Convolutional Recurrent Neural Networks for Knowledge Tracing

Wang, Wei; Liu, Tieyuan; Chang, Liang; Gu, Tianlong; Zhao, Xuemei

doi:10.1109/cyberc49757.2020.00054

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…DKVMN [6] and Deep-IRT [7] on the seven described datasets. Note that the python code 11 used for the DKT model experiments requires that the train/test split is performed during code execution, thus the data files have been converted to the appropriate format required for the experimentation process.…”

Section: Methodsmentioning

confidence: 99%

“…Recently, methods from the first two categories were combined with deep learning methods, for example DBN [8] and DPFA [9], achieving improved results. Additionally, convolutional neural networks [10], [11], [12], [13] and graph neural networks models [14], [15] have been recently proposed for the KT task. The aim of all the works is the optimal representation of the knowledge state and the prediction of student performance so that the learning process can be improved and adapted to the student's learning needs.…”

mentioning

confidence: 99%

“…Other KT approaches combine earlier proposed mod- Recent research has presented knowledge tracing models using convolutional neural networks (CNN) [10], [11], [12], [13] and graph neural networks (GNN) [14], [15]. Other works [21], [22] employ CNN and GNN models incorporating recurrent neural layers, such as LSTM, in order to achieve better performance.…”

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confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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

Delianidi¹,

Diamantaras²

2022

Preprint

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“…In DKT, student responses serve as inputs, latent knowledge states are hidden layers, and the predicted probabilities of correctness for questions are outputs [41]. There are many variations of DKT as reported in [42], some changed the network structure [43], [44], while others include different faceted student information [45]- [50]. In general, DKT substantially saves the efforts of human experts, as it does not need to explicitly construct a prior domain knowledge model.…”

Section: B Data-driven Approachesmentioning

confidence: 99%

Student Modeling and Analysis in Adaptive Instructional Systems

et al. 2022

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There is a growing interest in developing and implementing adaptive instructional systems to improve, automate, and personalize student education. A necessary part of any such adaptive instructional system is a student model used to predict or analyze learner behavior and inform adaptation. To help inform researchers in this area, this paper presents a state-of-theart review of 11 years of research (2010)(2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020)(2021) in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models. We mainly emphasize increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems. In addition, we discuss challenges inherent in real-world multimodal modeling, such as uncontrolled data collection environments leading to noisy data and data sync issues. Finally, we reinforce our findings and conclusions through an industry case study of an adaptive instructional system. In our study, we verify that adding multiple data modalities increases our model prediction accuracy from 53.3% to 69%. At the same time, the challenges encountered with our real-world case study, including uncontrolled data collection environment with inevitably noisy data, calls for synchronization and noise control strategies for data quality and usability.

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“…As deep learning develops, a lot of deep learning models have been applied in KT. Chris Piech applies the recurrent neural network (RNN) to model the student learning process for the rst time and proposes deep knowledge tracing (DKT) [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

Qiang

Sun³

et al. 2022

Computational Intelligence and Neuroscience

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Knowledge tracing (KT) is the task of modelling students’ knowledge state based on their historical interactions on intelligent tutoring systems. Existing KT models ignore the relevance among the multiple knowledge concepts of a question and characteristics of online tutoring systems. This paper proposes a neural Turing machine-based skill-aware knowledge tracing (NSKT) for conjunctive skills, which can capture the relevance among the knowledge concepts of a question to model students’ knowledge state more accurately and to discover more latent relevance among knowledge concepts effectively. We analyze the characteristics of the three real-world KT datasets in depth. Experiments on real-world datasets show that NSKT outperforms the state-of-the-art deep KT models on the AUC of prediction. This paper explores details of the prediction process of NSKT in modelling students’ knowledge state, as well as the relevance of knowledge concepts and conditional influences between exercises.

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Convolutional Recurrent Neural Networks for Knowledge Tracing

Cited by 6 publications

References 10 publications

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

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

Student Modeling and Analysis in Adaptive Instructional Systems

NTM-Based Skill-Aware Knowledge Tracing for Conjunctive Skills

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