Thanks and appreciation: Financing and supporting the research of the explanatory comments
Links table
Abstract and 1 introduction
2. The background
2.1 Effective teaching practice
2.2 Reactions for teacher training
2.3 Signs sequence to generate comments
2.4 Great language models in education
3. The method
3.1 Data set and 3.2 sequence modes
3.3 GPT made signs of sequences
3.4 Measurements
4. Results
4.1 Results on RQ1
4.2 Results on RQ2
5. Discussion
6. Restrictions and future works
7. Conclusion
8. Thanks and appreciation
9. References
Excessive
Lesson principles
B. Inputs to set GPT-3.5
Jim is scattered from the relationship on the results based on the results
D. Detailed Results for the GPT-3.5 performance that has been seized
8. Thanks and appreciation
This work is supported by financing from the Richard Kung Mellon Foundation (Grant No. 10851) and the Virtual Institute of Educational Engineering (https: // learning- Virtu Al-Institue.org/). Any opinions, results and conclusions expressed in this paper are the opinions of the authors. We also would like to express our gratitude to Dr. Ralph Abboud and Dr. Caroline B. Rose for their recommendations and recommendations, and to Yiang Zhao Wining Wang to help check the classification scheme.
This paper is available on arxiv under CC with a verb license 4.0.
Authors:
(1) Jeonhao Lin, University of Carnegie Mellon ([email protected]);
(2) Ison Chen, University of Carnegie Mellon ([email protected]);
(3) Zivi Han, University of Toronto ([email protected]);
(4) Ashish Gurong, University of Carnegie Mellon ([email protected]);
(5) Daniel R. Thomas, University of Carnegie Mellon ([email protected]);
(6) Wei Tan, Monash University ([email protected]);
(7) Ngook Dang Ngwin, Monash University ([email protected]);
(8) Kenneth R. Keidinger, Carnegie Mellon University ([email protected]).
