A Survey of Knowledge Tracing

Liu, Qi; Shen, Shuanghong; Huang, Zhenya; Chen, Enhong; Zheng, Yonghe

doi:10.48550/arxiv.2105.15106

Cited by 12 publications

(31 citation statements)

References 89 publications

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“…State-of-the-art approaches include deep neural networks [42,17,35], and nonparametric Bayesian methods [21]. We find that the literature is sparse for inclusive LMs applied to ND populations, and we were unable to find many bespoke models or datasets (real or synthetic) even in recent literature reviews [1,28]. Kohli et.…”

Section: Introductionmentioning

confidence: 91%

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

Twomey¹,

McMullan²,

Elhalal³

et al. 2022

Preprint

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At present, the educational data mining community lacks many tools needed for ensuring equitable ability estimation for Neurodivergent (ND) learners. On one hand, most learner models are susceptible to under-estimating ND ability since confounding contexts cannot be held accountable (e.g. consider dyslexia and text-heavy assessments), and on the other, few (if any) existing datasets are suited for appraising model and data bias in ND contexts. In this paper we attempt to model the relationships between context (delivery and response types) and performance of ND students with zero-inflated learner models. This approach facilitates simulation of several expected ND behavioural traits, provides equitable ability estimates across all student groups from generated datasets, increases interpretability confidence, and can double the number of learning opportunities for ND students in some cases. Our approach consistently out-performs baselines in our experiments and can also be applied to many other learner modelling frameworks.

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Section: Introductionmentioning

confidence: 91%

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

Twomey¹,

McMullan²,

Elhalal³

et al. 2022

Preprint

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“…Discussion. Through encoding exercises-related or students-related information into the model, FMs allow us to make use of the side information available at hand [2]. However, there still exists plentiful side information under explored, e.g., relationships about S-S. Additionally, factorization machine-based KT models mapped students' knowledge level to an implicit space, and thus it is difficult to give a reasonable explanation for the prediction results.…”

Section: Factorization Machine-based Kt Modelsmentioning

confidence: 99%

“…Due to space constraints, it is considerably hard for us to give a comprehensive and detailed introduction to all the related works and characterize the benefits and drawbacks for all related works one by one. Instead, we refer readers to several survey or review articles [2], [19], [20] about knowledge tracing.…”

Section: Deep Neural Network-based Kt Modelsmentioning

confidence: 99%

“…Recent decades have witnessed the rapid growth of the computer-aided education systems, e.g., intelligent tutoring systems (ITS), which aim at automatically and significantly boosting learning gains [1]. One of the key tasks in these systems is knowledge tracing, which can model and track students' evolving knowledge state according to their historical learning interactions [2]. To be specific, the task of knowledge tracing is to predict whether a student can answer the next exercise correctly based upon all previous answer records.…”

Section: Introductionmentioning

confidence: 99%

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SPAKT: A Self-Supervised Pre-TrAining Method for Knowledge Tracing

Han

Qiao

et al. 2022

IEEE Access

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Knowledge tracing (KT) is the core task of computer-aided education systems, and it aims at predicting whether a student can answer the next exercise (i.e., question) correctly based on his/her historical answer records. In recent years, deep neural network-based approaches have been widely developed in KT and achieved promising results. More recently, several researches further boost these KT models via exploiting plentiful relationships including exercise-skill relations (E-S), the exercise similarity (E-E) as well as skill similarity (S-S). However, these relationship information are frequently absent in many real-world educational applications, and it is a labor-intensive work for human experts to label it. Inspired by recent advances in natural language processing domain, we propose a novel pre-training approach, namely as SPAKT, and utilize self-supervised learning to pre-train exercise embedding representation without the need for expensive human-expert annotations in this paper. Contrary to existing pre-training methods that highly rely on manually labeling knowledge about the E-E, S-S, or E-S relationships, the core idea of the proposed SPAKT is to design three self-attention modules to model the E-S, E-E, and S-S relationships, respectively, and all of these three modules can be trained in the self-supervised setting. As a pre-training approach, our SPAKT can be effortlessly incorporated into existing deep neural network-based KT frameworks. We experimentally show that, even without using expensive annotations about the aforementioned three kinds of relationships, our model achieves competitive performance compared with state-of-the-arts. Our algorithm implementations have been made publicly available at https://github.com/Vinci-hp/pretrainKT.

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“…In general, according to [2], knowledge tracing approaches can be classified into three main categories:…”

mentioning

confidence: 99%

KT-Bi-GRU: Student Performance Prediction with a Recurrent Knowledge Tracing Neural Network

Delianidi¹,

Diamantaras²

2022

Preprint

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<p>The work in this paper is an extended research of our previous work: M. Delianidi, K. Diamantaras, G. Chrysogonidis, and V. Nikiforidis,“Student performance prediction using dynamic neural models,” in Fourteenth International Conference on Educational Data Mining (EDM 2021), 2021, pp. 46–54. In both works we study the task of predicting a student's performance in a series of questions based, at each step, on the answers he/she has given on the previous questions. We propose a recurrent neural network approach where the dynamic part of the model is a Bidirectional GRU layer. In this work, we differentiate the model architecture from the earlier paper by imposing that the dynamic part is based exclusively on the history of previous question/answers, not including the current question. Then, the subsequent classification part is fed by the output of the dynamic part and the current question. In this way, the first part estimates the student's knowledge state and represents it with a dynamically generated vector considering only the student's previous questions and responses. We call this part the “Knowledge State Representation subnet''. Using this representation, the following “Tracing subnet'' which is a static multi-layer classifier can predict the correctness of the answer to any following question. Therefore, this architecture is suitable not only for described prediction task but also for recommendation tasks which were not possible with the previous architecture. In addition, in this work, we also study the effectiveness of our models against each other, as well as against the previous state-of-the-art models on seven datasets including a new multi-skills dataset derived from the NeurIPS 2020 education challenge. Both our models compare favorably against the state-of-the-art methods in almost all datasets, while the newly proposed architecture achieves very similar results compared to our earlier model in most cases except for two cases, including the multi-skill “NeurIPS-2020-small'' dataset, where it achieves considerably better results.</p>

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A Survey of Knowledge Tracing

Cited by 12 publications

References 89 publications

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

Equitable Ability Estimation in Neurodivergent Student Populations with Zero-Inflated Learner Models

SPAKT: A Self-Supervised Pre-TrAining Method for Knowledge Tracing

KT-Bi-GRU: Student Performance Prediction with a Recurrent Knowledge Tracing Neural Network

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