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
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.
Knowledge Tracing Challenge: Optimal Activity Sequencing for Students
Knowledge tracing is a method used in education to assess and track the acquisition of knowledge by individual learners.
A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining
With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data.
Deep Knowledge Tracing is an implicit dynamic multidimensional item response theory model
Knowledge tracing consists in predicting the performance of some students on new questions given their performance on previous questions, and can be a prior step to optimizing assessment and learning.
Leveraging Skill-to-Skill Supervision for Knowledge Tracing
To do so, knowledge tracing systems should trace the knowledge state of the students by utilizing their problem-solving history and knowledge about the problems.
Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation
Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm.
Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generation
In this paper, we present a novel approach for distilling math word problem solving capabilities from large language models (LLMs) into smaller, more efficient student models.
Adaptive Learning Path Navigation Based on Knowledge Tracing and Reinforcement Learning
This paper introduces the Adaptive Learning Path Navigation (ALPN) system, a novel approach for enhancing E-learning platforms by providing highly adaptive learning paths for students.
Multi-granulariy Time-based Transformer for Knowledge Tracing
In this paper, we present a transformer architecture for predicting student performance on standardized tests.
Quiz-based Knowledge Tracing
In this paper, we present the Quiz-based Knowledge Tracing (QKT) model to monitor students' knowledge states according to their quiz-based learning interactions.
