335 citations · 535 across the 88 of their papers we have counts for
17 papers · 1 filter
CATTO: Balancing Preferences and Confidence in Language Models
Nisarg Parikh, Ananya Sai, Pannaga Shivaswamy +2
Large language models (LLMs) often make accurate next token predictions but their confidence in these predictions can be poorly calibrated: high-confidence predictions are frequent…
KASER: Knowledge-Aligned Student Error Simulator for Open-Ended Coding Tasks
Zhangqi Duan, Nigel Fernandez, Andrew Lan
Open-ended tasks, such as coding problems that are common in computer science education, provide detailed insights into student knowledge. However, training large language models (…
Pattern-based Knowledge Component Extraction from Student Code Using Representation Learning
Muntasir Hoq, Griffin Pitts, Tirth Bhatt +4
Personalized instruction aims to provide learners with support that adapts to their individual knowledge and progress toward learning objectives. Discovering and tracing Knowledge…
LookAlike: Consistent Distractor Generation in Math MCQs
Nisarg Parikh, Nigel Fernandez, Alexander Scarlatos +2
Large language models (LLMs) are increasingly used to generate distractors for multiple-choice questions (MCQs), especially in domains like math education. However, existing approa…
A Conceptual Model for End-to-End Causal Discovery in Knowledge Tracing
Nischal Ashok Kumar, Wanyong Feng, Jaewook Lee +3
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 ski…
Multi-Layer Personalized Federated Learning for Mitigating Biases in Student Predictive Analytics
Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio +4
Conventional methods for student modeling, which involve predicting grades based on measured activities, struggle to provide accurate results for minority/underrepresented student…
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning
Yun-Wei Chu, Seyyedali Hosseinalipour, Elizabeth Tenorio +4
Traditional learning-based approaches to student modeling generalize poorly to underrepresented student groups due to biases in data availability. In this paper, we propose a metho…
Automated Scoring for Reading Comprehension via In-context BERT Tuning
Nigel Fernandez, Aritra Ghosh, Naiming Liu +4
Automated scoring of open-ended student responses has the potential to significantly reduce human grader effort. Recent advances in automated scoring often leverage textual represe…
Process-BERT: A Framework for Representation Learning on Educational Process Data
Alexander Scarlatos, Christopher Brinton, Andrew Lan
Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn.…
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach
Yun-Wei Chu, Elizabeth Tenorio, Laura Cruz +3
We study the problem of predicting student knowledge acquisition in online courses from clickstream behavior. Motivated by the proliferation of eLearning lecture delivery, we speci…
BOBCAT: Bilevel Optimization-Based Computerized Adaptive Testing
Aritra Ghosh, Andrew Lan
Computerized adaptive testing (CAT) refers to a form of tests that are personalized to every student/test taker. CAT methods adaptively select the next most informative question/it…
Math Operation Embeddings for Open-ended Solution Analysis and Feedback
Mengxue Zhang, Zichao Wang, Richard Baraniuk +1
Feedback on student answers and even during intermediate steps in their solutions to open-ended questions is an important element in math education. Such feedback can help students…
Do We Really Need Gold Samples for Sample Weighting Under Label Noise?
Aritra Ghosh, Andrew Lan
Learning with labels noise has gained significant traction recently due to the sensitivity of deep neural networks under label noise under common loss functions. Losses that are th…
Option Tracing: Beyond Correctness Analysis in Knowledge Tracing
Aritra Ghosh, Jay Raspat, Andrew Lan
Knowledge tracing refers to a family of methods that estimate each student's knowledge component/skill mastery level from their past responses to questions. One key limitation of m…
Context-Aware Attentive Knowledge Tracing
Aritra Ghosh, Neil Heffernan, Andrew S. Lan
Knowledge tracing (KT) refers to the problem of predicting future learner performance given their past performance in educational applications. Recent developments in KT using flex…
qDKT: Question-centric Deep Knowledge Tracing
Shashank Sonkar, Andrew E. Waters, Andrew S. Lan +2
Knowledge tracing (KT) models, e.g., the deep knowledge tracing (DKT) model, track an individual learner's acquisition of skills over time by examining the learner's performance on…
MSE-Optimal Neural Network Initialization via Layer Fusion
Ramina Ghods, Andrew S. Lan, Tom Goldstein +1
Deep neural networks achieve state-of-the-art performance for a range of classification and inference tasks. However, the use of stochastic gradient descent combined with the nonco…