# Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content Mastery for Predicting Math Achievement

arXiv: https://arxiv.org/abs/2405.06218 · HTML: https://mlsanity.com/paper/2405.06218/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 6 of 6)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Hyperparameters and Tuning Strategies for Random Forest](https://mlsanity.com/paper/1804.03515/) (2018)
- [Assessing Competency Using Metacognition and Motivation: The Role of Time-Awareness in Preparation for Future Learning](https://mlsanity.com/paper/2303.14609/) (2023)
- [Preparing Unprepared Students For Future Learning](https://mlsanity.com/paper/2303.11960/) (2023)
- [The Power of Nudging: Exploring Three Interventions for Metacognitive Skills Instruction across Intelligent Tutoring Systems](https://mlsanity.com/paper/2303.11965/) (2023)
- [Mixing Backward- with Forward-Chaining for Metacognitive Skill Acquisition and Transfer](https://mlsanity.com/paper/2303.12223/) (2023)

## Built on by (most cited first, 3 of 3)

- [Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs](https://mlsanity.com/paper/2409.16490/) (2024)
- [Exploring LLMs for Predicting Tutor Strategy and Student Outcomes in Dialogues](https://mlsanity.com/paper/2507.06910/) (2025)
- [Predicting Long Term Sequential Policy Value Using Softer Surrogates](https://mlsanity.com/paper/2412.20638/) (2024)

## Abstract (arXiv)

This work investigates how tutoring discourse interacts with students' proximal knowledge to explain and predict students' learning outcomes. Our work is conducted in the context of high-dosage human tutoring where 9th-grade students (N= 1080) attended small group tutorials and individually practiced problems on an Intelligent Tutoring System (ITS). We analyzed whether tutors' talk moves and students' performance on the ITS predicted scores on math learning assessments. We trained Random Forest Classifiers (RFCs) to distinguish high and low assessment scores based on tutor talk moves, student's ITS performance metrics, and their combination. A decision tree was extracted from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity among the two feature sources. Specifically, the best decision tree emerged from combining the tutor talk moves that encouraged rigorous thinking and students' ITS mastery. In essence, tutor talk that encouraged mathematical reasoning predicted achievement for students who demonstrated high mastery on the ITS, whereas tutors' revoicing of students' mathematical ideas and contributions was predictive for students with low ITS mastery. Implications for practice are discussed.