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Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model

The quality evaluation of innovation and entrepreneurship (I&E) in the education sector is achieving worldwide attention as empowering nations with high quality talents is quintessential for economic progress. China, a pioneer in the world market in almost all sectors have transformed its educat...

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Autores principales: Lu, Qianqian, Chai, Yongxiang, Ren, Lihui, Ren, Pengyu, Zhou, Junhui, Lin, Chunlei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280670/
https://www.ncbi.nlm.nih.gov/pubmed/37346726
http://dx.doi.org/10.7717/peerj-cs.1329
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author Lu, Qianqian
Chai, Yongxiang
Ren, Lihui
Ren, Pengyu
Zhou, Junhui
Lin, Chunlei
author_facet Lu, Qianqian
Chai, Yongxiang
Ren, Lihui
Ren, Pengyu
Zhou, Junhui
Lin, Chunlei
author_sort Lu, Qianqian
collection PubMed
description The quality evaluation of innovation and entrepreneurship (I&E) in the education sector is achieving worldwide attention as empowering nations with high quality talents is quintessential for economic progress. China, a pioneer in the world market in almost all sectors have transformed its educational policies and incorporated entrepreneurial skills as a part of their education models to further catalyst the country’s economic progress. This research focuses on building a novel hybrid Machine Learning (ML) model by integrating two powerful algorithms namely Random Forest (RF) and Logistic Regression (LR) to assess the intensity of the I&E in education from the data acquired from 25 leading Higher Educational Institution’s (HEI) in different provinces. The major contributions to the work are, (1) construction of quality index for each topic of interest using individual RF, (2) ranking the indicators based on the quality index to assess the strength and weaknesses, (3) and finally use the LR algorithm study the quality of each indicator. The efficacy of the proposed hybrid model is validated using the benchmark classification metrics to assess its learning and prediction performance in evaluating the quality of I&E education. The result of the research portrays that the universities have now started to integrate entrepreneurship skills as a part of the curriculum, which is evident from the better ranking of the topic curriculum development which is followed by the enrichment of skills. This comprehensive research will help the institutions to identify the potential areas of growth to boost the economic development and improve the skill set necessary for I&E education among college students.
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spelling pubmed-102806702023-06-21 Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model Lu, Qianqian Chai, Yongxiang Ren, Lihui Ren, Pengyu Zhou, Junhui Lin, Chunlei PeerJ Comput Sci Algorithms and Analysis of Algorithms The quality evaluation of innovation and entrepreneurship (I&E) in the education sector is achieving worldwide attention as empowering nations with high quality talents is quintessential for economic progress. China, a pioneer in the world market in almost all sectors have transformed its educational policies and incorporated entrepreneurial skills as a part of their education models to further catalyst the country’s economic progress. This research focuses on building a novel hybrid Machine Learning (ML) model by integrating two powerful algorithms namely Random Forest (RF) and Logistic Regression (LR) to assess the intensity of the I&E in education from the data acquired from 25 leading Higher Educational Institution’s (HEI) in different provinces. The major contributions to the work are, (1) construction of quality index for each topic of interest using individual RF, (2) ranking the indicators based on the quality index to assess the strength and weaknesses, (3) and finally use the LR algorithm study the quality of each indicator. The efficacy of the proposed hybrid model is validated using the benchmark classification metrics to assess its learning and prediction performance in evaluating the quality of I&E education. The result of the research portrays that the universities have now started to integrate entrepreneurship skills as a part of the curriculum, which is evident from the better ranking of the topic curriculum development which is followed by the enrichment of skills. This comprehensive research will help the institutions to identify the potential areas of growth to boost the economic development and improve the skill set necessary for I&E education among college students. PeerJ Inc. 2023-04-17 /pmc/articles/PMC10280670/ /pubmed/37346726 http://dx.doi.org/10.7717/peerj-cs.1329 Text en ©2023 Lu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Algorithms and Analysis of Algorithms
Lu, Qianqian
Chai, Yongxiang
Ren, Lihui
Ren, Pengyu
Zhou, Junhui
Lin, Chunlei
Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
title Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
title_full Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
title_fullStr Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
title_full_unstemmed Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
title_short Research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
title_sort research on quality evaluation of innovation and entrepreneurship education for college students based on random forest algorithm and logistic regression model
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280670/
https://www.ncbi.nlm.nih.gov/pubmed/37346726
http://dx.doi.org/10.7717/peerj-cs.1329
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