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Found 19 papers, 10 papers with code
Optimizing Mastery Learning by Fast-Forwarding Over-Practice Steps
Mastery learning improves learning proficiency and efficiency.
LLM-Generated Feedback Supports Learning If Learners Choose to Use It
After adjusting for this effect, two out of seven lessons showed statistically significant learning benefits from LLM feedback with standardized effect sizes of 0. 28 and 0. 33.
Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study
Tutoring improves student achievement, but identifying and studying what tutoring actions are most associated with student learning at scale based on audio transcriptions is an open research problem.
An Integrated Platform for Studying Learning with Intelligent Tutoring Systems: CTAT+TutorShop
This platform has been used to develop and conduct an estimated 147 research studies which have run in a wide variety of laboratory and real-world educational settings, including K-12 and higher education, and have addressed a wide range of research questions.
Augmenting Human-Annotated Training Data with Large Language Model Generation and Distillation in Open-Response Assessment
We conclude that integrating human and LLM-generated data to improve text classification models in assessment offers a scalable solution that leverages both the accuracy of human coding and the variety of LLM outputs.
Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA
Specifically, we address (1) whether ABROCA follows any known distribution, (2) how to reliably test for algorithmic bias using ABROCA, and (3) the statistical power achievable with ABROCA-based bias assessments under typical EDM sample specifications.
Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support
We contribute insights into how tutoring systems can best be merged with LLMs to support hybrid tutoring settings through conversational assistance, facilitating effective caregiver involvement in tutoring systems.
Do Tutors Learn from Equity Training and Can Generative AI Assess It?
Advances in generative AI via large language models (LLMs) are being used in a wide range of applications, with this present work assessing its use in the equity domain.
Does Multiple Choice Have a Future in the Age of Generative AI? A Posttest-only RCT
Using a posttest-only randomized control design, we compare the performance of 234 tutors (790 lesson completions) across three conditions: MCQ only, open response only, and a combination of both.
Evaluating the Impact of Data Augmentation on Predictive Model Performance
This paper systematically compares data augmentation techniques and their impact on prediction performance in a typical LA task: prediction of academic outcomes.
ABROCA Distributions For Algorithmic Bias Assessment: Considerations Around Interpretation
When assessing whether a classifier is biased, this skewness inflates ABROCA values by chance, even when data is drawn (by simulation) from populations with equivalent ROC curves.
Data Augmentation for Sparse Multidimensional Learning Performance Data Using Generative AI
Second, a tensor factorization method is used to impute missing values in sparse tensors of collected learner data, thereby grounding the imputation on knowledge tracing tasks that predict missing performance values based on real observations.
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities.
Improving Assessment of Tutoring Practices using Retrieval-Augmented Generation
The results indicate that the RAG prompt demonstrated more accurate performance (assessed by the level of hallucination and correctness in the generated assessment texts) and lower financial costs than the other strategies evaluated.
3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
Learning performance data (e. g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level.
Revealing Networks: Understanding Effective Teacher Practices in AI-Supported Classrooms using Transmodal Ordered Network Analysis
Taken together, offering early conceptual support to students with low learning rates could make classroom practice with AI tutors more effective.
Using Think-Aloud Data to Understand Relations between Self-Regulation Cycle Characteristics and Student Performance in Intelligent Tutoring Systems
We discuss system re-design opportunities to add SRL support during stages of processing and paths forward for using machine learning to speed research depending on the assessment of SRL based on transcription of think-aloud data.
Insights into undergraduate pathways using course load analytics
Course load analytics (CLA) inferred from LMS and enrollment features can offer a more accurate representation of course workload to students than credit hours and potentially aid in their course selection decisions.
Looking for a Handsome Carpenter! Debiasing GPT-3 Job Advertisements
The growing capability and availability of generative language models has enabled a wide range of new downstream tasks.
