Research
Findings from LEVI Projects
Analyzing Student Attention and Acceptance of Conversational AI for Math Learning: Insights from a Randomized Controlled Trial
Analyzing Student Attention and Acceptance of Conversational AI for Math Learning: Insights from a Randomized Controlled Trial Abstract The significance of nurturing a deep conceptual understanding in math learning cannot be overstated. Grounded in the pedagogical strategies of induction, concretization,
Students’ perceived roles, opportunities, and challenges of a generative AI-powered teachable agent: a case of middle school math class
Students’ Perceived Roles, Opportunities, and Challenges of a Generative AI-powered Teachable Agent: A Case of Middle School Math Class Abstract Ongoing advancements in Generative AI (GenAI) have boosted the potential of applying long-standing “learning-by-teaching” practices in the form of a
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration Abstract Multiple choice questions (MCQs) are a popular method for evaluating students’ knowledge due to their efficiency in administration and grading. Crafting high-quality math MCQs is a labor-intensive process that
Automated Feedback for Student Math Responses Based on Multi-Modality and Fine-Tuning
Automated Feedback for Student Math Responses Based on Multi-Modality and Fine-Tuning Abstract Open-ended mathematical problems are a commonly used method for assessing students’ abilities by teachers. In previous automated assessments, natural language processing focusing on students’ textual answers has been
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning Abstract Automatically generating feedback via large language models (LLMs) in intelligent tutoring systems and online learning platformshas the potential to improve the learning outcomes of many students.However, both feedback generation
An Evaluation of Perceptions Regarding Mentor Competencies for Technology-based Personalized Learning
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.