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Found 48 papers, 29 papers with code
SMART: Simulated Students Aligned with Item Response Theory for Question Difficulty Prediction
Item (question) difficulties play a crucial role in educational assessments, enabling accurate and efficient assessment of student abilities and personalization to maximize learning outcomes.
LookAlike: Consistent Distractor Generation in Math MCQs
Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education.
The StudyChat Dataset: Student Dialogues With ChatGPT in an Artificial Intelligence Course
\textbf{StudyChat} provides a rich resource for the learning sciences and AI in education communities, enabling further research into the evolving role of LLMs in education.
From Text to Visuals: Using LLMs to Generate Math Diagrams with Vector Graphics
We address three research questions: (1) how to automatically generate math diagrams in problem-solving hints and evaluate their quality, (2) whether SVG is an effective intermediate representation for math diagrams, and (3) what prompting strategies and formats are required for LLMs to generate accurate SVG-based diagrams.
Training LLM-based Tutors to Improve Student Learning Outcomes in Dialogues
Specifically, we generate a set of candidate tutor utterances and score them using (1) an LLM-based student model to predict the chance of correct student responses and (2) a pedagogical rubric evaluated by GPT-4o.
Learning Code-Edit Embedding to Model Student Debugging Behavior
Providing effective feedback for programming assignments in computer science education can be challenging: students solve problems by iteratively submitting code, executing it, and using limited feedback from the compiler or the auto-grader to debug.
Automated Knowledge Component Generation and Knowledge Tracing for Coding Problems
Knowledge components (KCs) mapped to problems help model student learning, tracking their mastery levels on fine-grained skills thereby facilitating personalized learning and feedback in online learning platforms.
Whose story is it? Personalizing story generation by inferring author styles
Human evaluation highlights the high quality of our Author Writing Sheet and provides valuable insights into the personalized story generation task.
Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams
Recent advances in generative artificial intelligence (AI) have shown promise in accurately grading open-ended student responses.
Test Case-Informed Knowledge Tracing for Open-ended Coding Tasks
In this paper, we introduce Test case-Informed Knowledge Tracing for Open-ended Coding (TIKTOC), a framework to simultaneously analyze and predict both open-ended student code and whether the code passes each test case.
Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs
Recent advances in large language models (LLMs) have led to the development of artificial intelligence (AI)-powered tutoring chatbots, showing promise in providing broad access to high-quality personalized education.
Exploring Automated Keyword Mnemonics Generation with Large Language Models via Overgenerate-and-Rank
In this paper, we study an under-explored area of language and vocabulary learning: keyword mnemonics, a technique for memorizing vocabulary through memorable associations with a target word via a verbal cue.
DiVERT: Distractor Generation with Variational Errors Represented as Text for Math Multiple-choice Questions
High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real students is difficult.
Interpreting Latent Student Knowledge Representations in Programming Assignments
Recent advances in artificial intelligence for education leverage generative large language models, including using them to predict open-ended student responses rather than their correctness only.
Can Large Language Models Replicate ITS Feedback on Open-Ended Math Questions?
We fine-tune both open-source and proprietary LLMs on real student responses and corresponding ITS-provided feedback.
Generating Feedback-Ladders for Logical Errors in Programming using Large Language Models
These methods ask the LLM to generate feedback given the problem statement and a student's (buggy) submission.
Math Multiple Choice Question Generation via Human-Large Language Model Collaboration
We conduct a pilot study involving math educators to investigate how the tool can help them simplify the process of crafting high-quality math MCQs.
Improving Automated Distractor Generation for Math Multiple-choice Questions with Overgenerate-and-rank
Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale.
Exploring Automated Distractor Generation for Math Multiple-choice Questions via Large Language Models
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable format in assessments and practices.
SyllabusQA: A Course Logistics Question Answering Dataset
Automated teaching assistants and chatbots have significant potential to reduce the workload of human instructors, especially for logistics-related question answering, which is important to students yet repetitive for instructors.
Improving the Validity of Automatically Generated Feedback via Reinforcement Learning
Second, we propose a framework for feedback generation that optimizes both correctness and alignment using reinforcement learning (RL).
Improving Socratic Question Generation using Data Augmentation and Preference Optimization
The Socratic method is a way of guiding students toward solving a problem independently without directly revealing the solution to the problem.
Using Large Language Models for Student-Code Guided Test Case Generation in Computer Science Education
The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowledge, which is important for two reasons.
Automated Distractor and Feedback Generation for Math Multiple-choice Questions via In-context Learning
Multiple-choice questions (MCQs) are ubiquitous in almost all levels of education since they are easy to administer, grade, and are a reliable form of assessment.
Improving Reading Comprehension Question Generation with Data Augmentation and Overgenerate-and-rank
Reading comprehension is a crucial skill in many aspects of education, including language learning, cognitive development, and fostering early literacy skills in children.
Interpretable Math Word Problem Solution Generation Via Step-by-step Planning
We propose a step-by-step planning approach for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps.
Modeling and Analyzing Scorer Preferences in Short-Answer Math Questions
In this paper, we investigate a collection of models that account for the individual preferences and tendencies of each human scorer in the automated scoring task.
RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning
Recent developments in large pre-trained language models have enabled unprecedented performance on a variety of downstream tasks.
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
In this paper, we take a preliminary step towards solving the problem of causal discovery in knowledge tracing, i. e., finding the underlying causal relationship among different skills from real-world student response data.
SmartPhone: Exploring Keyword Mnemonic with Auto-generated Verbal and Visual Cues
Our approach, an end-to-end pipeline for auto-generating verbal and visual cues, can automatically generate highly memorable cues.
Algebra Error Classification with Large Language Models
Existing data-driven methods avoid these limitations but specifically require mathematical expressions in student responses to be parsed into syntax trees.
Tree-Based Representation and Generation of Natural and Mathematical Language
In this paper, we propose a series of modifications to existing language models to jointly represent and generate text and math: representing mathematical expressions as sequences of node tokens in their operator tree format, using math symbol and tree position embeddings to preserve the semantic and structural properties of mathematical expressions, and using a constrained decoding method to generate mathematically valid expressions.
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student groups due to data availability biases.
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning
To learn better representations of student activity, we augment our approach with a self-supervised behavioral pretraining methodology that leverages multiple modalities of student behavior (e. g., visits to lecture videos and participation on forums), and include a neural network attention mechanism in the model aggregation stage.
Automatic Short Math Answer Grading via In-context Meta-learning
In this paper, we study the problem of automatic short answer grading for students' responses to math questions and propose a novel framework for this task.
Automated Scoring for Reading Comprehension via In-context BERT Tuning
Our approach, in-context BERT fine-tuning, produces a single shared scoring model for all items with a carefully-designed input structure to provide contextual information on each item.
Process-BERT: A Framework for Representation Learning on Educational Process Data
One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention.
GPT-based Open-Ended Knowledge Tracing
In education applications, knowledge tracing refers to the problem of estimating students' time-varying concept/skill mastery level from their past responses to questions and predicting their future performance.
DiPS: Differentiable Policy for Sketching in Recommender Systems
In sequential recommender system applications, it is important to develop models that can capture users' evolving interest over time to successfully recommend future items that they are likely to interact with.
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker.
Math Operation Embeddings for Open-ended Solution Analysis and Feedback
Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education.
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
Consequently, several recently proposed methods, such as Meta-Weight-Net (MW-Net), use a small number of unbiased, clean samples to learn a weighting function that downweights samples that are likely to have corrupted labels under the meta-learning framework.
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions.
Contrastive Learning Improves Model Robustness Under Label Noise
One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise, or re-weight training samples and down-weight those with higher loss values.
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Learning Student Interest Trajectory for MOOC Thread Recommendation
The projection operation learns to estimate future embedding of students and threads.
Personalized Education in the AI Era: What to Expect Next?
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to ultimately meet her desired goal.
Learning Student Interest Trajectory for MOOCThread Recommendation
The projection operation learns to estimate future embedding of students and threads.
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics
We propose VarFA, a variational inference factor analysis framework that extends existing factor analysis models for educational data mining to efficiently output uncertainty estimation in the model's estimated factors.
