Ensemble Knowledge Tracing: Modeling interactions in learning process

Sun, Jianwen; Zou, Rui; Liang, Ruxia; Gao, Lu; Liu, Sanya; Li, Qing; Zhang, Kai; Jiang, Lulu

doi:10.1016/j.eswa.2022.117680

Cited by 17 publications

(3 citation statements)

References 25 publications

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“…e feature elements are sorted from largest to smallest according to the attribute importance, and the search starts from the full set of all features, and the features with the lowest importance are removed or not according to the accuracy of the results; all the features are traversed and the optimal feature subset is output [13].…”

Section: Multifeature Selection Algorithmmentioning

confidence: 99%

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Chuan

2022

Mathematical Problems in Engineering

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As the mainstream of Confucianism, the Song and Ming philosophies played an irreplaceable role in the 800 years of history from the Song dynasty to the end of the Qing dynasty. The theoreticians were concerned with the principles of heaven, nature, and physics and especially with the principles of human nature, which changed in the Qing Dynasty to a practice-oriented way of thinking. However, the practical thinking of Qing dynasty Neo-Confucianism is difficult to apply to practice because of the obscure text and the limitations of the time period, so it is worth further discussing how to deconstruct and promote the learning of Qing dynasty practical thinking under the new technology. This paper systematically reviews the ideological origins and political practices of Qing dynasty Neo-Confucianism and proposes a knowledge-tracking model around deep learning technology, which not only provides a contemporary technological tool for deconstructing the practical thinking of Qing dynasty Neo-Confucianism but also provides an application path for the integration of technology and knowledge.

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Section: Multifeature Selection Algorithmmentioning

confidence: 99%

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Chuan

2022

Mathematical Problems in Engineering

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“…The time complexity of the ASC is O(3 D L). The time complexity of the Adaboost is O(ΦK f ) [43], where f is the complexity of a base learner. In this work, the used base learner is the HMM, and the corresponding time complexity is O(LZ 2 ) [44].…”

Section: Constructing Ensemble-sequencehmm Using Adaboostmentioning

confidence: 99%

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

Kong,

Liu,

Chen

et al. 2024

Mathematics

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Motion sequence data comprise a chronologically organized recording of a series of movements or actions carried out by a human being. Motion patterns found in such data holds significance for research and applications across multiple fields. In recent years, various feature representation techniques have been proposed to carry out sequence analysis. However, many of these methods have not fully uncovered the correlations between elements in sequences nor the internal interrelated structures among different dimensions, which are crucial to the recognition of motion patterns. This study proposes a novel Adaptive Sequence Coding (ASC) feature representation with ensemble hidden Markov models for motion sequence analysis. The ASC adopts the dual symbolization integrating first-order differential symbolization and event sequence encoding to effectively represent individual motion sequences. Subsequently, an adaptive boost algorithm based on a hidden Markov model is presented to distinguish the coded sequence data into different motion patterns. The experimental results on several publicly available datasets demonstrate that the proposed methodology outperforms other competing techniques. Meanwhile, ablation studies conducted on ASC and the adaptive boost approach further verify their significant potential in motion sequence analysis.

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“…Students' proficiency in answering questions accurately is contingent upon both their level of knowledge and their problem-solving abilities. The primary determinants of students' problem-solving abilities are the questions' inherent features, such as their complexity and differentiation [33]. In this paper, we fit the problem features 𝑑 𝑡 by passing the embedding vector 𝑘 𝑡 of 𝑞 𝑡 to a multilayer perceptual machine (MLP).…”

Section: Problem-solving Abilitymentioning

confidence: 99%

DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

Zhang,

Gong,

Luo

et al. 2024

IEEE Access

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Knowledge tracing aims to predict students' future question-answering performance based on their historical question-answering records, but the current mainstream knowledge tracing model ignores the individual differences in different students' knowledge-absorption and problem-solving abilities, which leads to a poor prediction of students' question-answering performance by the model. To solve this, Dynamic Key-Value Memory Networks Knowledge Tracing with Students' Knowledge-Absorption Ability and Problem-Solving Ability (DKVMN-KAPS) is proposed in this paper. Firstly, a hierarchical convolutional neural network is used to consider students' knowledge mastery at multiple time steps, and then quantify students' knowledge-absorption ability, aiming to more accurately portray students' knowledge states; secondly, an autoencoder is used to dynamically update students' problem-solving ability at each time step; and finally, students' question answering performance is predicted by considering the students' knowledge state, problemsolving ability, and question features. Extensive experiments on three datasets show that the prediction performance of DKVMN-KAPS outperforms existing models and improves the prediction accuracy of deep knowledge tracing models.INDEX TERMS knowledge tracing, knowledge-absorption ability, problem-solving ability, individual differences.

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Ensemble Knowledge Tracing: Modeling interactions in learning process

Cited by 17 publications

References 25 publications

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Practical Thinking of Neo-Confucianism in Qing Dynasty from the Scope of Practice Based on Deep Learning

Motion Sequence Analysis Using Adaptive Coding with Ensemble Hidden Markov Models

DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

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