Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Background 2.1 Effective Tutoring Practice 2.1 Effective Tutoring Practice 2.2 Feedback for Tutor Training 2.2 Feedback for Tutor Training 2.3 Sequence Labeling for Feedback Generation 2.3 Sequence Labeling for Feedback Generation 2.4 Large Language Models in Education 2.4 Large Language Models in Education 3. Method 3.1 Dataset and 3.2 Sequence Labeling 3.1 Dataset and 3.2 Sequence Labeling 3.3 GPT Facilitated Sequence Labeling 3.3 GPT Facilitated Sequence Labeling 3.4 Metrics 3.4 Metrics 4. Results 4.1 Results on RQ1 4.1 Results on RQ1 4.2 Results on RQ2 4.2 Results on RQ2 5. Discussion 5. Discussion 6. Limitation and Future Works 6. Limitation and Future Works 7. Conclusion 7. Conclusion 8. Acknowledgments 8. Acknowledgments 9. References 9. References APPENDIX APPENDIX A. Lesson Principles A. Lesson Principles B. Input for Fine-Tunning GPT-3.5 B. Input for Fine-Tunning GPT-3.5 C. Scatter Matric of the Correlation on the Outcome-based Praise C. Scatter Matric of the Correlation on the Outcome-based Praise D. Detailed Results of Fine-Tuned GPT-3.5 Model's Performance 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. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Jionghao Lin, Carnegie Mellon University (jionghal@cs.cmu.edu);
(2) Eason Chen, Carnegie Mellon University (easonc13@cmu.edu);
(3) Zeifei Han, University of Toronto (feifei.han@mail.utoronto.ca);
(4) Ashish Gurung, Carnegie Mellon University (agurung@andrew.cmu.edu);
(5) Danielle R. Thomas, Carnegie Mellon University (drthomas@cmu.edu);
(6) Wei Tan, Monash University (wei.tan2@monash.edu);
(7) Ngoc Dang Nguyen, Monash University (dan.nguyen2@monash.edu);
(8) Kenneth R. Koedinger, Carnegie Mellon University (koedinger@cmu.edu). Authors: Authors: (1) Jionghao Lin, Carnegie Mellon University (jionghal@cs.cmu.edu); (2) Eason Chen, Carnegie Mellon University (easonc13@cmu.edu); (3) Zeifei Han, University of Toronto (feifei.han@mail.utoronto.ca); (4) Ashish Gurung, Carnegie Mellon University (agurung@andrew.cmu.edu); (5) Danielle R. Thomas, Carnegie Mellon University (drthomas@cmu.edu); (6) Wei Tan, Monash University (wei.tan2@monash.edu); (7) Ngoc Dang Nguyen, Monash University (dan.nguyen2@monash.edu); (8) Kenneth R. Koedinger, Carnegie Mellon University (koedinger@cmu.edu).

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Acknowledgments: Funding and Support for Explanatory Feedback Research

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