Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis

This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment bas...

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Autores principales:	Hua Yin, Hong Wu, Sang-Bing Tsai
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/6bdeb845b9cb43f6b1575615dca54cea
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spelling	oai:doaj.org-article:6bdeb845b9cb43f6b1575615dca54cea2021-11-08T02:37:28ZInnovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis1563-514710.1155/2021/1460172https://doaj.org/article/6bdeb845b9cb43f6b1575615dca54cea2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1460172https://doaj.org/toc/1563-5147This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners’ dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance.Hua YinHong WuSang-Bing TsaiHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Hua Yin Hong Wu Sang-Bing Tsai Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis
description	This article first addresses the problem that the unstructured data in the existing e-learning education data is difficult to effectively use and the problem that the coarser granularity of sentiment analysis results in traditional sentiment analysis methods and proposes multipolarized sentiment based on fine-grained sentiment analysis evaluation model. Then, an algorithm for behavior prediction and course recommendation based on emotional change trends is proposed, and the established multiple linear regression equation is solved with an improved algorithm. Finally, the method in this paper is verified by a comprehensive example with algorithm comparison analysis and cross-validation evaluation method. The research method proposed in this article provides new research ideas for evaluating and predicting the learning behavior of e-learners, which is conducive to timely discovering learners’ dropout tendency and recommending relevant courses of interest to improve their graduation rate, so as to optimize the learning experience of learners, promote the development of personalized education and effective teaching of the e-learning teaching platform, and provide a certain reference value for accelerating the reform process of education informatization. In order to improve the speed of searching for parameters and the best parameters, this paper proposes a particle swarm algorithm (to improve the support vector machine parameters in a sense) and finds the best parameters which also achieved the goal from academic expression to academic performance.
format	article
author	Hua Yin Hong Wu Sang-Bing Tsai
author_facet	Hua Yin Hong Wu Sang-Bing Tsai
author_sort	Hua Yin
title	Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis
title_short	Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis
title_full	Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis
title_fullStr	Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis
title_full_unstemmed	Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis
title_sort	innovative research on the construction of learner’s emotional cognitive model in e-learning by big data analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/6bdeb845b9cb43f6b1575615dca54cea
work_keys_str_mv	AT huayin innovativeresearchontheconstructionoflearnersemotionalcognitivemodelinelearningbybigdataanalysis AT hongwu innovativeresearchontheconstructionoflearnersemotionalcognitivemodelinelearningbybigdataanalysis AT sangbingtsai innovativeresearchontheconstructionoflearnersemotionalcognitivemodelinelearningbybigdataanalysis
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Innovative Research on the Construction of Learner’s Emotional Cognitive Model in E-Learning by Big Data Analysis

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