Search Results for author:
Found 30 papers, 18 papers with code
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.
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.
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.
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
Traditional learning-based approaches to student modeling (e. g., predicting grades based on measured activities) generalize poorly to underrepresented/minority student groups due to biases in data availability.
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.
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.
Ranked #28 on
Image Classification
on Clothing1M
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.
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.
