Fetching the paper…
Reading the bibliography…
Research suggests that tutors should adopt a strategic approach when addressing math errors made by low-efficacy students.
Effective tutoring techniques: A comparison of human tutors and intelligent tutoring systems
Douglas C Merrill, Brian J Reiser, Michael Ranney, and J Gregory Trafton · 1992
Earlier work this paper cites.
Motivational techniques of expert human tutors: Lessons for the design of computer-based tutors. computers as cognitive tools. sp lajoie, & sj, derry, hillsdate, 1993
MR Lepper, M Woolverton, D Mumme, and J Gurtner · 1993
Earlier work this paper cites.
Tutoring: Guided learning by doing
Douglas C Merrill, Brian J Reiser, Shannon K Merrill, and Shari Landes · 1995
Earlier work this paper cites.
Transfer of learning: Concept and process
Cathlin Macaulay and Viviene E Cree · 1999
Earlier work this paper cites.
The wisdom of practice: Lessons learned from the study of highly effective tutors
Mark R Lepper and Maria Woolverton · 2002
Earlier work this paper cites.
The work of teaching and the challenge for teacher education
Deborah Loewenberg Ball and Francesca M Forzani · 2009
Earlier work this paper cites.
Kappa coefficient: a popular measure of rater agreement
Tang Wan, Hu Jun, Hui Zhang, Wu Pan, and He Hua · 2015
Earlier work this paper cites.
Do you think you can? the influence of student self-efficacy on the effectiveness of tutorial dialogue for computer science
Joseph B Wiggins, Joseph F Grafsgaard, Kristy Elizabeth Boyer, Eric N Wiebe, and James C Lester · 2017
Earlier work this paper cites.
Fostering the intelligent novice: Learning from errors with metacognitive tutoring
Santosh A Mathan and Kenneth R Koedinger · 2018
Cited alongside, same era.
A blueprint for scaling tutoring and mentoring across public schools
Matthew A Kraft and Grace T Falken · 2021
Cited alongside, same era.
The national online tuition pilot
Lydia Marshall, Jonah Bury, Robert Wishart, Rebekka Hammelsbeck, and Emily Roberts · 2021
Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell · 2021
Cited alongside, same era.
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen · 2022
Cited alongside, same era.
Can large language models provide feedback to students? a case study on chatgpt
Wei Dai, Jionghao Lin, Hua Jin, Tongguang Li, Yi-Shan Tsai, Dragan Gašević, and Guanliang Chen · 2023
Later among the works it cites.
Gpt-3.5, gpt-4, or bard? evaluating llms reasoning ability in zero-shot setting and performance boosting through prompts
Jessica López Espejel, El Hassane Ettifouri, Mahaman Sanoussi Yahaya Alassan, El Mehdi Chouham, and Walid Dahhane · 2023
Later among the works it cites.
Measuring five accountable talk moves to improve instruction at scale
Ashlee Kupor, Candice Morgan, and Dorottya Demszky · 2023
Later among the works it cites.
OpenAI, 2023
OpenAI · 2023
Later among the works it cites.
When the tutor becomes the student: Design and evaluation of efficient scenario-based lessons for tutors
Danielle Thomas, Xinyu Yang, Shivang Gupta, Adetunji Adeniran, Elizabeth Mclaughlin, and Kenneth Koedinger · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Pallavi Chhabra, Danielle Chine, Adetunji Adeniran, Shivang Gupta, and Kenneth Koedinger · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Rose E Wang and Dorottya Demszky · 2023
Later among the works it cites.
Step-by-step remediation of students’ mathematical mistakes
Rose E Wang, Qingyang Zhang, Carly Robinson, Susanna Loeb, and Dorottya Demszky · 2023
Later among the works it cites.