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One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors.
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Torres, J.: Feedback as open-ended conversation: Inviting students to coregulate and metacognitively reflect during assessment. Journal of the Scholarship of Teaching and Learning 22
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MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., Huang, Z.: Generating diverse code explanations using the gpt-3 large language model. In: Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 2, pp. 37–39 (2022)
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Lin, J., Rakovic, M., Lang, D., Gasevic, D., Chen, G.: Exploring the politeness of instructional strategies from human-human online tutoring dialogues. In: LAK22: 12th International Learning Analytics and Knowledge Conference, pp. 282–293 (2022)
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Thomas, D., Yang, X., Gupta, S., Adeniran, A., Mclaughlin, E., Koedinger, K.: When the tutor becomes the student: Design and evaluation of efficient scenario-based lessons for tutors. In: LAK23: 13th International Learning Analytics and Knowledge Conference, pp. 250–261 (2023)
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Lin, J., Raković, M., Xie, H., Lang, D., Gašević, D., Chen, G., Li, Y.: On the role of politeness in online human–human tutoring. British Journal of Educational Technology (2023)
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Dai, W., Tsai, Y.-S., Lin, J., Aldino, A., Jin, H., Li, T., Gaševic, D., Chen, G.: Assessing the proficiency of large language models in automatic feedback generation: An evaluation study (2024)
2024
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Lin, J., Chen, E., Han, Z., Gurung, A., Thomas, D.R., Tan, W., Nguyen, N.D., Koedinger, K.R.: How Can I Improve? Using GPT to Highlight the Desired and Undesired Parts of Open-ended Responses (2024)
2024
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