# Optimizing dental students’ use of feedback: Validation of a theoretical model

## TL;DR

This study validates a model that identifies factors influencing how dental students use feedback, showing some demographic factors affect feedback engagement.

## Contribution

The study validates a theoretical model of feedback use among dental students and identifies demographic influences on feedback-related components.

## Key findings

- Nine key components influencing feedback use were identified, including feedback utility, credibility, and accessibility.
- Seven components were influenced by gender, nationality, ethnicity, and educational background, but not by age, year group, or religion.
- The model was validated, showing demographic differences in feedback engagement that can inform educational practices.

## Abstract

A model describing the factors that influence the use of feedback by dental students from three UK dental schools has been proposed. Seven factors that influence feedback utilization were identified. This project aims to test the validity of this model in a larger student population from two UK dental schools in Newcastle upon Tyne and Cardiff.

An electronic questionnaire was created and circulated to two dental schools. Data from schools were combined and analyzed using SPSS software. A total of 304 responses were analyzed using principal component analysis resulting in the extraction of nine components which were identified as “most important.” The Kruskal–Wallis test was used to analyze the correlations of these components with the demographic characteristics of participants.

The nine components were feedback utility, feedback value, credibility, learning approach, the learner's understanding of the feedback's purpose, and its processes, accessibility, future applicability, and institutional processes. According to the Kruskal–Wallis analysis, seven of the components (but not credibility or institutional processes) were found to be influenced by differences in the gender, nationality, and ethnicity of students as well as their previous educational experience. However, no evidence was found that variables such as age, student year group, or religious beliefs affected any of these components.

Some demographic characteristics are more engaged with one or more components than others. Understanding that will help optimizing this model and ultimately benefits students and institutions. The main project's goal was met by the research in validating the previously developed model.