Relational feedback framework for designing feedback that strengthens student–instructor relationships¶

Theory · All theories
Evidence · 3 claims (3 for) · 2 studies (1 causal, 1 theoretical), q1–q2 · 0 of 2 report an effect size · 3 claims rest on one study

Description¶

The relational feedback framework, proposed by Dai and colleagues (2025) and adopted as this study's theoretical basis, holds that feedback can enhance its effectiveness by strengthening students' relationships with the learning community, including instructors and peers. It prescribes four principles: "feedback should (i) clarify students' performance, (ii) contain actionable information for students to improve their learning, (iii) encourage students to communicate with the learning community, and (iv) evoke students' positive emotions." The study operationalized these principles in intervention emails sent to predictively identified at-risk students.

Design Implications¶

Context¶

Requirements¶

• Feedback must be designed with expertise in feedback research; four feedback researchers collaborated on drafting and revising the message.
• Trace data on students' unvisited learning activities is needed to supply the actionable, performance-clarifying content.

Constraints¶

• At-risk students' lower participation can create distance from instructors and peers, which might result in these students not paying attention to, or even disregarding, the feedback provided.

Target Learners¶

• Undergraduate students identified as at risk of failing an introductory programming course

Target Learning Objectives¶

• Increased behavioural engagement with online learning activities
• Uptake of feedback and communication with the learning community

Claims¶

• Relational Feedback Email Increases Activity Visits [+M]
• At Risk Students Satisfied Prefer Frequent Feedback [+M]
• Strengthening the student-educator relationship can contribute positively to marginalised students' self-efficacy and sense of belonging in higher education [+W]

Related Theories¶

Examples¶

• Design at-risk intervention emails by mapping message sentences to the four relational feedback principles with expert collaboration

Key Sources¶

• Wei Dai, Jionghao Lin, Flora Ji-Yoon Jin, Yi-Shan Tsai, Namrata Srivastava, Pierre Le Bodic, Dragan Gašević, and Guanliang Chen. (2025). Learning Analytics for Early Identification of At-Risk Students and Feedback Intervention. Journal of Learning Analytics, 12(3), 102–125. https://doi.org/10.18608/jla.2025.8735