feedback with regulation scripts – an effective way to ensure students' participation.</p> <hd id="AN0183175095-5">Relationship between feedback features and revision uptake</hd> <p>In a peer feedback environment, Wu and Schunn ([<reflink idref="bib48" id="ref36">48</reflink>]) define feedback features as the structural components of feedback comments, such as clearly identifying a problem or expressing praise, sometimes referred to as feedback content. Previous studies have examined features of peer feedback from cognitive or affective perspectives (Wu & Schunn, [<reflink idref="bib48" id="ref37">48</reflink>]; Lu & Law, [<reflink idref="bib24" id="ref38">24</reflink>]). Cognitive feedback targets the content of the work, such as providing advice, and identifying or explaining problems. Affective feedback focuses on the quality of the work, e.g., using affective language to express praise ('good job') or criticism ('poorly written').</p> <p>Uptake refers to the learners' act of incorporating feedback into their writing when they receive it (Choi et al., [<reflink idref="bib8" id="ref39">8</reflink>]). Revision of self-work is a critical part of peer assessment (Cheng et al., [<reflink idref="bib6" id="ref40">6</reflink>]). In the process, researchers believe that it is beneficial for learners to translate the feedback they receive into practical actions, and the key is to let them try to incorporate the feedback. Although incorporating peer feedback in revision does not necessarily imply learning, the process has been shown to be helpful in triggering changes that improve academic essays (Wich mann, Funk, & Rumme, [<reflink idref="bib44" id="ref41">44</reflink>]). For feedback recipients, receiving feedback and deciding what aspects to implement can provide learning opportunities (Topping, [<reflink idref="bib40" id="ref42">40</reflink>]). Feedback may not be effective and productive if student dialogue and uptake is not encouraged (Er et al., [<reflink idref="bib11" id="ref43">11</reflink>]).</p> <p>In previous research, feedback has been linked to students' uptake in revision, and it has been argued that feedback features contribute to revision uptake in different ways (Hattie & Timperley, [<reflink idref="bib15" id="ref44">15</reflink>]). Shute ([<reflink idref="bib36" id="ref45">36</reflink>]) found that elaborated comments (such as information about the task and how to effectively perform the task) can have a more positive impact on students' performance than general comments (such as information about results). Moreover, in a study on the relationship between feedback features and uptake, Wu and Schunn ([<reflink idref="bib48" id="ref46">48</reflink>]) proposed that the information contained in peer feedback would affect student behavior. They found that explanatory comments predicted a student's willingness to improve their work. Accordingly, this study will consider previous attempts to link feedback features and revision uptake to explore what types of peer feedback can predict uptake.</p> <hd id="AN0183175095-6">Self-efficacy and the feedback feature–revision uptake relationship</hd> <p>Bandura ([<reflink idref="bib4" id="ref47">4</reflink>]) described self-efficacy as the belief that a person can successfully perform a particular task. Recent studies have emphasized that learners' domain-specific self-efficacy plays an important role in peer feedback. For example, when students worry about their own professional knowledge or evaluate the work of outstanding peers, their self-efficacy may be reduced along with their willingness to participate in peer feedback processes (Zong et al., [<reflink idref="bib53" id="ref48">53</reflink>]). In contrast, students with high self-efficacy are more confident and active in learning from the highlights of peers' work (Wei et al., [<reflink idref="bib43" id="ref49">43</reflink>]). In general, self-efficacy appears to be a useful way of examining how different types of peer feedback can significantly affect students' learning performance (Jin et al., [<reflink idref="bib17" id="ref50">17</reflink>]).</p> <p>Studies have linked students' self-efficacy to their uptake of peer feedback. Nease et al. ([<reflink idref="bib25" id="ref51">25</reflink>]) noted that self-efficacy affects a recipient's response to feedback and subsequent behavior. For example, when students have low self-efficacy, they tend to focus on their own shortcomings and do not improve based on feedback (Wingate, [<reflink idref="bib45" id="ref52">45</reflink>]). Similarly, Tsao ([<reflink idref="bib41" id="ref53">41</reflink>]) recognized the need to pay attention to improving learners' participation and writing self-efficacy, and the relationship between these two variables. In a study of written corrective feedback, this researcher found that self-efficacy for writing self-regulation can facilitate learners' uptake and revision based on feedback from peers.</p> <p>Social cognitive theory posits that individual factors (e.g., self-efficacy) play an important role in the relationship between the environment (e.g., peer feedback and other social influences) and individual behavior, and this interaction suggests that self-efficacy has a moderating effect on this relationship (Bandura, [<reflink idref="bib5" id="ref54">5</reflink>]). Correspondingly, self-efficacy moderates the relationship between peer feedback and responses to that feedback (Tsao, [<reflink idref="bib41" id="ref55">41</reflink>]). Silver et al. ([<reflink idref="bib37" id="ref56">37</reflink>]) proposed that individuals' self-efficacy plays a decisive role in their responses to positive and negative performance feedback. Furthermore, some studies have found that individuals with high self-efficacy are more likely to make more effort to respond to negative feedback than individuals with low self-efficacy (Bandura, [<reflink idref="bib4" id="ref57">4</reflink>]; Podsakoff & Farh, [<reflink idref="bib30" id="ref58">30</reflink>]). However, Aben et al. ([<reflink idref="bib2" id="ref59">2</reflink>]) found no significant correlation between writing self-efficacy and peer-feedback uptake. Therefore, whether and how learners' writing self-efficacy affects their uptake and revision in response to peer feedback still need further exploration (Tsao, [<reflink idref="bib41" id="ref60">41</reflink>]). Based on these findings, we will explore the impact of self-efficacy on feedback uptake, and the presentation of the relationship between feedback features and uptake at different levels of self-efficacy.</p> <hd id="AN0183175095-7">Prior knowledge and the feedback feature–revision uptake relationship</hd> <p>Prior knowledge is another learner characteristic related to feedback and its effects, and is frequently found to have a significant impact on student learning and performance (Riesen et al., [<reflink idref="bib32" id="ref61">32</reflink>]; Ruppert et al., [<reflink idref="bib33" id="ref62">33</reflink>]). When students' prior domain knowledge level is low, they lack theoretical knowledge and tend to exhibit more trial-and-error behavior (Kalyuga, [<reflink idref="bib18" id="ref63">18</reflink>]). Prior knowledge can influence student learning – especially in peer feedback contexts – when students are giving feedback to peers or receiving feedback from others. On the one hand, when students provide feedback, their prior knowledge and learning are strongly correlated. With sufficient domain knowledge, students can provide precise and meaningful feedback to peers, thereby increasing learning opportunities (Dmoshinskaia et al., [<reflink idref="bib10" id="ref64">10</reflink>]). On the other hand, students with different levels of prior knowledge may respond differently to the feedback they receive. Kamp et al. ([<reflink idref="bib19" id="ref65">19</reflink>]) suggested that students' prior knowledge may influence the effectiveness of peer feedback in collaborative learning groups. Students with lower prior knowledge may contribute poorly at the beginning of an activity and be more likely to incorporate peer feedback.</p> <p>Moreover, the combination of prior knowledge and different types of feedback can create an interaction effect. In the context of feedback, Fyfe and Rittle-Johnson ([<reflink idref="bib14" id="ref66">14</reflink>]) proposed reasons for prior knowledge as a key moderator. One is that most studies of feedback theory have highlighted the key role of prior knowledge, such as the interaction between prior knowledge and feedback information. For example, students with less domain-specific knowledge may not respond to elaborated feedback because of lower motivation (Yuan et al., [<reflink idref="bib51" id="ref67">51</reflink>]). Wong et al. ([<reflink idref="bib46" id="ref68">46</reflink>]) also mentioned that individual learners gain from different learning suggestions. Learners with higher prior knowledge receive more prediction-based information, whereas learners with lower prior knowledge learn more from reasoning-based information. Hence, it is reasonable to further explore the interaction between prior knowledge and feedback content (Noroozi et al., [<reflink idref="bib27" id="ref69">27</reflink>]; Kamp et al., [<reflink idref="bib19" id="ref70">19</reflink>]).</p> <hd id="AN0183175095-8">The present study</hd> <p>Previous studies have found inconsistent findings on the relationship between feedback features and revision uptake, and there have been few analyses of this relationship from the perspective of learner characteristics. To fill the gap, the present study will explore the impact of feedback features, self-efficacy, and prior knowledge on revision uptake in dialogic peer feedback. Additionally, the study will examine whether self-efficacy and prior knowledge can moderate the relationship between feedback features and uptake. Based on this, the research questions of this study are as follows:</p> <p>RQ 1. What feedback features can predict revision uptake?</p> <p>RQ 2. Does self-efficacy to complete the writing assignment predict revision uptake?</p> <p>RQ 3. Does prior knowledge about the writing topic predict revision uptake?</p> <p>RQ 4. What is the role of self-efficacy on the relationship between feedback features and revision uptake?</p> <p>RQ 5. What is the role of prior knowledge on the relationship between feedback features and revision uptake?</p> <p>In response to the research questions, the rest of the paper will test and discuss the direct effects of feedback features, self-efficacy and prior knowledge on revision uptake, as well as the moderating effects of self-efficacy and prior knowledge on the association between feedback features and revision uptake.</p> <hd id="AN0183175095-9">Method</hd> <p></p> <hd id="AN0183175095-10">Context and participants</hd> <p>The present study was conducted with 29 students (5 males and 24 females, aged between 20 and 22) enrolled in a fourth-year Intelligent Teaching System undergraduate course at a university in Beijing, all of whom were majoring in educational technology. This course is a professional elective course designed to help students understand basic knowledge, technologies, and methods of artificial intelligence and their applications in the field of education. At the end of the course, all students had to complete a research paper based on one of the five given sub themes of technology, under the broad theme 'Artificial Intelligence Applications for education.'</p> <hd id="AN0183175095-11">Design and procedure</hd> <p>The study was conducted during six stages over seven weeks: pre-test of questionnaire, completion of the writing assignment, peer feedback training, online peer review, online dialogue, and paper revision. In week 1, students completed a pre-questionnaire, which covered their self-efficacy to complete the writing assignment. In weeks 2 and 3, all students independently completed the first draft and submitted it to TronClass, a teaching platform that supports flipped classroom, learning management, peer review, and other functions.</p> <p>In week 4, training videos containing evaluation requirements, a peer assessment rubric (adapted from Liang & Tsai, [<reflink idref="bib22" id="ref71">22</reflink>]), tips for providing comments, and platform manipulation tips were uploaded to TronClass in advance, and students were required to study the training videos before engaging in formal peer feedback activities. In week 5, the researcher randomly distributed the papers to students through TronClass. Each student was required to evaluate approximately three peer drafts under the same topic. They graded each draft using the peer assessment rubric, and needed to provide at least one comment for each criterion. In week 6, with the support of the regulation script (adapted from by Cheng et al., [<reflink idref="bib7" id="ref72">7</reflink>]), students used Tencent Meeting (a synchronous captioning audio-visual feedback tool) and Tencent Docs (a collaborative editing tool) to carry out a dialogue around feedback. In week 7, students revised their papers based on the feedback they had received earlier and submitted the final version. After completing the revision, 15 students participated in a semi-structured interview.</p> <hd id="AN0183175095-12">Measures</hd> <p>This study measured peer comments, essay revisions, self-efficacy, prior knowledge, and control variables (namely, previous academic performance and first draft quality). To effectively ensure the reliability of the analysis data, two researchers coded 30% of all dimensional data and achieved a satisfactory level of consistency. All differences were resolved through discussion before final coding.</p> <hd id="AN0183175095-13">Peer comments</hd> <p>The processing of peer comments received by students included three stages: segmentation, selection and coding. First, we segmented written and dialogue comments into idea units. The idea unit here followed the definition of Wu and Schunn ([<reflink idref="bib48" id="ref73">48</reflink>]), that is, to propose and/or solve a problem on a given dimension.</p> <p>Revision-oriented comments needed to be selected before coding because the focus of this study was on student uptake. Feedback was considered to be either revision-oriented or non-revision-oriented (Pham, [<reflink idref="bib29" id="ref74">29</reflink>]). Revision-oriented feedback included specific suggestions, with common features being suggestion, identification, and explanation. Non-revision-oriented feedback, in contrast, only contains summary or praise of peer work. According to this description, the two researchers excluded non-revision-oriented comments from all segmented peer feedback and retained 242 revision-oriented peer feedback.</p> <p>Table 1 Feedback features coding scheme</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Category</p></th><th align="left"><p>Description</p></th><th align="left"><p>Example</p></th></tr></thead><tbody><tr><td align="left" colspan="3"><p>Cognitive</p></td></tr><tr><td align="left"><p>Identification</p></td><td align="left"><p>To point out what is problematic</p></td><td align="left"><p>Future applications are somewhat broad.</p></td></tr><tr><td align="left"><p>Explanation</p></td><td align="left"><p>To explain why it is problematic</p></td><td align="left"><p>The research paper lacks substantive and in-depth content, as the statement of research and application status mainly focuses on basic knowledge introduction and bibliometric analysis.</p></td></tr><tr><td align="left"><p>Suggestion</p></td><td align="left"><p>To provide general advice by giving directions for improvements</p></td><td align="left"><p>The completeness and richness of the content need to be supplemented.</p></td></tr><tr><td align="left"><p>Solution</p></td><td align="left"><p>To provide specific advice by outlining revised content</p></td><td align="left"><p>The first paragraph of the second part is too long. Maybe it can be divided into two parts. For example, "2.1. Application of NLP in teaching, learning, testing and evaluation" and "2.2. Application of NLP in different scenarios".</p></td></tr><tr><td align="left" colspan="3"><p>Affective</p></td></tr><tr><td align="left"><p>Positive</p></td><td align="left"><p>Make a positive evaluation, stating the advantages or useful content of the report</p></td><td align="left"><p>The number of references is large, and the review is detailed, reliable and complete, and you can find out whether there is relevant literature.</p></td></tr><tr><td align="left"><p>Negative</p></td><td align="left"><p>Make a negative evaluation, stating the paper's shortcomings or need to be corrected</p></td><td align="left"><p>In terms of organization, the transitional expression between parts is poor.</p></td></tr><tr><td align="left"><p>Positive-and-negative</p></td><td align="left"><p>Make both positive and negative evaluations</p></td><td align="left"><p>The language expression is smooth and natural, but the academic standardization is insufficient.</p></td></tr><tr><td align="left"><p>Neutral</p></td><td align="left"><p>No obvious position and affective bias, only stating objective facts</p></td><td align="left"><p>I'm not sure whether this model uses the deep learning method.</p></td></tr></tbody></table> </ephtml> </p> <p> <sups>NLP Natural language processing</sups> </p> <p>Next, to examine feedback features and their impact on uptake, two researchers coded revision-oriented comments using the coding scheme shown in Table 1. Based on previous research on peer dialogic function (Pham, [<reflink idref="bib29" id="ref75">29</reflink>]) and peer feedback features (Wu & Schunn, [<reflink idref="bib48" id="ref76">48</reflink>]), this study adapted the peer feedback features coding scheme developed by Lu and Law ([<reflink idref="bib24" id="ref77">24</reflink>]) as Table 1. The table includes two first-level dimensions, cognitive and affective, and each first-level dimension also includes four sub-dimensions. The cognitive dimension includes: identification (Kappa = 0.920), explanation (Kappa = 0.933), suggestion (Kappa = 0.831), solution (Kappa = 0.953). The affective dimension includes: positive (Kappa = 0.969), negative (Kappa = 0.926), positive-and-negative (Kappa = 0.909), and neutral (Kappa = 0.974).</p> <hd id="AN0183175095-14">Essay revisions</hd> <p>To determine which peer comments were incorporated, the study used Microsoft Word's Compare Document tool to compare students' first and revised essay, and mark all revision-oriented comments (Yes = incorporated, No = non-incorporated).</p> <p>After finishing the revision, all students from the peer feedback activity were invited to participate in a half-hour semi-structured interview. From these 29 students, 15 agreed and were interviewed. The interviews were audiotaped to confirm the above comparison results and the reasons why learners incorporate or did not incorporate peer feedback, thereby providing support for the findings of the study.</p> <hd id="AN0183175095-15">Self-efficacy</hd> <p>The self-efficacy questionnaire was adapted from the scale developed by Wang and Lin ([<reflink idref="bib42" id="ref78">42</reflink>]), with a total of eight items, such as 'I believe I will receive an excellent grade on my essay.' All responses were rated on a 5-point Likert scale (1 = strongly disagree; 5 = strongly agree), with higher rating scores indicating higher self-efficacy. Cronbach's α was 0.869. Exploratory Factor Analysis (EFA) showed good validity: the Kaiser-Meyer-Olkin measure = 0.736, and Bartlett's test of sphericity <emph>χ</emph><sups>2</sups> = 135.181, <emph>df</emph> = 28, <emph>p</emph> < 0.01.</p> <hd id="AN0183175095-16">Prior knowledge</hd> <p>Students' formative grades in the course were used to indicate prior knowledge. During the course, students were required to collaborate on a topic presentation task assigned by the teacher and record each person's contribution. Subsequently, the teacher determined the score based on each person's performance, which served as the basis of their formative grades. This task aims to assess the students' understanding of Artificial Intelligence technologies and how they can be applied in future education, which is closely related to the topic of the writing assignment. Before starting the writing assignment, all students completed the presentation task and acquired the relevant knowledge. Previous research relied on existing indicators, such as pretests, to measure prior knowledge. However, student response to feedback will be affected by other factors, e.g., intelligence (Fyfe & Rittle-Johnson, [<reflink idref="bib14" id="ref79">14</reflink>]). Therefore, this study used students' formative grades before starting writing as their prior knowledge about the writing topic to avoid variables outside the course.</p> <hd id="AN0183175095-17">Control variables</hd> <p>The study used previous academic performance and first draft quality as control variables. Each student's previous academic performance was calculated by dividing their total academic achievement over the previous three years by their total number of credits. First draft quality was calculated by averaging peer ratings.</p> <hd id="AN0183175095-18">Data analysis</hd> <p>Statistical analysis was conducted using SPSS (version 22., Armonk, NY: IBM Corp.). First, we calculated the means (M) and standard deviations (SD) of variables, and presented the Spearman correlation between these variables. Next, five sets of logistic regression analyses were conducted to analyze: (<reflink idref="bib1" id="ref80">1</reflink>) which feedback features predict revision uptake; (<reflink idref="bib2" id="ref81">2</reflink>) the predictive effect of self-efficacy on uptake; (<reflink idref="bib3" id="ref82">3</reflink>) the predictive effect of prior knowledge on uptake; (<reflink idref="bib4" id="ref83">4</reflink>) the moderating effect of self-efficacy in the relationship between feedback features and uptake; and (<reflink idref="bib5" id="ref84">5</reflink>) the moderating effect of prior knowledge in the relationship between feedback features and uptake. In the five analyses, previous academic performance and first draft quality were all controlled. Besides, interaction effects were examined by following Li et al.'s ([<reflink idref="bib21" id="ref85">21</reflink>]) procedure (<ulink href="http://www.jeremydawson.co.uk/slopes.htm">http://www.jeremydawson.co.uk/slopes.htm</ulink>)<emph>.</emph></p> <p>Besides, deductive analysis was used to analyse interview data based on the influence of feedback features on revision uptake, and the moderating effect of self-efficacy and prior knowledge on this relationship (Azungah, [<reflink idref="bib3" id="ref86">3</reflink>]). During the analysis process, any interpretation uncertainties were resolved through consultation among researchers and assistance from interviewees.</p> <hd id="AN0183175095-19">Results</hd> <p></p> <hd id="AN0183175095-20">Descriptive analysis and correlations among variables</hd> <p>Descriptive statistics for each variable and their correlations are presented in Table 2. Most correlations among the features were small (<emph>r</emph> < |0.4|). However, there was a strong correlation between negative and positive-and-negative (<emph>r</emph> = − 0.629, <emph>p</emph> < 0.01). Thus, negative was not included in subsequent analyses to avoid multicollinearity. Without considering this variable, the Variance Impact Factors of all predictor variables were less than 3.5, indicating that the degree of multicollinearity was very low. Table 2 also shows that some feedback features were significantly related to students' uptake, namely, suggestion (<emph>r</emph> = − 0.179, <emph>p</emph> < 0.01), solution (<emph>r</emph> = 0.192, <emph>p</emph> < 0.01), and positive-and-negative (<emph>r</emph> = − 0.223, <emph>p</emph> < 0.01). Of these, solution (<emph>r</emph> = − 0.134, <emph>p</emph> < 0.05) and positive-and-negative (<emph>r</emph> = 0.141, <emph>p</emph> < 0.05) were also significantly related to self-efficacy. Moreover, explanation (<emph>r</emph> = − 0.143, <emph>p</emph> < 0.05), positive (<emph>r</emph> = 0.135, <emph>p</emph> < 0.05) and positive-and-negative (<emph>r</emph> = 0.154, <emph>p</emph> < 0.05) were significantly correlated with students' prior knowledge. In addition, students' self-efficacy to complete the writing assignment had a significant positive correlation with their prior knowledge (<emph>r</emph> = 0.155, <emph>p</emph> < 0.05), but neither of these variables had a significant correlation with uptake.</p> <p>Table 2 Descriptive statistics (<emph>N</emph> = 242) and correlations</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left" /><th align="left"><p>1</p></th><th align="left"><p>2</p></th><th align="left"><p>3</p></th><th align="left"><p>4</p></th><th align="left"><p>5</p></th><th align="left"><p>6</p></th><th align="left"><p>7</p></th><th align="left"><p>8</p></th><th align="left"><p>9</p></th><th align="left"><p>10</p></th><th align="left"><p>11</p></th></tr></thead><tbody><tr><td align="left"><p>1. Identification</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>2. Explanation</p></td><td align="left"><p>− 0.407**</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>3. Suggestion</p></td><td align="left"><p>− 0.342**</p></td><td align="left"><p>− 0.236**</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>4. Solution</p></td><td align="left"><p>− 0.322**</p></td><td align="left"><p>− 0.221**</p></td><td align="left"><p>− 0.186**</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>5. Positive</p></td><td align="left"><p>− 0.123</p></td><td align="left"><p>− 0.084</p></td><td align="left"><p>0.144*</p></td><td align="left"><p>0.157*</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>6. Negative</p></td><td align="left"><p>0.075</p></td><td align="left"><p>0.204**</p></td><td align="left"><p>− 0.247**</p></td><td align="left"><p>− 0.102</p></td><td align="left"><p>− 0.129*</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>7. Positive-and-negative</p></td><td align="left"><p>0.285**</p></td><td align="left"><p>0.001</p></td><td align="left"><p>− 0.047</p></td><td align="left"><p>− 0.229**</p></td><td align="left"><p>− 0.124</p></td><td align="left"><p>− 0.629**</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>8. Neutral</p></td><td align="left"><p>− 0.388**</p></td><td align="left"><p>− 0.217**</p></td><td align="left"><p>0.302**</p></td><td align="left"><p>0.338**</p></td><td align="left"><p>− 0.080</p></td><td align="left"><p>− 0.409**</p></td><td align="left"><p>− 0.391**</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left"><p>9. Uptake</p></td><td align="left"><p>− 0.008</p></td><td align="left"><p>− 0.103</p></td><td align="left"><p>− 0.179**</p></td><td align="left"><p>0.192**</p></td><td align="left"><p>− 0.016</p></td><td align="left"><p>0.123</p></td><td align="left"><p>− 0.223**</p></td><td align="left"><p>0.125</p></td><td align="left"><p>-</p></td><td align="left" /><td align="left" /></tr><tr><td align="left"><p>10. Self-efficacy</p></td><td align="left"><p>0.098</p></td><td align="left"><p>− 0.069</p></td><td align="left"><p>0.072</p></td><td align="left"><p>− 0.134*</p></td><td align="left"><p>− 0.107</p></td><td align="left"><p>− 0.103</p></td><td align="left"><p>0.141*</p></td><td align="left"><p>− 0.002</p></td><td align="left"><p>0.086</p></td><td align="left"><p>-</p></td><td align="left" /></tr><tr><td align="left"><p>11. Prior knowledge</p></td><td align="left"><p>− 0.077</p></td><td align="left"><p>− 0.143*</p></td><td align="left"><p>0.006</p></td><td align="left"><p>0.114</p></td><td align="left"><p>0.135*</p></td><td align="left"><p>− 0.121</p></td><td align="left"><p>− 0.049</p></td><td align="left"><p>0.154*</p></td><td align="left"><p>0.029</p></td><td align="left"><p>0.155*</p></td><td align="left"><p>-</p></td></tr><tr><td align="left"><p>M ± SD</p></td><td align="left"><p>0.370 ± 0.484</p></td><td align="left"><p>0.220 ± 0.414</p></td><td align="left"><p>0.170 ± 0.372</p></td><td align="left"><p>0.150 ± 0.357</p></td><td align="left"><p>0.020 ± 0.156</p></td><td align="left"><p>0.400 ± 0.490</p></td><td align="left"><p>0.380 ± 0.485</p></td><td align="left"><p>0.200 ± 0.403</p></td><td align="left"><p>0.550 ± 0.499</p></td><td align="left"><p>3.524 ± 0.586</p></td><td align="left"><p>90.281 ± 4.071</p></td></tr></tbody></table> </ephtml> </p> <p>*p < 0.05, **p < 0.01</p> <hd id="AN0183175095-21">The main effects of feedback features, self-efficacy, and prior knowledge on revision uptake</hd> <p>The results of the logistic regression analyses can be seen in Table 3, expressed as odds ratios. When all other variables remain unchanged, odds ratios greater than 1 indicate that there is a positive correlation between the predictor and the outcome. An odds ratio lower than 1 means that when other variables remain unchanged, there is a negative correlation between the predictor and the outcome. As shown in Table 3, Model 1 showed that feedback with explanation (OR = 0.296, 95% CI = [0.089, 0.979]), suggestions (OR = 0.146, 95% CI = [0.041, 0.520]), and positive-and-negative (OR = 0.457, 95% CI = [0.246, 0.850]) had significant negative effects on uptake. This suggested that the more often students received these three types of comments, the more likely they were to reject the feedback.</p> <p>Based on Model 1, Model 2 included students' self-efficacy. The results showed that general suggestions (OR = 0.107, 95% CI = [0.029, 0.393]) and comments containing both positive-and-negative evaluations (OR = 0.392, 95% CI = [0.204,.751]) were the negative factors for students to incorporate peer feedback, whereas self-efficacy was a positive factor (OR = 2.728, 95% CI = [1.539, 4.838]). Model 3 further suggested that the more explanation (OR = 0.278, 95% CI = [0.083, 0.933]), suggestions (OR = 0.140, 95% CI = [0.039, 0.501]), and positive-and-negative feedback (OR = 0.461, 95% CI = [0.248, 0.858]) students receive, the less likely they are to incorporate feedback. However, prior knowledge cannot significantly predict students" uptake of feedback.</p> <hd id="AN0183175095-22">The moderating effects of self-efficacy and prior knowledge on the uptake of feedback feature...</hd> <p>Model 4 in Table 3 shows the moderating effect analyses of students' self-efficacy to complete the writing assignment in the relationship between feedback features and uptake. First, the interaction between explanatory comments and self-efficacy significantly positively predicted students' uptake in revision (<emph>β</emph> = 3.445, <emph>p</emph> < 0.05, 95% CI = [1.247, 787.814]), which means that self-efficacy has a significant positive moderating effect on the relationship between explanation and self-efficacy. To further explore the moderating effect, we performed the simple slope test (shown in Fig. 1). When students receive more feedback explaining the problems with their work, higher self-efficacy increases the probability of students incorporating these peer comments. However, for students with low self-efficacy, the more explanations they received, the more likely they were to reject them.</p> <p>Table 3 The main and interaction effects of feedback features, self-efficacy and prior knowledge on revision uptake</p> <p> <ephtml> <table frame="hsides" rules="groups"><thead><tr><th align="left"><p>Variables</p></th><th align="left" colspan="2"><p> Model 1</p></th><th align="left" colspan="2"><p> Model 2</p></th><th align="left" colspan="2"><p> Model 3</p></th><th align="left"><p>Model 4</p></th><th align="left"><p>Model 5</p></th></tr><tr><th align="left" /><th align="left"><p><italic>β</italic></p></th><th align="left"><p>OR (95% CI)</p></th><th align="left"><p><italic>β</italic></p></th><th align="left"><p>OR (95% CI)</p></th><th align="left"><p><italic>β</italic></p></th><th align="left"><p>OR (95% CI)</p></th><th align="left"><p><italic>β</italic></p></th><th align="left"><p><italic>β</italic></p></th></tr></thead><tbody><tr><td align="left"><p>Previous academic</p><p>performance</p></td><td align="left"><p>− 0.116</p></td><td align="left"><p>0.890 (0.749, 1.059)</p></td><td align="left"><p>− 0.278**</p></td><td align="left"><p>0.757 (0.618, 0.927)</p></td><td align="left"><p>− 0.117</p></td><td align="left"><p>0.890 (0.749, 1.057)</p></td><td align="left"><p>− 0.293**</p></td><td align="left"><p>− 0.149</p></td></tr><tr><td align="left"><p>First draft quality</p></td><td align="left"><p>0.143</p></td><td align="left"><p>1.154 (0.943, 1.411)</p></td><td align="left"><p>0.255*</p></td><td align="left"><p>1.290 (1.040, 1.601)</p></td><td align="left"><p>0.183</p></td><td align="left"><p>1.200 (0.959, 1.502)</p></td><td align="left"><p>0.278*</p></td><td align="left"><p>0.219</p></td></tr><tr><td align="left"><p>Identification</p></td><td align="left"><p>− 0.696</p></td><td align="left"><p>0.499 (0.159, 1.566)</p></td><td align="left"><p>− 0.707</p></td><td align="left"><p>0.493 (0.154, 1.583)</p></td><td align="left"><p>− 0.746</p></td><td align="left"><p>0.474 (0.150, 1.505)</p></td><td align="left"><p>− 1.014</p></td><td align="left"><p>− 0.415</p></td></tr><tr><td align="left"><p>Explanation</p></td><td align="left"><p>− 1.218*</p></td><td align="left"><p>0.296 (0.089, 0.979)</p></td><td align="left"><p>− 1.190</p></td><td align="left"><p>0.304 (0.089, 1.035)</p></td><td align="left"><p>− 1.282*</p></td><td align="left"><p>0.278 (0.083, 0.933)</p></td><td align="left"><p>− 1.415*</p></td><td align="left"><p>− 0.892</p></td></tr><tr><td align="left"><p>Suggestion</p></td><td align="left"><p>− 1.924**</p></td><td align="left"><p>0.146 (0.041, 0.520)</p></td><td align="left"><p>− 2.239**</p></td><td align="left"><p>0.107 (0.029, 0.393)</p></td><td align="left"><p>− 1.969**</p></td><td align="left"><p>0.140 (0.039, 0.501)</p></td><td align="left"><p>− 2.427**</p></td><td align="left"><p>− 1.572*</p></td></tr><tr><td align="left"><p>Solution</p></td><td align="left"><p>− 0.100</p></td><td align="left"><p>0.905 (0.241, 3.406)</p></td><td align="left"><p>0.044</p></td><td align="left"><p>1.045 (0.271, 4.024)</p></td><td align="left"><p>− 0.105</p></td><td align="left"><p>0.901 (0.239, 3.401)</p></td><td align="left"><p>− 0.323</p></td><td align="left"><p>0.076</p></td></tr><tr><td align="left"><p>Positive</p></td><td align="left"><p>− 0.310</p></td><td align="left"><p>0.734 (0.101, 5.319)</p></td><td align="left"><p>0.189</p></td><td align="left"><p>1.208 (0.159, 9.184)</p></td><td align="left"><p>− 0.260</p></td><td align="left"><p>0.771 (0.104, 5.740)</p></td><td align="left"><p>− 14.684</p></td><td align="left"><p>7.228</p></td></tr><tr><td align="left"><p>Positive-and-negative</p></td><td align="left"><p>− 0.782*</p></td><td align="left"><p>0.457 (0.246, 0.850)</p></td><td align="left"><p>− 0.937**</p></td><td align="left"><p>0.392 (0.204, 0.751)</p></td><td align="left"><p>− 0.775*</p></td><td align="left"><p>0.461 (0.248, 0.858)</p></td><td align="left"><p>− 0.904*</p></td><td align="left"><p>− 0.762*</p></td></tr><tr><td align="left"><p>Neutral</p></td><td align="left"><p>0.365</p></td><td align="left"><p>1.441 (0.546, 3.805)</p></td><td align="left"><p>0.427</p></td><td align="left"><p>1.533 (0.568, 4.137)</p></td><td align="left"><p>0.389</p></td><td align="left"><p>1.475 (0.557, 3.908)</p></td><td align="left"><p>0.346</p></td><td align="left"><p>0.351</p></td></tr><tr><td align="left"><p>SE</p></td><td align="left" /><td align="left" /><td align="left"><p>1.004**</p></td><td align="left"><p>2.728 (1.539, 4.838)</p></td><td align="left" /><td align="left" /><td align="left"><p>− 1.657</p></td><td align="left" /></tr><tr><td align="left"><p>PK</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 0.034</p></td><td align="left"><p>0.967 (0.890, 1.051)</p></td><td align="left" /><td align="left"><p>0.040</p></td></tr><tr><td align="left"><p>SE × Identification</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>2.018</p></td><td align="left" /></tr><tr><td align="left"><p>SE × Explanation</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>3.445*</p></td><td align="left" /></tr><tr><td align="left"><p>SE × Suggestion</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>1.795</p></td><td align="left" /></tr><tr><td align="left"><p>SE × Solution</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>1.634</p></td><td align="left" /></tr><tr><td align="left"><p>SE × Positive</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 23.566</p></td><td align="left" /></tr><tr><td align="left"><p>SE × Positive-and-negative</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.761</p></td><td align="left" /></tr><tr><td align="left"><p>SE × Neutral</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>1.168</p></td><td align="left" /></tr><tr><td align="left"><p>PK × Identification</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 0.290</p></td></tr><tr><td align="left"><p>PK × Explanation</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 0.107</p></td></tr><tr><td align="left"><p>PK × Suggestion</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 0.337</p></td></tr><tr><td align="left"><p>PK × Solution</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 0.025</p></td></tr><tr><td align="left"><p>PK × Positive</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>− 3.839</p></td></tr><tr><td align="left"><p>PK × Positive-and-negative</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.200*</p></td></tr><tr><td align="left"><p>PK × Neutral</p></td><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left" /><td align="left"><p>0.125</p></td></tr><tr><td align="left"><p>Nagelkerke <italic>R</italic><sup>2</sup></p></td><td align="left"><p>0.174</p></td><td align="left" /><td align="left"><p>0.234</p></td><td align="left" /><td align="left"><p>0.177</p></td><td align="left" /><td align="left"><p>0.280</p></td><td align="left"><p>0.241</p></td></tr></tbody></table> </ephtml> </p> <p>*p <.05, **p <.01, SE = Self-efficacy, PK = Prior knowledge</p> <p>Graph: Fig. 1 The interaction effects between explanatory feedback and self-efficacy on the probability of uptake</p> <p>Model 5 indicates that the interaction between feedback (both positive and negative evaluations) and prior knowledge can also have a positive effect on uptake (<emph>β</emph> = 0.200, <emph>p</emph> < 0.05, 95% CI = [1.025, 1.455]). Figure 2 shows that students with low prior knowledge always have a higher probability of incorporating positive-and-negative feedback, but this probability will decline with the increase in this type of feedback. For students with higher prior knowledge, the probability of incorporating positive-and-negative evaluations will slowly increase with the increase in feedback.</p> <p>Graph: Fig. 2 The interaction effects between positive-and-negative feedback and prior knowledge on the probability of uptake</p> <hd id="AN0183175095-23">Discussion and conclusion</hd> <p>The study focused on the predictive effect of feedback features on students' uptake during revision, and whether their individual factors could exert a moderating effect on this relationship. This study found that certain types of peer feedback (i.e., explanation, suggestion and positive-and-negative evaluation) and self-efficacy predict revision uptake. Additionally, previous studies indicated students' self-efficacy and prior knowledge were moderators. By exploring the moderating effect of self-efficacy and prior knowledge, this study found a close relationship between feedback features, individual factors, and uptake.</p> <hd id="AN0183175095-24">Relationship between feedback features and revision uptake</hd> <p>The present study found that explanation, suggestion, and positive-and-negative feedback were significantly negatively related to student uptake of this feedback. This finding differs from the results of Wu and Schunn ([<reflink idref="bib48" id="ref87">48</reflink>]), who reported a positive predictive effect of explanation when exploring the relationship between feedback features and uptake. Understanding the meaning of feedback is the premise of incorporating it (Patchan et al., [<reflink idref="bib28" id="ref88">28</reflink>]). Therefore, one possible reason for the differences noted is that the students in this study could not understand the explanations they received, leaving them unsure about how to carry out revisions based on the comments. As mentioned by students in interviews after the peer feedback activity, incomprehension is an important reason for refusing to implement peers' explanations of the problem:</p> <p>One of my peers thinks that my work lacks personal thought, but I don't understand which dimension this problem refers to. This comment is so broad that I'm not sure how to use it. (S1)</p> <p>I found that from my own thinking, I could not understand the explanation of my peers. So I did not incorporate these comments when revising my work. (S2)</p> <p>Students tended to reject general suggestions when they received this type of feedback. This finding is consistent with prior research (Lu & Law, [<reflink idref="bib24" id="ref89">24</reflink>]). One possible factor causing this negative outcome is the extent to which feedback recipients perceive the feedback to be valuable (Straub, [<reflink idref="bib39" id="ref90">39</reflink>]). When students think that the suggestions they receive are ineffective, they will not revise using that feedback. Some interviewees gave similar reasons. When they receive suggestions that only offer general directions for improvement, they tend to think that their peers did not carefully review their writing and may easily overlook these suggestions. Additionally, these suggestions might lack constructiveness, which can make it difficult for them to determine how to improve effectively:</p> <p>My peers suggested that I could explore further into the application of technology in education, but I didn't know how to improve upon it, so I didn't follow this advice and instead focused on other aspects. (S3)</p> <p>Some students who did not take the evaluation seriously tend to write short suggestions. This leads me to believe that they did not carefully read my writing, which could negatively impact the revision process. (S4)</p> <p>Moreover, positive-and-negative feedback exhibited a significant negative relation with students' uptake. This aligns with previous research that found that feedback with positive or negative evaluations usually contains less task-related information, resulting in less student participation (Hattie & Timperley, [<reflink idref="bib15" id="ref91">15</reflink>]). This finding may also be the reason for the negative effect in this study. In addition, five interviewees explicitly expressed their rejection of partially positive-and-negative evaluations. Similar to the above view, due to the belief that these comments lack effective information related to writing improvement tasks, they were not competent enough to make improvements based on them.</p> <hd id="AN0183175095-25">Relationship between self-efficacy, feedback features and revision uptake</hd> <p>Students' self-efficacy can positively predict their uptake of peer feedback. The finding aligns with previous research that revealed that self-efficacy is an important factor in encouraging students' uptake of peer-feedback (Tsao, [<reflink idref="bib41" id="ref92">41</reflink>]). Wingate ([<reflink idref="bib45" id="ref93">45</reflink>]) also suggested that students with lower self-efficacy are more likely to ignore comments from peers. However, Aben et al. ([<reflink idref="bib2" id="ref94">2</reflink>]) found no significant correlation between writing self-efficacy and uptake of peer feedback. They believe that the insignificant correlation may be due to students viewing peer feedback as an extra-curricular activity and ignoring the contribution of peer feedback to the final assessment. In contrast, students in this study were reminded that peer feedback is a part of the curriculum and its importance in completing writing assignment. Considering that individuals with greater self-efficacy are inclined to pursue self-improvement, they are more likely than those with lower self-efficacy to evince a heightened tendency towards accepting peer feedback and taking action based on it (Wingate, [<reflink idref="bib45" id="ref95">45</reflink>]). Seven interviewees also believed that self-efficacy has a positive impact on incorporating peer feedback. When they believe they have the ability to improve their writing, they are willing to make revisions based on peer feedback to make their work more complete:</p> <p>I recognized that my writing in the section discussing future scenarios required improvement. Therefore, after receiving suggestions from my peers, I became more motivated to enhance this section and strengthen the overall quality of my work. (S5)</p> <p>The present study also found that students' uptake of explanation feedback is actively influenced by their self-efficacy. In other words, when self-efficacy is high, explanation will improve feedback uptake. In this study, explanation can help clarify why students' work was problematic. Furthermore, this feedback has been demonstrated as a beneficial factor in promoting students' uptake in the research conducted by Wu and Schunn ([<reflink idref="bib48" id="ref96">48</reflink>]). Previous research revealed a different view: learners with high self-efficacy tend to be reluctant to accept feedback indicating their performance errors, thus affecting their accuracy in judging feedback (Nease et al., [<reflink idref="bib25" id="ref97">25</reflink>]). However, if students lack confidence in themselves, they will have emotional barriers to accepting critical feedback (Fan & Xu, [<reflink idref="bib12" id="ref98">12</reflink>]). Therefore, we believe that learners with high self-efficacy face explanatory comments in a more positive emotional state and are, thus, more willing to respond to feedback. Correspondingly, six interviewees reported the difficulty of this writing assignment. Those who believed they were unable to make further revisions chose to ignore peers' explanations, while those who were confident in completing the assignment made revisions based on the explanations:</p> <p>My peers identified that there are problems with my future vision, but we did not solve them during our discussion. So we believes (sic.) that this is the most difficult part of this writing. (S6)</p> <p>The encouragement from peers enhanced my confidence in completing assignments, and their explanations also changed my original views. (S7)</p> <hd id="AN0183175095-26">Relationship between prior knowledge, feedback features and revision uptake</hd> <p>In addition, students' prior knowledge moderates between positive-and-negative feedback and their uptake of it. Lower prior knowledge reduces the probability of incorporating positive-and-negative feedback, whereas higher prior knowledge can improve the probability. Fyfe and Rittle-Johnson ([<reflink idref="bib14" id="ref99">14</reflink>]) reported that prior knowledge moderates the effect of verification feedback. They provided students with positive or/and negative feedback on correctness, and found that compared with students who already knew the correct strategies, students who had no previous relevant knowledge were more likely to reduce the number of wrong strategies and generate correct problem-solving strategies on the basis of the feedback given. For students with some prior knowledge, receiving feedback with positive and negative comments may cause them to focus more on the quality of their work rather than thinking about how to improve based on the comments (Kluger & DeNisi, [<reflink idref="bib20" id="ref100">20</reflink>]). This could be the reason why students with higher prior knowledge tend to have a relatively lower but slowly increasing probability of incorporating this feedback. Accordingly, seven students indicated that lack of prior knowledge limited their uptake of comments with both positive and negative evaluations:</p> <p>I didn't incorporate positive-and-negative evaluations into my revisions because I didn't have a clear understanding of the Knowledge Graph technology and its implementation process, which made it difficult for me to explain the core knowledge accurately. (S8)</p> <p>I believe a semester is too short for studying artificial intelligence. As a result, my limited understanding prevents me from providing solutions to the problems raised by my peers. (S2)</p> <hd id="AN0183175095-27">Implications and limitations</hd> <p></p> <hd id="AN0183175095-28">Implications for practice</hd> <p>The findings have implications for teachers in designing and facilitating peer feedback. First, learner characteristics should be considered when grouping students for peer feedback activities (Wong et al., [<reflink idref="bib46" id="ref101">46</reflink>]), and differentiated learning support should be provided for students in different groups. For example, we suggest that feedback containing both positive and negative evaluations should be reduced for students with lower prior knowledge, as they may overlook these comments and not incorporate them (Noroozi et al., [<reflink idref="bib27" id="ref102">27</reflink>]). Furthermore, students with high self-efficacy may benefit from more feedback on the reasons for their work's problems, while it may be best to reduce this type of feedback for students with lower self-efficacy.</p> <p>Second, measures should be taken to improve students' self-efficacy because self-efficacy is an important factor needed for students to incorporate peer feedback (Tsao, [<reflink idref="bib41" id="ref103">41</reflink>]; Nease et al., [<reflink idref="bib25" id="ref104">25</reflink>]). For example, teachers can provide students with Q&A and guidance before they begin writing, thus improving their confidence to complete high quality writing tasks.</p> <p>Third, students should be supported to propose specific solutions instead of simply providing affective evaluations to peers. Because comments containing only affective feedback are often less relevant to the task or do not present specific improvements (Hattie & Timperley, [<reflink idref="bib15" id="ref105">15</reflink>]).</p> <hd id="AN0183175095-29">Limitations and future research</hd> <p>Three limitations of the study should also be recognized. First, the participants in this study only comprised senior students engaged in the Intelligent Teaching System course. Future research could select samples from different disciplines, learning stages, and countries/regions to enable more generalization of findings.</p> <p>Second, in studying the moderating effect, we only considered intrapersonal factors and did not explore situational or interpersonal factors. Future research could explore the effects of other learner characteristics (such as willingness to participate in peer feedback, cultural background, and historical knowledge) or interpersonal factors (such as peer familiarity) on peer feedback uptake. This could help propose effective intervention strategies.</p> <p>Third, this study did not fully explore the interactive content in peer dialogue. Future research could further examine the complex interplay of different feedback features during peer feedback dialogues to obtain more valuable research findings. Furthermore, future studies also could analyze the role of students and turn-taking during the dialogue, and explore the impact of these factors on students' revision.</p> <hd id="AN0183175095-30">Acknowledgements</hd> <p>We thank Michelle Pascoe, PhD, from Liwen Bianji (Edanz) (<ulink href="http://www.liwenbianji.cn/">www.liwenbianji.cn/</ulink>) for editing the English text of a draft of this manuscript.</p> <hd id="AN0183175095-31">Funding</hd> <p>This study was supported by the National Natural Science Foundation of China (Grant No: 62277006) and the Beijing Natural Science Foundation (Grant No: 9222019).</p> <hd id="AN0183175095-32">Data availability</hd> <p>Access to the data can be requested from the first author via e-mail.</p> <hd id="AN0183175095-33">Declarations</hd> <p>The authors report no potential conflict of interest.</p> <hd id="AN0183175095-34">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0183175095-35"> <title> References </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> Abdu Saeed Mohammed M, Abdullah Alharbi M. Cultivating learners' technology-mediated dialogue of feedback in writing: Processes, potentials and limitations. Assessment & Evaluation in Higher Education. 2022; 47; 6: 942-958. 10.1080/02602938.2021.1969637</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> Aben JE, Timmermans AC, Dingyloudi F, Lara MM, Strijbos JW. What influences students' peer-feedback uptake? Relations between error tolerance, feedback tolerance, writing self-efficacy, perceived language skills and peer-feedback processing. Learning and Individual Differences. 2022; 97: 102175. 10.1016/j.lindif.2022.102175</bibtext> </blist> <blist> <bibl id="bib3" idref="ref3" type="bt">3</bibl> <bibtext> Azungah T. Qualitative research: Deductive and inductive approaches to data analysis. Qualitative Research Journal. 2018; 18; 4: 383-400. 10.1108/QRJ-D-18-00035</bibtext> </blist> <blist> <bibl id="bib4" idref="ref47" type="bt">4</bibl> <bibtext> Bandura A. Social foundations of thought and action: a social cognitive theory. 1986; Prentice-Hall</bibtext> </blist> <blist> <bibl id="bib5" idref="ref54" type="bt">5</bibl> <bibtext> Bandura AWeiner IB, Craighead WE. Self-efficacy. The corsini encyclopedia of psychology. 2010; John Wiley & Sons, Inc.: 1534-1536</bibtext> </blist> <blist> <bibl id="bib6" idref="ref40" type="bt">6</bibl> <bibtext> Cheng KH, Liang JC, Tsai CC. Examining the role of feedback messages in undergraduate students' writing performance during an online peer assessment activity. The Internet and Higher Education. 2015; 25: 78-84. 10.1016/j.iheduc.2015.02.001</bibtext> </blist> <blist> <bibl id="bib7" idref="ref34" type="bt">7</bibl> <bibtext> Cheng L, Li Y, Su Y, Gao L. Effect of regulation scripts for dialogic peer assessment on feedback quality, critical thinking and climate of trust. Assessment & Evaluation in Higher Education. 2022; 48: 1-13. 10.1080/02602938.2022.2092068</bibtext> </blist> <blist> <bibl id="bib8" idref="ref23" type="bt">8</bibl> <bibtext> Choi E, Schallert DL, Jee MJ, Ko J. Transpacific telecollaboration and L2 writing: Influences of interpersonal dynamics on peer feedback and revision uptake. Journal of Second Language Writing. 2021; 54: 100855. 10.1016/j.jslw.2021.100855</bibtext> </blist> <blist> <bibl id="bib9" idref="ref16" type="bt">9</bibl> <bibtext> Day INZ, Saab N, Admiraal W. Online peer feedback on video presentations: Type of feedback and improvement of presentation skills. Assessment & Evaluation in Higher Education. 2022; 47; 2: 183-197. 10.1080/02602938.2021.1904826</bibtext> </blist> <blist> <bibtext> Dmoshinskaia N, Gijlers H, de Jong T. Learning from reviewing peers' concept maps in an inquiry context: Commenting or grading, which is better?. Studies in Educational Evaluation. 2021; 68: 100959. 10.1016/j.stueduc.2020.100959</bibtext> </blist> <blist> <bibtext> Er E, Dimitriadis Y, Gašević D. A collaborative learning approach to dialogic peer feedback: A theoretical framework. Assessment & Evaluation in Higher Education. 2021; 46; 4: 586-600. 10.1080/02602938.2020.1786497</bibtext> </blist> <blist> <bibtext> Fan Y, Xu J. Exploring student engagement with peer feedback on L2 writing. Journal of Second Language Writing. 2020; 50: 100775. 10.1016/j.jslw.2020.100775</bibtext> </blist> <blist> <bibtext> Filius RM, de Kleijn RA, Uijl SG, Prins FJ, van Rijen HV, Grobbee DE. Strengthening dialogic peer feedback aiming for deep learning in SPOCs. Computers & Education. 2018; 125: 86-100. 10.1016/j.compedu.2018.06.004</bibtext> </blist> <blist> <bibtext> Fyfe ER, Rittle-Johnson B. Feedback both helps and hinders learning: The causal role of prior knowledge. Journal of Educational Psychology. 2016; 108; 1: 82. 10.1037/edu0000053</bibtext> </blist> <blist> <bibtext> Hattie J, Timperley H. The power of feedback. Review of Educational Research. 2007; 77; 1: 81-112. 10.3102/003465430298487</bibtext> </blist> <blist> <bibtext> Huisman B, Saab N, Van Driel J, Van Den Broek P. Peer feedback on academic writing: Undergraduate students' peer feedback role, peer feedback perceptions and essay performance. Assessment & Evaluation in Higher Education. 2018; 43; 6: 955-968. 10.1080/02602938.2018.1424318</bibtext> </blist> <blist> <bibtext> Jin X, Jiang Q, Xiong W, Feng Y, Zhao W. Effects of student engagement in peer feedback on writing performance in higher education. Interactive Learning Environments. 2022; 32: 1-16. 10.1080/10494820.2022.2081209</bibtext> </blist> <blist> <bibtext> Kalyuga S. Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review. 2007; 19: 509-539. 10.1007/s10648-007-9054-3</bibtext> </blist> <blist> <bibtext> Kamp RJ, van Berkel HJ, Popeijus HE, Leppink J, Schmidt HG, Dolmans DH. Midterm peer feedback in problem-based learning groups: The effect on individual contributions and achievement. Advances in Health Sciences Education. 2014; 19: 53-69. 10.1007/s10459-013-9460-x</bibtext> </blist> <blist> <bibtext> Klueger A, DeNisi A. Effects of feedback intervention on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin. 1996; 119; 2: 254-284. 10.1037/0033-2909.119.2.254</bibtext> </blist> <blist> <bibtext> Li X, Lee CY, Lin YJ, Chu M, Qin X, Zhang S, Chiang YC. Moderating effects of teachers' praise/criticism on the bullying of vulnerable students among teenagers. Current Psychology. 2022; 42: 1-15. 10.1007/s12144-022-03032-y</bibtext> </blist> <blist> <bibtext> Liang JC, Tsai CC. Learning through science writing via online peer assessment in a college biology course. The Internet and Higher Education. 2010; 13; 4: 242-247. 10.1016/j.iheduc.2010.04.004</bibtext> </blist> <blist> <bibtext> Liu NF, Carless D. Peer feedback: The learning element of peer assessment. Teaching in Higher Education. 2006; 11; 3: 279-290. 10.1080/13562510600680582</bibtext> </blist> <blist> <bibtext> Lu J, Law N. Online peer assessment: Effects of cognitive and affective feedback. Instructional Science. 2012; 40: 257-275. 10.1007/s11251-011-9177-2</bibtext> </blist> <blist> <bibtext> Nease AA, Mudgett BO, Quiñones MA. Relationships among feedback sign, self-efficacy, and acceptance of performance feedback. Journal of Applied Psychology. 1999; 84; 5: 806. 10.1037/0021-9010.84.5.806</bibtext> </blist> <blist> <bibtext> Nelson MM, Schunn CD. The nature of feedback: How different types of peer feedback affect writing performance. Instructional Science. 2009; 37: 375-401. 10.1007/s11251-008-9053-x</bibtext> </blist> <blist> <bibtext> Noroozi O, Banihashem SK, Taghizadeh Kerman N, Parvaneh Akhteh Khaneh M, Babayi M, Ashrafi H, Biemans HJ. Gender differences in students' argumentative essay writing, peer review performance and uptake in online learning environments. Interactive Learning Environments. 2022; 31: 1-15. 10.1080/10494820.2022.2034887</bibtext> </blist> <blist> <bibtext> Patchan MM, Schunn CD, Correnti RJ. The nature of feedback: How peer feedback features affect students' implementation rate and quality of revisions. Journal of Educational Psychology. 2016; 108; 8: 1098-1120. 10.1037/edu0000103</bibtext> </blist> <blist> <bibtext> Pham HTP. Computer-mediated and face-to-face peer feedback: Student feedback and revision in EFL writing. Computer Assisted Language Learning. 2022; 35; 9: 2112-2147. 10.1080/09588221.2020.1868530</bibtext> </blist> <blist> <bibtext> Podsakoff PM, Farh JL. Effects of feedback sign and credibility on goal setting and task performance. Organizational Behavior and Human Decision Processes. 1989; 44; 1: 45-67. 10.1016/0749-5978(89)90034-4</bibtext> </blist> <blist> <bibtext> Price M, Handley K, Millar J. Feedback: Focusing attention on engagement. Studies in Higher Education. 2011; 36; 8: 879-896. 10.1080/03075079.2010.483513</bibtext> </blist> <blist> <bibtext> Riesen SA, Gijlers H, Anjeweirden AA, de Jong T. The influence of prior knowledge on the effectiveness of guided experiment design. Interactive Learning Environments. 2022; 30; 1: 17-33. 10.1080/10494820.2019.1631193</bibtext> </blist> <blist> <bibtext> Ruppert J, Duncan RG, Chinn CA. Disentangling the role of domain-specific knowledge in student modeling. Research in Science Education. 2019; 49: 921-948. 10.1007/s11165-017-9656-9</bibtext> </blist> <blist> <bibtext> Sapouna M. Evaluating the impact of the patchwork text process in criminal justice education. Innovations in Education and Teaching International. 2018; 55; 3: 376-383. 10.1080/14703297.2016.1212724</bibtext> </blist> <blist> <bibtext> Schillings M, Roebertsen H, Savelberg H, van Dijk A, Dolmans D. Improving the understanding of written peer feedback through face-to-face peer dialogue: Students' perspective. Higher Education Research & Development. 2021; 40; 5: 1100-1116. 10.1080/07294360.2020.1798889</bibtext> </blist> <blist> <bibtext> Shute VJ. Focus on formative feedback. Review of Educational Research. 2008; 78; 1: 153-189. 10.3102/0034654307313795</bibtext> </blist> <blist> <bibtext> Silver WS, Mitchell TR, Gist ME. Responses to successful and unsuccessful performance: The moderating effect of self-efficacy on the relationship between performance and attributions. Organizational Behavior and Human Decision Processes. 1995; 62; 3: 286-299. 10.1006/obhd.1995.1051</bibtext> </blist> <blist> <bibtext> Steen-Utheim A, Wittek AL. Dialogic feedback and potentialities for student learning. Learning Culture and Social Interaction. 2017; 15: 18-30. 10.1016/j.lcsi.2017.06.002</bibtext> </blist> <blist> <bibtext> Straub R. Students' reactions to teacher comments: An exploratory study. Research in the Teaching of English. 1997; 31; 1: 91-119. 10.58680/rte19973873</bibtext> </blist> <blist> <bibtext> Topping K. Peer assessment: Channels of operation. Education Sciences. 2021; 11; 3: 91. 10.3390/educsci11030091</bibtext> </blist> <blist> <bibtext> Tsao JJ. Effects of EFL learners' L2 writing self-efficacy on engagement with written corrective feedback. The Asia-Pacific Education Researcher. 2021; 30; 6: 575-584. 10.1007/s40299-021-00591-9</bibtext> </blist> <blist> <bibtext> Wang SL, Lin SS. The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Computers in Human Behavior. 2007; 23; 5: 2256-2268. 10.1016/j.chb.2006.03.005</bibtext> </blist> <blist> <bibtext> Wei W, Cheong CM, Zhu X, Lu Q. Comparing self-reflection and peer feedback practices in an academic writing task: a student self-efficacy perspective. Teaching in Higher Education. 2022; 29: 1-17. 10.1080/13562517.2022.2042242</bibtext> </blist> <blist> <bibtext> Wichmann A, Funk A, Rummel N. Leveraging the potential of peer feedback in an academic writing activity through sense-making support. European Journal of Psychology of Education. 2018; 33; 1: 165-184. 10.1007/s10212-017-0348-7</bibtext> </blist> <blist> <bibtext> Wingate U. The impact of formative feedback on the development of academic writing. Assessment & Evaluation in Higher Education. 2010; 35; 5: 519-533. 10.1080/02602930903512909</bibtext> </blist> <blist> <bibtext> Wong J, Baars M, Davis D, Van Der Zee T, Houben GJ, Paas F. Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction. 2019; 35; 4–5: 356-373. 10.1080/10447318.2018.1543084</bibtext> </blist> <blist> <bibtext> Wood J. Making peer feedback work: The contribution of technology-mediated dialogic peer feedback to feedback uptake and literacy. Assessment & Evaluation in Higher Education. 2022; 47; 3: 327-346. 10.1080/02602938.2021.1914544</bibtext> </blist> <blist> <bibtext> Wu Y, Schunn CD. From feedback to revisions: Effects of feedback features and perceptions. Contemporary Educational Psychology. 2020; 60: 101826. 10.1016/j.cedpsych.2019.101826</bibtext> </blist> <blist> <bibtext> Wu Y, Schunn CD. From plans to actions: A process model for why feedback features influence feedback implementation. Instructional Science. 2021; 49; 3: 365-394. 10.1007/s11251-021-09546-5</bibtext> </blist> <blist> <bibtext> Xun GE, Land SM. A conceptual framework for scaffolding III-structured problem-solving processes using question prompts and peer interactions. Educational Technology Research and Development. 2004; 52; 2: 5-22. 10.1007/BF02504836</bibtext> </blist> <blist> <bibtext> Yuan J, Savadatti S, Zheng G. Self-assessing a test with a possible bonus enhances low performers' academic performance. Computers & Education. 2021; 160: 104036. 10.1016/j.compedu.2020.104036</bibtext> </blist> <blist> <bibtext> Zhan Y. Are they ready? An investigation of university students' difficulties in peer assessment from dual perspectives. Teaching in Higher Education. 2021; 29: 1-18. 10.1080/13562517.2021.2021393</bibtext> </blist> <blist> <bibtext> Zong Z, Schunn C, Wang Y. What makes students contribute more peer feedback? The role of within-course experience with peer feedback. Assessment & Evaluation in Higher Education. 2022; 47; 6: 972-983. 10.1080/02602938.2021.1968792</bibtext> </blist> </ref> <aug> <p>By Keru Li; Yanyan Li; Yansu Wang; Yunshan Chen and Wanqing Hu</p> <p>Reported by Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib16" firstref="ref5"></nolink> <nolink nlid="nl2" bibid="bib13" firstref="ref6"></nolink> <nolink nlid="nl3" bibid="bib40" firstref="ref7"></nolink> <nolink nlid="nl4" bibid="bib31" firstref="ref8"></nolink> <nolink nlid="nl5" bibid="bib52" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib49" firstref="ref10"></nolink> <nolink nlid="nl7" bibid="bib44" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib48" firstref="ref13"></nolink> <nolink nlid="nl9" bibid="bib26" firstref="ref14"></nolink> <nolink nlid="nl10" bibid="bib25" firstref="ref15"></nolink> <nolink nlid="nl11" bibid="bib14" firstref="ref17"></nolink> <nolink nlid="nl12" bibid="bib45" firstref="ref18"></nolink> <nolink nlid="nl13" bibid="bib41" firstref="ref19"></nolink> <nolink nlid="nl14" bibid="bib27" firstref="ref20"></nolink> <nolink nlid="nl15" bibid="bib23" firstref="ref22"></nolink> <nolink nlid="nl16" bibid="bib34" firstref="ref25"></nolink> <nolink nlid="nl17" bibid="bib38" firstref="ref27"></nolink> <nolink nlid="nl18" bibid="bib11" firstref="ref28"></nolink> <nolink nlid="nl19" bibid="bib47" firstref="ref29"></nolink> <nolink nlid="nl20" bibid="bib35" firstref="ref31"></nolink> <nolink nlid="nl21" bibid="bib50" firstref="ref33"></nolink> <nolink nlid="nl22" bibid="bib24" firstref="ref38"></nolink> <nolink nlid="nl23" bibid="bib15" firstref="ref44"></nolink> <nolink nlid="nl24" bibid="bib36" firstref="ref45"></nolink> <nolink nlid="nl25" bibid="bib53" firstref="ref48"></nolink> <nolink nlid="nl26" bibid="bib43" firstref="ref49"></nolink> <nolink nlid="nl27" bibid="bib17" firstref="ref50"></nolink> <nolink nlid="nl28" bibid="bib37" firstref="ref56"></nolink> <nolink nlid="nl29" bibid="bib30" firstref="ref58"></nolink> <nolink nlid="nl30" bibid="bib32" firstref="ref61"></nolink> <nolink nlid="nl31" bibid="bib33" firstref="ref62"></nolink> <nolink nlid="nl32" bibid="bib18" firstref="ref63"></nolink> <nolink nlid="nl33" bibid="bib10" firstref="ref64"></nolink> <nolink nlid="nl34" bibid="bib19" firstref="ref65"></nolink> <nolink nlid="nl35" bibid="bib51" firstref="ref67"></nolink> <nolink nlid="nl36" bibid="bib46" firstref="ref68"></nolink> <nolink nlid="nl37" bibid="bib22" firstref="ref71"></nolink> <nolink nlid="nl38" bibid="bib29" firstref="ref74"></nolink> <nolink nlid="nl39" bibid="bib42" firstref="ref78"></nolink> <nolink nlid="nl40" bibid="bib21" firstref="ref85"></nolink> <nolink nlid="nl41" bibid="bib28" firstref="ref88"></nolink> <nolink nlid="nl42" bibid="bib39" firstref="ref90"></nolink> <nolink nlid="nl43" bibid="bib12" firstref="ref98"></nolink> <nolink nlid="nl44" bibid="bib20" firstref="ref100"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1460935
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Feedback Features and Revision Uptake in Dialogic Peer Feedback: The Moderating Effect of Self-Efficacy and Prior Knowledge
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Keru+Li%22">Keru Li</searchLink><br /><searchLink fieldCode="AR" term="%22Yanyan+Li%22">Yanyan Li</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8558-5911">0000-0002-8558-5911</externalLink>)<br /><searchLink fieldCode="AR" term="%22Yansu+Wang%22">Yansu Wang</searchLink><br /><searchLink fieldCode="AR" term="%22Yunshan+Chen%22">Yunshan Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Wanqing+Hu%22">Wanqing Hu</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Instructional+Science%3A+An+International+Journal+of+the+Learning+Sciences%22"><i>Instructional Science: An International Journal of the Learning Sciences</i></searchLink>. 2025 53(1):49-69.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 21
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Peer+Evaluation%22">Peer Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Self+Efficacy%22">Self Efficacy</searchLink><br /><searchLink fieldCode="DE" term="%22Prior+Learning%22">Prior Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Error+Correction%22">Error Correction</searchLink><br /><searchLink fieldCode="DE" term="%22Positive+Reinforcement%22">Positive Reinforcement</searchLink><br /><searchLink fieldCode="DE" term="%22Negative+Reinforcement%22">Negative Reinforcement</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Processes%22">Learning Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11251-024-09690-8
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0020-4277<br />1573-1952
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1460935
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1460935
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11251-024-09690-8
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 49
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: College Students
        Type: general
      – SubjectFull: Peer Evaluation
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Self Efficacy
        Type: general
      – SubjectFull: Prior Learning
        Type: general
      – SubjectFull: Error Correction
        Type: general
      – SubjectFull: Positive Reinforcement
        Type: general
      – SubjectFull: Negative Reinforcement
        Type: general
      – SubjectFull: Learning Processes
        Type: general
      – SubjectFull: Instructional Design
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Feedback Features and Revision Uptake in Dialogic Peer Feedback: The Moderating Effect of Self-Efficacy and Prior Knowledge
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Keru Li
      – PersonEntity:
          Name:
            NameFull: Yanyan Li
      – PersonEntity:
          Name:
            NameFull: Yansu Wang
      – PersonEntity:
          Name:
            NameFull: Yunshan Chen
      – PersonEntity:
          Name:
            NameFull: Wanqing Hu
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 0020-4277
            – Type: issn-electronic
              Value: 1573-1952
          Numbering:
            – Type: volume
              Value: 53
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Instructional Science: An International Journal of the Learning Sciences
              Type: main
ResultId 1