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Found 19 papers, 4 papers with code
Balancing Test Accuracy and Security in Computerized Adaptive Testing
Computerized adaptive testing (CAT) is a form of personalized testing that accurately measures students' knowledge levels while reducing test length.
Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach
Our methodology for predicting in-video quiz performance is based on three key ideas we develop.
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints
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 scenario.
Option Tracing: Beyond Binary Knowledge Tracing
This paper details our solutions to Tasks 1&2 of the NeurIPS 2020 Education Challenge. 1 Knowledge tracing, a family of methods to estimate each student’s mastery levels on skills/knowledge components from their past responses to assessment questions, is useful for progress monitoring, personalization, and helping teachers to deliver personalized and targeted feedback to students to improve their learning outcomes.
Context-Aware Attentive Knowledge Tracing
We also conduct several case studies and show that AKT exhibits excellent interpretability and thus has potential for automated feedback and personalization in real-world educational settings.
qDKT: Question-centric Deep Knowledge Tracing
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 questions related to those skills.
Robust and Interpretable Grounding of Spatial References with Relation Networks
Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation.
MSE-Optimal Neural Network Initialization via Layer Fusion
To address this issue, a variety of methods that rely on random parameter initialization or knowledge distillation have been proposed in the past.
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.
An Estimation and Analysis Framework for the Rasch Model
The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance.
Linear Spectral Estimators and an Application to Phase Retrieval
Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements.
Linearized Binary Regression
We showcase the efficacy of our methods and results for a number of synthetic and real-world datasets, which demonstrates that linearized binary regression finds potential use in a variety of inference, estimation, signal processing, and machine learning applications that deal with binary-valued observations or measurements.
Data-Mining Textual Responses to Uncover Misconception Patterns
An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions.
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors.
Quantized Matrix Completion for Personalized Learning
The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e. g., correct/incorrect) graded learner responses to questions.
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner's knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts.
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications.
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
In order to better interpret the estimated latent concepts, SPARFA relies on a post-processing step that utilizes user-defined tags (e. g., topics or keywords) available for each question.
Sparse Factor Analysis for Learning and Content Analytics
We estimate these factors given the graded responses to a collection of questions.
