Knowledge Tracing
69 papers with code • 2 benchmarks • 1 datasets
Knowledge Tracing is the task of modelling student knowledge over time so that we can accurately predict how students will perform on future interactions. Improvement on this task means that resources can be suggested to students based on their individual needs, and content which is predicted to be too easy or too hard can be skipped or delayed.
Source: Deep Knowledge Tracing
Libraries
Use these libraries to find Knowledge Tracing models and implementations
EdNet
Latest papers with no code
A Question-centric Multi-experts Contrastive Learning Framework for Improving the Accuracy and Interpretability of Deep Sequential Knowledge Tracing Models
However, the inherent black-box nature of deep learning techniques often poses a hurdle for teachers to fully embrace the model's prediction results.
A Survey of Explainable Knowledge Tracing
This paper thoroughly analyzes the interpretability of KT algorithms.
Improving Low-Resource Knowledge Tracing Tasks by Supervised Pre-training and Importance Mechanism Fine-tuning
Knowledge tracing (KT) aims to estimate student's knowledge mastery based on their historical interactions.
Predicting Learning Performance with Large Language Models: A Study in Adult Literacy
This research is motivated by the potential of LLMs to predict learning performance based on its inherent reasoning and computational capabilities.
A Review of Data Mining in Personalized Education: Current Trends and Future Prospects
Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness.
KARL: Knowledge-Aware Retrieval and Representations aid Retention and Learning in Students
Flashcard schedulers are tools that rely on 1) student models to predict the flashcards a student knows; and 2) teaching policies to schedule cards based on these predictions.
Analysis of Knowledge Tracing performance on synthesised student data
Knowledge Tracing (KT) aims to predict the future performance of students by tracking the development of their knowledge states.
Parametric Constraints for Bayesian Knowledge Tracing from First Principles
Bayesian Knowledge Tracing (BKT) is a probabilistic model of a learner's state of mastery corresponding to a knowledge component.
Difficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction
This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level.
On the verification of Embeddings using Hybrid Markov Logic
The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks.
