23 research outputs found
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. It involves using a variety of
techniques, such as quizzes, tests, and other forms of assessment, to determine
what a learner knows and does not know about a particular subject. The goal of
knowledge tracing is to identify gaps in understanding and provide targeted
instruction to help learners improve their understanding and retention of
material. This can be particularly useful in situations where learners are
working at their own pace, such as in online learning environments. By
providing regular feedback and adjusting instruction based on individual needs,
knowledge tracing can help learners make more efficient progress and achieve
better outcomes. Effectively solving the KT problem would unlock the potential
of computer-aided education applications such as intelligent tutoring systems,
curriculum learning, and learning materials recommendations. In this paper, we
will present the results of the implementation of two Knowledge Tracing
algorithms on a newly released dataset as part of the AAAI2023 Global Knowledge
Tracing Challenge
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. Deep knowledge tracing (DKT) is a
competitive model for knowledge tracing relying on recurrent neural networks,
even if some simpler models may match its performance. However, little is known
about why DKT works so well. In this paper, we frame deep knowledge tracing as
a encoderdecoder architecture. This viewpoint not only allows us to propose
better models in terms of performance, simplicity or expressivity but also
opens up promising avenues for future research directions. In particular, we
show on several small and large datasets that a simpler decoder, with possibly
fewer parameters than the one used by DKT, can predict student performance
better.Comment: ICCE 2023 - The 31st International Conference on Computers in
Education, Asia-Pacific Society for Computers in Education, Dec 2023, Matsue,
Shimane, Franc
3DG: A Framework for Using Generative AI for Handling Sparse Learner Performance Data From Intelligent Tutoring Systems
Learning performance data (e.g., quiz scores and attempts) is significant for
understanding learner engagement and knowledge mastery level. However, the
learning performance data collected from Intelligent Tutoring Systems (ITSs)
often suffers from sparsity, impacting the accuracy of learner modeling and
knowledge assessments. To address this, we introduce the 3DG framework
(3-Dimensional tensor for Densification and Generation), a novel approach
combining tensor factorization with advanced generative models, including
Generative Adversarial Network (GAN) and Generative Pre-trained Transformer
(GPT), for enhanced data imputation and augmentation. The framework operates by
first representing the data as a three-dimensional tensor, capturing dimensions
of learners, questions, and attempts. It then densifies the data through tensor
factorization and augments it using Generative AI models, tailored to
individual learning patterns identified via clustering. Applied to data from an
AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG
framework effectively generated scalable, personalized simulations of learning
performance. Comparative analysis revealed GAN's superior reliability over
GPT-4 in this context, underscoring its potential in addressing data sparsity
challenges in ITSs and contributing to the advancement of personalized
educational technology
Deep learning for knowledge tracing in learning analytics: An overview
Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting,
analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the
environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of
the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid
failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep
Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge
Tracing (DKT). This was made possible by the digitalization process that has simplified the gathering
of educational data from many different sources such as online learning platforms, intelligent objects,
and mainstream IT-based systems for education. DKT predicts the student’s performances by using
the information embedded in the collected data. Moreover, it has been shown to be able to outperform
the state-of-the-art models for KT. In this paper, we briefly describe the most promising DL models, by
focusing on their prominent contribution in solving the KT task
Deep learning for knowledge tracing in learning analytics: An overview
Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge Tracing (DKT). This was made possible by the digitalization process that has simplified the gathering of educational data from many different sources such as online learning platforms, intelligent objects, and mainstream IT-based systems for education. DKT predicts the student’s performances by using the information embedded in the collected data. Moreover, it has been shown to be able to outperform the state-of-the-art models for KT. In this paper, we briefly describe the most promising DL models, by focusing on their prominent contribution in solving the KT task
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. Despite the acknowledged significance of difficulty,
previous KT research has yet to exploit its potential for model optimization
and has struggled to predict difficulty from unseen data. To address these
problems, we propose a difficulty-centered contrastive learning method for KT
models and a Large Language Model (LLM)-based framework for difficulty
prediction. These innovative methods seek to improve the performance of KT
models and provide accurate difficulty estimates for unseen data. Our ablation
study demonstrates the efficacy of these techniques by demonstrating enhanced
KT model performance. Nonetheless, the complex relationship between language
and difficulty merits further investigation.Comment: 10 pages, 4 figures, 2 table