Putting excellence first: How rubric performance level order and feedback type influence students’ reading patterns and task performance
Background: Rubrics are structured assessment tools that describe criteria and levels of performance, helping students understand expectations and improve their work. They are widely used to support learning in educational settings. However, little is known about how students process rubrics in real...
| Authors: | , , , , , |
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| Format: | article |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/174091 |
| Online Access: | https://hdl.handle.net/11441/174091 https://doi.org/10.1016/j.learninstruc.2025.102168 |
| Access Level: | Open access |
| Keyword: | Rubric Feedback Reading patterns Eye-tracking Academic performance |
| Summary: | Background: Rubrics are structured assessment tools that describe criteria and levels of performance, helping students understand expectations and improve their work. They are widely used to support learning in educational settings. However, little is known about how students process rubrics in real time, and empirical research on rubric design and feedback effects is limited. Aim: This study examines how university students engage with rubrics during two landscape analysis tasks, focusing on two variables: the order of performance levels (highest first vs. last) and the type of feedback received (no feedback [control], process-based, product-based, or rubric-based). By combining eye-tracking and think-aloud protocols, the study offers a multimodal perspective on students’ visual attention and cognitive engagement. Sample: Eighty undergraduate students from six degree programs were randomly assigned to one of four feedback conditions. Methods: A randomized controlled trial was conducted. Eye-tracking data—fixation times, number of visits, and gaze transitions—and verbal data from think-aloud protocols were collected across task phases. Integrating these process-tracing methods enabled detailed analysis of how students interacted with the rubric and how engagement related to performance. Results: Students focused primarily on the highest performance level, especially when it appeared first. Visual attention to this level predicted task performance; verbal references did not. Rubric-based feedback increased visual alignment between rubric and task, while process-based feedback led to the strongest performance gains. Conclusion: Rubric design and feedback type significantly influence student engagement and performance. Eyetracking and think-aloud data provide complementary insights, reinforcing rubrics’ instructional value when paired with targeted feedback. |
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