335 citations · 535 across the 88 of their papers we have counts for
10 papers · 1 filter
DiPS: Differentiable Policy for Sketching in Recommender Systems
Aritra Ghosh, Saayan Mitra, Andrew Lan
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 the…
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…
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
Zichao Wang, Andrew S. Lan, Richard G. Baraniuk
We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem s…
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…
Contrastive Learning Improves Model Robustness Under Label Noise
Aritra Ghosh, Andrew Lan
Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data. One common type of method that can mi…
Personalized Education in the AI Era: What to Expect Next?
Setareh Maghsudi, Andrew Lan, Jie Xu +1
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…
Learning Student Interest Trajectory for MOOCThread Recommendation
Shalini Pandey, Andrew Lan, George Karypis +1
In recent years, Massive Open Online Courses (MOOCs) have witnessed immense growth in popularity. Now, due to the recent Covid19 pandemic situation, it is important to push the lim…