Knowledge Tracing
90 papers with code • 2 benchmarks • 2 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
Most implemented papers
A Self-Attentive model for Knowledge Tracing
theophilee/learner-performance-prediction
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Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities.
Deep Knowledge Tracing
Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education.
Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing
To the best of our knowledge, this is the first work to suggest an encoder-decoder model for knowledge tracing that applies deep self-attentive layers to exercises and responses separately.
Last Query Transformer RNN for knowledge tracing
arshadshk/Last_Query_Transformer_RNN-PyTorch
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The novel point of the model is that it only uses the last input as query in transformer encoder, instead of all sequence, which makes QK matrix multiplication in transformer Encoder to have O(L) time complexity, instead of O(L^2).
DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills
In this article, we first frame the research problem of optimizing an adaptive and personalized spaced repetition scheduler when memorization concerns the application of underlying multiple skills.
SAINT+: Integrating Temporal Features for EdNet Correctness Prediction
We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information.
GIKT: A Graph-based Interaction Model for Knowledge Tracing
Rimoku/GIKT
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With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.
Application of Deep Self-Attention in Knowledge Tracing
The development of intelligent tutoring system has greatly influenced the way students learn and practice, which increases their learning efficiency.
Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization
ckyeungac/deep-knowledge-tracing-plus
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In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods.
Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing
jilljenn/ktm
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Knowledge tracing is a sequence prediction problem where the goal is to predict the outcomes of students over questions as they are interacting with a learning platform.
