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An Evaluation of Perceptions Regarding Mentor Competencies for Technology-based Personalized Learning

Carnegie Mellon University,  Public Good

This study discusses the development of Personalized Learning 2 (PL2), an online human mentoring system. PL2 uses student math learning data and mentor input to write custom feedback. This particular research is focused on finding a more efficient and research-based way to organize resources for PL2. 18 PL2 partner members completed a survey that revealed that Engaging and Motivating Students was the most important skill and Underingstanding Educational Norms and Policies was the least important. Reorganization will optimize mentor training and their ability to help students overcome barriers.

June 4, 2025 / Comments Off on An Evaluation of Perceptions Regarding Mentor Competencies for Technology-based Personalized Learning
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Development of Scenario-Based Mentor Lessons

Carnegie Mellon University,  Public Good

This demonstration shows the recent advancement of scenario based tutor training and its focus on using the learn-by-doing approach. The 15 minute lessons outlined in this study use the predict-observe-explain inquiry method to develop tutor skills in helping student motivation. These methods are being developed within the Personalized Learning2 (PL2) program. PL2 is an app that combines student software with human tutors to improve mentoring ability. Enhancing mentor training will help to increase student ability while also maintaining low costs. This form of training works best when tutors have scenario based practice with response specific feedback.

March 22, 2024 / Comments Off on Development of Scenario-Based Mentor Lessons
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Rewriting Math Word Problems with Large Language Models

Carnegie Learning,  Public Good

In a recent study, math problems in Carnegie Learning’s MATHia adaptive learning software were rewritten by human authors and AI to improve clarity. Findings showed that readers spent less time reading rewritten human content and achieved higher mastery than did readers who read the original content. The team conducting the study also used GPT-4 to rewrite the same set of math word problems with the same guidelines that the human authors used. comparing zero-shot, few-shot and chain-of-thought prompting strategies. Overall, report analysis of human-written, original and GTP-written problems showed that GTP rewrites have the most optimal readability, lexical diversity and cohesion scores, though used more low frequency words. Carnegie Learning plans to present their outputs at randomized field trials in MATHia.

March 12, 2024 / Comments Off on Rewriting Math Word Problems with Large Language Models
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Scenario-Based Training and On-The-Job Support for Equitable Mentoring

Carnegie Mellon University,  Public Good

Personalized Learning2 (PL2) is a professional mentoring platform created by researchers at Carnegie Mellon. Its goal is to improve workplace efficiency and utilize personalized learning to teach through situation-based instruction. PL2 combines both AI and mentor driven research training to help under-trained tutors with personalized learning. This platform includes social-emotional learning, math content and culturally responsive teaching practices to address the gap between historically marginalized students by training tutors to be more efficient and productive. PL2 offers a lower cost option for deliberate practice in order to increase impact and learning capacity of tutors.

March 12, 2024 / Comments Off on Scenario-Based Training and On-The-Job Support for Equitable Mentoring
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Computer-Supported Human Mentoring

Carnegie Mellon University,  Public Good

In a recent study, math problems in Carnegie Learning’s MATHia adaptive learning software were rewritten by human authors and AI to improve clarity. Findings showed that readers spent less time reading rewritten human content and achieved higher mastery than did readers who read the original content. The team conducting the study also used GPT-4 to rewrite the same set of math word problems with the same guidelines that the human authors used. comparing zero-shot, few-shot and chain-of-thought prompting strategies. Overall, report analysis of human-written, original and GTP-written problems showed that GTP rewrites have the most optimal readability, lexical diversity and cohesion scores, though used more low frequency words. Carnegie Learning plans to present their outputs at randomized field trials in MATHia.

March 12, 2024 / Comments Off on Computer-Supported Human Mentoring
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New LLMs for Math Education

Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

Public Good

Interactive question-answering (QA) with tutors has shown to be an effective way for middle school math students to learn. While not all students have access to a tutor, large language models make it possible to automate portions of the tutoring process–including interactive QA to support students’ conceptual discussion of mathematical concepts. Some have questioned how LLM responses can be better aligned with a school’s curriculum. In this paper, Levonian and colleagues explore how retrieval-augmented generation (RAG) can help improve response quality by incorporating textbook information and other educational resources, while also identifying trade-offs of using RAG.

January 22, 2024 / Comments Off on Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference
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