Internet Plus Innovation and Entrepreneurship Education Model Based on Machine Learning Algorithms

Chen, Xiaoxia

doi:10.1155/2022/6176675

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…In their research, they not only crafted a platform architecture with diverse functions but also validated its precision and effectiveness through empirical studies. They also proposed strategic directions for the advancement of the Internet Plus education framework to bolster students' capabilities in innovation and entrepreneurship [62]. He et al investigated the present conditions and hurdles faced by innovation and entrepreneurship education in China, pinpointing deficiencies in student initiatives and the assessment systems.…”

Section: Innovative Educationmentioning

confidence: 99%

Machine Learning Algorithms for Fostering Innovative Education for University Students

Wang,

You,

2024

Electronics

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Data augmentation with mixup has been proven effective in various machine learning tasks. However, previous methods primarily concentrate on generating previously unseen virtual examples using randomly selected mixed samples, which may overlook the importance of similar spatial distributions. In this work, we extend mixup and propose MbMix, a novel yet simple training approach designed for implementing mixup with memory batch augmentation. MbMix specifically selects the samples to be mixed via memory batch to guarantee that the generated samples have the same spatial distribution as the dataset samples. Conducting extensive experiments, we empirically validate that our method outperforms several mixup methods across a broad spectrum of text classification benchmarks, including sentiment classification, question type classification, and textual entailment. Of note, our proposed method achieves a 5.61% improvement compared to existing approaches on the TREC-fine benchmark. Our approach is versatile, with applications in sentiment analysis, question answering, and fake news detection, offering entrepreneurial teams and students avenues to innovate. It enables simulation and modeling for student ventures, fostering an entrepreneurial campus culture and mindset.

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Section: Innovative Educationmentioning

confidence: 99%

Machine Learning Algorithms for Fostering Innovative Education for University Students

Wang,

You,

2024

Electronics

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“…The efficacy of the model is validated using a comprehensive questionnaire. A holistic platform and core business processes for assessing the quality of I&E in HEI’s is proposed by Chen (2022a) and Chen (2022) . The proposed system integrated many modules like skill diagnosis, project management skills, online teaching-learning, team building resource availability, testing and assessment evaluation, information and resource sharing etc.…”

Section: Related Workmentioning

confidence: 99%

“…Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) was employed by Chen (2022a) and Chen (2022) to check the effectiveness of the I&E in modern education system. Entrepreneurship orientation among the individuals is studies and analysed using the predictive analytics approach using ridge, lasso, SVM, BP, and RF ( Sabahi & Parast, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

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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

Lü¹,

Chai

Ren

et al. 2023

PeerJ Computer Science

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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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“…I&E education is doubtlessly important for higher vocational colleges to realize their ultimate goal of cultivating technical talents with professional skills [1][2][3][4], so I&E education and practice course should be built into the compulsory course system of higher vocational colleges, so that students could master skills and knowledge required for starting a business, and their employment opportunity and entrepreneurship success rate could both be increased [5][6][7][8][9][10][11]. Moreover, this can also train their ability to solve problems and rise to challenges, from the perspective of society, this is conductive to creating more innovative enterprises and providing powerful impetus for economic development [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Quality Evaluation of Innovation and Entrepreneurship Education Based on Modelling of Students’ Behavior Sequences

Niu¹

2023

Int. J. Emerg. Technol. Learn.

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In the Innovation and Entrepreneurship (I&E) education of some higher vocational colleges, there’s a common problem: serious disconnection between professional education and practical education, which may result in poor quality of I&E education. With the help of I&E education quality evaluation, we can figure out the distribution and utilization of education resources, discover and solve problems during the teaching process in time, and optimize and adjust teaching content and methods in a targeted manner. Student behavior analysis can reveal the actual needs and questions encountered by students during I&E education, thereby attaining more pertinent and pragmatic evaluation results. For this reason, this paper aims to study the I&E education quality evaluation by means of modelling students’ behavior sequences. At first, a student I&E behavior sequence feature extraction module was created based on attention mechanism, and the student I&E ability level feature extraction layer and student I&E ability level feature evolution layer contained in the module structure were introduced in detail. Then, the data sources of I&E education quality evaluation based on students’ I&E behavior analysis were given, the I&E behavior sequences of students were modeled based on the single-sequence first-order linear differential equation model GM(1,1), and the established model was applied to I&E education quality evaluation. At last, experimental results verified the validity and accuracy of the proposed method.

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Internet Plus Innovation and Entrepreneurship Education Model Based on Machine Learning Algorithms

Cited by 8 publications

References 15 publications

Machine Learning Algorithms for Fostering Innovative Education for University Students

Machine Learning Algorithms for Fostering Innovative Education for University Students

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

Quality Evaluation of Innovation and Entrepreneurship Education Based on Modelling of Students’ Behavior Sequences

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