Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination

Yu Xiaopeng; Zhang Hanghang; Wu Yuntao

doi:10.1109/rcae53607.2021.9638936

Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination

Yu Xiaopeng, Zhang Hanghang, Wu Yuntao

Wuhan Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The traditional knowledge tracking model can only predict a single knowledge point, which is not enough to reflect the students' mastery of multiple knowledge: predicting multiple knowledge points and their combined knowledge involves a lot of calculations, which leads to low efficiency. This paper proposes a parallel knowledge tracking algorithm oriented to the combination of knowledge points. The algorithm proposes parallel HMM to estimate the mastery of multiple individual knowledge points, and predicts students' mastery of the combined knowledge points through least squares fitting and gradient descent fitting methods. Experiments show that this method can significantly improve the calculation efficiency and the accuracy of the combined knowledge point prediction.

Original language	English
Title of host publication	2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	181-185
Number of pages	5
ISBN (Electronic)	9781665427302
DOIs	https://doi.org/10.1109/rcae53607.2021.9638936
State	Published - 2021
Externally published	Yes
Event	4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021 - Wuhan, China Duration: 4 Nov 2021 → 6 Nov 2021

Publication series

Name	2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021

Conference

Conference	4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021
Country/Territory	China
City	Wuhan
Period	4/11/21 → 6/11/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Funding

Fund Project: The Humanities and Social Science Research Planning Fund Project of the Ministry of Education in 2019 "Research on the Representation Tutoring System of Mathematical Application Problems Based on Problem Situation Simulation" (No. 19YJA880077).

Funders	Funder number
Humanities and Social Science Research Planning Fund
Ministry of Education of the People's Republic of China	19YJA880077

Keywords

GD-HMM
OLS-HMM
intelligent education
knowledge combination
knowledge tracking model
parallel computing

Access to Document

10.1109/rcae53607.2021.9638936

Cite this

Xiaopeng, Y., Hanghang, Z., & Yuntao, W. (2021). Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination. In 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021 (pp. 181-185). (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/rcae53607.2021.9638936

Xiaopeng, Yu ; Hanghang, Zhang ; Yuntao, Wu. / Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination. 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 181-185 (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021).

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title = "Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination",

abstract = "The traditional knowledge tracking model can only predict a single knowledge point, which is not enough to reflect the students' mastery of multiple knowledge: predicting multiple knowledge points and their combined knowledge involves a lot of calculations, which leads to low efficiency. This paper proposes a parallel knowledge tracking algorithm oriented to the combination of knowledge points. The algorithm proposes parallel HMM to estimate the mastery of multiple individual knowledge points, and predicts students' mastery of the combined knowledge points through least squares fitting and gradient descent fitting methods. Experiments show that this method can significantly improve the calculation efficiency and the accuracy of the combined knowledge point prediction.",

keywords = "GD-HMM, OLS-HMM, intelligent education, knowledge combination, knowledge tracking model, parallel computing",

author = "Yu Xiaopeng and Zhang Hanghang and Wu Yuntao",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021 ; Conference date: 04-11-2021 Through 06-11-2021",

year = "2021",

doi = "10.1109/rcae53607.2021.9638936",

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series = "2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "181--185",

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Xiaopeng, Y, Hanghang, Z & Yuntao, W 2021, Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination. in 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021, Institute of Electrical and Electronics Engineers Inc., pp. 181-185, 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021, Wuhan, China, 4/11/21. https://doi.org/10.1109/rcae53607.2021.9638936

Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination. / Xiaopeng, Yu; Hanghang, Zhang; Yuntao, Wu.
2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 181-185 (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The traditional knowledge tracking model can only predict a single knowledge point, which is not enough to reflect the students' mastery of multiple knowledge: predicting multiple knowledge points and their combined knowledge involves a lot of calculations, which leads to low efficiency. This paper proposes a parallel knowledge tracking algorithm oriented to the combination of knowledge points. The algorithm proposes parallel HMM to estimate the mastery of multiple individual knowledge points, and predicts students' mastery of the combined knowledge points through least squares fitting and gradient descent fitting methods. Experiments show that this method can significantly improve the calculation efficiency and the accuracy of the combined knowledge point prediction.

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Xiaopeng Y, Hanghang Z, Yuntao W. Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination. In 2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 181-185. (2021 4th International Conference on Robotics, Control and Automation Engineering, RCAE 2021). doi: 10.1109/rcae53607.2021.9638936

Research on Multi Core Parallel Knowledge Tracking Algorithm for Knowledge Combination

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

Access to Document

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Cite this