Learning Analysis Based on Learners Learning Model

Wang Wangzhu; Liao Zhixin; Deng Yi; Xu Song; Guo Xiaoyu; Ye Junmin

doi:doi:10.11648/j.ijeedu.20180701.11

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Learning Analysis Based on Learners Learning Model

Wang Wangzhu, Liao Zhixin, Deng Yi, Xu Song, Guo Xiaoyu, Ye Junmin

Published in International Journal of Elementary Education (Volume 7, Issue 1)

Received: 23 February 2018 Published: 27 February 2018

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Abstract

In the era of big data and artificial intelligence, collecting and analyzing learners learning data can modle and predict learners learning trend and help learners avoid risks of academic failures. For those purposes, this paper presents a learning analysis method based on learners learning model. First, the teaching model of the curriculum is put forward to support the learning data analysis. Secondly, various methods including questionnaire are used in data collection and quantification of learners offline learning data so that all the meaningful data can be transformed into the numerical data that can be processed; thirdly, the linear fitting method is used to analyze the learning data and predict the learners learning trend. The results show that the linear fitting method can effectively describe learners learning trend.

Published in	International Journal of Elementary Education (Volume 7, Issue 1)
DOI	10.11648/j.ijeedu.20180701.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Learning Trends, Data Analysis, Linear Fitting

References

[1]	The New Media Consortium (NMC). 2011 -Horizon-Report-K12 [DB / OL].
[2]	The New Media Consortium (NMC). 2015 -Horizon-Report-K12 [DB / OL].
[3]	Verónica Rivera-Pelayo, Valentin Zacharias, Lars Müller, and Simone Braun. Applying Quantified Self Approaches to Support Reflective Learning [OL], LAK, 2012.
[4]	Annika Wolff, Zdenek Zdrahal, Andriy Nikolov, Michal Pantucek. Improving retention: predicting at-risk students by analyzing and behavior in a virtual learning environment [OL], LAK, 2013.
[5]	Richard Joseph Waddington, Sung Jin Nam. Practice Exams Make Perfect: Incorporating Course Resource Use into an Early Warning System [OL]. LAK, 2014.
[6]	Adams B S, Cummins, Davis, et al. NMC Horizon Report: 2017 Higher Education Edition [J]. Journal of Open Learning, 2017.
[7]	Hallinen N R, Schwartz D L. Modeling exploration strategies to predict student performance within a learning environment and beyond [C] International Learning Analytics & Knowledge Conference. ACM, 2017: 31-40.
[8]	Hlosta M, Zdrahal Z, Zendulka J. Ouroboros: early identification of at-risk students without models based on legacy data [C]// International Learning Analytics & Knowledge Conference. 2017: 6-15.
[9]	MO King Kee. How teachers carry out classroom instruction evaluation [J]. Curriculum, Textbook, Teaching Method, 2008 (11): 14-18.
[10]	LIU Gang, Tian Jing. Several problems in the reform of classroom teaching evaluation [J]. Shanxi Normal University Press: Social Science Edition, 2012 (1): 144-147.
[11]	FANG Haiguang, Wei Feng, Wang Xiaochun, Chu Yunhai. Data mining and analysis of digital classroom learning process based on PADClass model [J]. Environmental Construction and Resource Exploitation, 2014, (10): 110-120.
[12]	JIN Yifu, Wu Tao, Zhang Zishi and Wang Weidong, Design and Analysis of Learning Alert System in Big Data Condition China Educational Technology 2016.2: 69-73.
[13]	ZHAO Huiqiong, JIANG Qiang, ZHAO Wei, LI Yongfan and ZHAO Yan, Empirical Research of Predictive Factors and Intervention Countermeasures of Online Learning Performance on Big Data-based Learning Analytics. e- education research, 2017.1: 62-69.
[14]	HE Guang-dong. Application of ID3 Algorithm in Psychological Education for College Students. Success, 2012, (20): 268.
[15]	WANG Zheng, TAN Longjiang. Recommendation System Based on Customer Psychological Mining and Prediction [J]. Computer Engineering and Design, 2012, 31 (11): 4347-4350.
[16]	ZHAO Xiaoyan. Psychological data mining system for higher vocational students and application [D]. Chengdu: University of Electronic Science and Technology, 2009.

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APA Style

Wang Wangzhu, Liao Zhixin, Deng Yi, Xu Song, Guo Xiaoyu, et al. (2018). Learning Analysis Based on Learners Learning Model. International Journal of Elementary Education, 7(1), 1-6. https://doi.org/10.11648/j.ijeedu.20180701.11

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ACS Style

Wang Wangzhu; Liao Zhixin; Deng Yi; Xu Song; Guo Xiaoyu, et al. Learning Analysis Based on Learners Learning Model. Int. J. Elem. Educ. 2018, 7(1), 1-6. doi: 10.11648/j.ijeedu.20180701.11

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AMA Style

Wang Wangzhu, Liao Zhixin, Deng Yi, Xu Song, Guo Xiaoyu, et al. Learning Analysis Based on Learners Learning Model. Int J Elem Educ. 2018;7(1):1-6. doi: 10.11648/j.ijeedu.20180701.11

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@article{10.11648/j.ijeedu.20180701.11,
  author = {Wang Wangzhu and Liao Zhixin and Deng Yi and Xu Song and Guo Xiaoyu and Ye Junmin},
  title = {Learning Analysis Based on Learners Learning Model},
  journal = {International Journal of Elementary Education},
  volume = {7},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijeedu.20180701.11},
  url = {https://doi.org/10.11648/j.ijeedu.20180701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeedu.20180701.11},
  abstract = {In the era of big data and artificial intelligence, collecting and analyzing learners learning data can modle and predict learners learning trend and help learners avoid risks of academic failures. For those purposes, this paper presents a learning analysis method based on learners learning model. First, the teaching model of the curriculum is put forward to support the learning data analysis. Secondly, various methods including questionnaire are used in data collection and quantification of learners offline learning data so that all the meaningful data can be transformed into the numerical data that can be processed; thirdly, the linear fitting method is used to analyze the learning data and predict the learners learning trend. The results show that the linear fitting method can effectively describe learners learning trend.},
 year = {2018}
}

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TY  - JOUR
T1  - Learning Analysis Based on Learners Learning Model
AU  - Wang Wangzhu
AU  - Liao Zhixin
AU  - Deng Yi
AU  - Xu Song
AU  - Guo Xiaoyu
AU  - Ye Junmin
Y1  - 2018/02/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijeedu.20180701.11
DO  - 10.11648/j.ijeedu.20180701.11
T2  - International Journal of Elementary Education
JF  - International Journal of Elementary Education
JO  - International Journal of Elementary Education
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-7640
UR  - https://doi.org/10.11648/j.ijeedu.20180701.11
AB  - In the era of big data and artificial intelligence, collecting and analyzing learners learning data can modle and predict learners learning trend and help learners avoid risks of academic failures. For those purposes, this paper presents a learning analysis method based on learners learning model. First, the teaching model of the curriculum is put forward to support the learning data analysis. Secondly, various methods including questionnaire are used in data collection and quantification of learners offline learning data so that all the meaningful data can be transformed into the numerical data that can be processed; thirdly, the linear fitting method is used to analyze the learning data and predict the learners learning trend. The results show that the linear fitting method can effectively describe learners learning trend.
VL  - 7
IS  - 1
ER  -

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Author Information

Wang Wangzhu

School of Foreign Languages, South-Central University for Nationalities, Wuhan, China
Liao Zhixin

School of Computer, Central China Normal University, Wuhan, China
Deng Yi

School of Computer, Central China Normal University, Wuhan, China
Xu Song

School of Computer, Central China Normal University, Wuhan, China
Guo Xiaoyu

School of Computer, Central China Normal University, Wuhan, China
Ye Junmin

School of Computer, Central China Normal University, Wuhan, China

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Plain Text BibTeX RIS

APA Style

Wang Wangzhu, Liao Zhixin, Deng Yi, Xu Song, Guo Xiaoyu, et al. (2018). Learning Analysis Based on Learners Learning Model. International Journal of Elementary Education, 7(1), 1-6. https://doi.org/10.11648/j.ijeedu.20180701.11

Copy | Download

ACS Style

Wang Wangzhu; Liao Zhixin; Deng Yi; Xu Song; Guo Xiaoyu, et al. Learning Analysis Based on Learners Learning Model. Int. J. Elem. Educ. 2018, 7(1), 1-6. doi: 10.11648/j.ijeedu.20180701.11

Copy | Download

AMA Style

Wang Wangzhu, Liao Zhixin, Deng Yi, Xu Song, Guo Xiaoyu, et al. Learning Analysis Based on Learners Learning Model. Int J Elem Educ. 2018;7(1):1-6. doi: 10.11648/j.ijeedu.20180701.11

Copy | Download

@article{10.11648/j.ijeedu.20180701.11,
  author = {Wang Wangzhu and Liao Zhixin and Deng Yi and Xu Song and Guo Xiaoyu and Ye Junmin},
  title = {Learning Analysis Based on Learners Learning Model},
  journal = {International Journal of Elementary Education},
  volume = {7},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ijeedu.20180701.11},
  url = {https://doi.org/10.11648/j.ijeedu.20180701.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeedu.20180701.11},
  abstract = {In the era of big data and artificial intelligence, collecting and analyzing learners learning data can modle and predict learners learning trend and help learners avoid risks of academic failures. For those purposes, this paper presents a learning analysis method based on learners learning model. First, the teaching model of the curriculum is put forward to support the learning data analysis. Secondly, various methods including questionnaire are used in data collection and quantification of learners offline learning data so that all the meaningful data can be transformed into the numerical data that can be processed; thirdly, the linear fitting method is used to analyze the learning data and predict the learners learning trend. The results show that the linear fitting method can effectively describe learners learning trend.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Learning Analysis Based on Learners Learning Model
AU  - Wang Wangzhu
AU  - Liao Zhixin
AU  - Deng Yi
AU  - Xu Song
AU  - Guo Xiaoyu
AU  - Ye Junmin
Y1  - 2018/02/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ijeedu.20180701.11
DO  - 10.11648/j.ijeedu.20180701.11
T2  - International Journal of Elementary Education
JF  - International Journal of Elementary Education
JO  - International Journal of Elementary Education
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-7640
UR  - https://doi.org/10.11648/j.ijeedu.20180701.11
AB  - In the era of big data and artificial intelligence, collecting and analyzing learners learning data can modle and predict learners learning trend and help learners avoid risks of academic failures. For those purposes, this paper presents a learning analysis method based on learners learning model. First, the teaching model of the curriculum is put forward to support the learning data analysis. Secondly, various methods including questionnaire are used in data collection and quantification of learners offline learning data so that all the meaningful data can be transformed into the numerical data that can be processed; thirdly, the linear fitting method is used to analyze the learning data and predict the learners learning trend. The results show that the linear fitting method can effectively describe learners learning trend.
VL  - 7
IS  - 1
ER  -

Copy | Download