Dissertations, Theses, and Capstone Projects

Date of Degree

9-2025

Document Type

Doctoral Dissertation

Degree Name

Doctor of Philosophy

Program

Educational Psychology

Advisor

Anastasiya A. Lipnevich

Committee Members

Steven J. Holochwost

Bixi Zhang

Basak Calik

Anna Serbati

Subject Categories

Educational Assessment, Evaluation, and Research | Educational Psychology

Keywords

feedback, writing, exemplars, emotions, AI, expectations

Abstract

This dissertation explores how the characteristics of instructional feedback, such as its source, recipient characteristics, and context, may affect students’ engagement, emotional responses, and performance. Drawing on the Student-Feedback Interaction Model (Lipnevich & Smith, 2022), it presents three experimental studies designed to examine the dynamic interplay between feedback and learner characteristics across diverse educational contexts.

The first study investigated the effects of three feedback sources: teacher comments, annotated exemplars, and their combination on Brazilian middle school students’ writing performance. It also explores whether recipient characteristics, such as gender and educational level, moderate the influence of feedback. The findings from this study provided support to the use of feedback in revisions of writing tasks, regardless the source. Students in all three conditions showed significant performance improvements (especially girls), but no difference among the experimental groups were found.

The second study expands the analysis of recipient characteristics by focusing on students’ expectations, personality traits, and emotions. Using a situational judgment test with undergraduate students from the United States and New Zealand, this study examines how expectation-performance (mis)alignment shapes anticipated engagement with feedback. The results suggested that it was not the expectation, but the performance that influenced students’ intentions to act on the feedback. That is, students assigned to the low performance conditions (High Expectation/Low Result and Low Expectation/Low Result) reported significantly higher intention to take actions towards that feedback. We also found significant mediation effect of the emotion interest and significant moderation of personality traits.

The third experimental study shifts focus to the growing use of artificial intelligence in education, comparing teacher comments, AI-generated feedback, and simulated teacher feedback across both secondary (in Brazil) and higher education (in the US) contexts. Together, these studies aim to extend theoretical understanding of how feedback is interpreted and acted upon, while offering scalable, evidence-based alternatives to traditional teacher feedback. By including participants from three countries and varying educational levels, this work contributes to a more generalizable and contextually rich understanding of feedback practices in contemporary classrooms.

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