Acknowledgments: Funding and Support for Explanatory Feedback Research

Written by highlighter | Published 2025/05/30
Tech Story Tags: educational-ai | research-funding | research-support | explanatory-feedback | dr.-ralph-abboud | automated-feedback | educational-technology | gpt-models

TLDRWe acknowledge the funding from the Richard King Mellon Foundation and the Learning Engineering Virtual Institute, as well as the invaluable guidance from key collaborators for this research.via the TL;DR App

Table of Links

Abstract and 1 Introduction

2. Background

2.1 Effective Tutoring Practice

2.2 Feedback for Tutor Training

2.3 Sequence Labeling for Feedback Generation

2.4 Large Language Models in Education

3. Method

3.1 Dataset and 3.2 Sequence Labeling

3.3 GPT Facilitated Sequence Labeling

3.4 Metrics

4. Results

4.1 Results on RQ1

4.2 Results on RQ2

5. Discussion

6. Limitation and Future Works

7. Conclusion

8. Acknowledgments

9. References

APPENDIX

A. Lesson Principles

B. Input for Fine-Tunning GPT-3.5

C. Scatter Matric of the Correlation on the Outcome-based Praise

D. Detailed Results of Fine-Tuned GPT-3.5 Model's Performance

8. ACKNOWLEDGMENTS

This work is supported by funding from the Richard King Mellon Foundation (Grant #10851) and the Learning Engineering Virtual Institute (https://learning-engineering-virtu al-institute.org/). Any opinions, findings, and conclusions expressed in this paper are those of the authors. We also wish to express our gratitude to Dr. Ralph Abboud and Dr. Carolyn P. Ros´e for their invaluable guidance and recommendations, and to Yiyang Zhao and Yuting Wang for their assistance in verifying the rating scheme.

This paper is available on arxiv under CC BY 4.0 DEED license.

Authors:

(1) Jionghao Lin, Carnegie Mellon University ([email protected]);

(2) Eason Chen, Carnegie Mellon University ([email protected]);

(3) Zeifei Han, University of Toronto ([email protected]);

(4) Ashish Gurung, Carnegie Mellon University ([email protected]);

(5) Danielle R. Thomas, Carnegie Mellon University ([email protected]);

(6) Wei Tan, Monash University ([email protected]);

(7) Ngoc Dang Nguyen, Monash University ([email protected]);

(8) Kenneth R. Koedinger, Carnegie Mellon University ([email protected]).


Written by highlighter | Shining light on key points, making the vital stand out, guiding eyes to what matters most.
Published by HackerNoon on 2025/05/30