College Students' Innovation and Entrepreneurship, Government Driven and the Effect Evaluation of the Policy System

Wang, Baijie; Chang, Junpeng; Huang, Silong

doi:10.1145/3429630.3429643

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…With promotion of the "Emerging Engineering Education" construction plan, considerable research has been conducted on teaching methods. To improve interdisciplinary applications, enhance the fusion of theory and practice, and enrich the innovation and entrepreneurship education curriculum [14], the Innovation and Entrepreneurship Center of East China University of Science and Technology (IEC-ECUST) began offering an interdisciplinary and interprofessional public elective course titled "Machine Vision Algorithms Training" from January 2021. The course is offered to all students and is not dependent on their majors.…”

Section: Introductionmentioning

confidence: 99%

Machine Vision Algorithm Training Course Construction with PBL

Jiang¹,

Wan²,

Zhu³

et al. 2022

IJIET

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Interdisciplinary integration of theory and practice is imperative as a course requirement in emerging engineering education, and in the public elective course "Machine Vision Algorithm Training". Considering the entire teaching process, including pre-training, in-training, and post-training, this paper discusses the course construction and content in detail in terms of project-based learning (PBL). The PBL teaching approach and evaluation methods are described in detail through a comprehensive face recognition training case based on a convolutional neural network (CNN) and Raspberry Pi. Through project design training from shallower to deeper, interdisciplinary integration of theory and practice is cultivated, stimulating interest in course study. The results demonstrate that PBL teaching improves the engineering application and innovative abilities of students.

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Section: Introductionmentioning

confidence: 99%

Machine Vision Algorithm Training Course Construction with PBL

Jiang¹,

Wan²,

Zhu³

et al. 2022

IJIET

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“…Entrepreneurship is an activity with high risk and high failure rate, especially the new startup business [1][2][3][4][5][6][7][8]. In the business running process, many factors can affect entrepreneurship, such as external factors of policy and market, and internal factors of manpower, experience, and resource [9][10][11][12][13]. As the saying goes, "experience is the best teacher", so among these factors, the entrepreneurial experience of entrepreneurs is a more important factor that closely related to the opportunity identification, strategic development, and enterprise performance of the newly created business [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Social Experience Guidance for College Students’ Entrepreneurship in the Social Network

Li¹,

Li²,

Huang³

et al. 2022

Int. J. Emerg. Technol. Learn.

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The low success rate of entrepreneurship is quite common for contemporary college students, and its main reason can be attributed to their lack of social experience and entrepreneurial guidance, thus, it’s of certain practical significance to study the social experience guidance for college students’ entrepreneurship and this paper aims to explore this problem based on social network. At first, this paper introduced the formation mechanism of the social experience guidance for college students’ entrepreneurship, proposed a novel Social Network Representation Learning (SNRL) method for college students’ entrepreneurship, which could attain more information of social experience guidance from networks with isomorphic substructures. Then, in the social network of college students’ entrepreneurship, this paper discussed the extraction method of structural subgraph of neighborhood space of college students and other entrepreneurial subject nodes, and proposed a method for building sub-networks similar to the scale and development state in the social network of college students’ entrepreneurship, and realized the information sharing of social and entrepreneurial experiences among sub-networks. At last, this paper constructed a Social and Entrepreneurial Experience Guidance (SEEG) model, and verified its effectiveness in experiments.

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College Students' Innovation and Entrepreneurship, Government Driven and the Effect Evaluation of the Policy System

Cited by 2 publications

References 7 publications

Machine Vision Algorithm Training Course Construction with PBL

Machine Vision Algorithm Training Course Construction with PBL

Social Experience Guidance for College Students’ Entrepreneurship in the Social Network

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