The evaluation of college students’ innovation and entrepreneurship ability based on nonlinear model

Liang, Guangwen; Alghazzawi, Daniyal M.; Joseph, Nympha Rita

doi:10.2478/amns.2021.2.00101

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Through Bayesian reasoning, students can learn to leverage existing data, market trends, and industry insights to make informed judgments about business opportunities, potential risks, and optimal strategies [4]. Moreover, Bayesian methods facilitate a holistic approach to problem-solving, encouraging students to consider multiple sources of information and weigh uncertainties systematically, thereby enhancing their critical thinking and analytical skills [5]. Furthermore, Bayesian statistics offers a powerful framework for predictive modeling and hypothesis testing, enabling students to assess the viability of business ideas, refine product designs, and optimize marketing strategies with greater precision [6].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Chain Feedforward Bayesian Statistic Network (Rcf-Bsn) Model: A Fuzzy Based Approach to Recognize Entrepreneurship and the Imperative for Education to Adapt

Fanpeng Meng, Dongquan Han

2024

jes

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Entrepreneurial methods and ability enhancement are crucial aspects of college education, empowering students to develop the skills and mindset necessary for success in the dynamic world of entrepreneurship. Through targeted initiatives and experiential learning opportunities, colleges strive to foster innovation, creativity, and entrepreneurial spirit among their students. These programs often include courses on business fundamentals, startup management, and market analysis, complemented by hands-on projects, mentorship programs, and networking events. This paper proposed innovative entrepreneurial methods and the enhancement of ability among college students in the new era, employing Bayesian statistics with the Recurrent Chain Feedforward Bayesian Statistic Network (RCF-BSN). Recognizing the evolving landscape of entrepreneurship and the imperative for education to adapt accordingly, this research aims to explore effective methodologies for fostering entrepreneurial skills and mindset among college students. Results from the study shed light on the effectiveness of different entrepreneurial methods, such as experiential learning, mentorship programs, and startup incubators, in fostering entrepreneurial abilities among college students. Additionally, the study identifies key factors contributing to entrepreneurial success in the new era, including adaptability, resilience, and creativity. Quantitative analysis using Bayesian statistics reveals that students who participated in experiential learning programs showed a 30% increase in entrepreneurial self-efficacy scores compared to those who did not participate. Additionally, students who received mentorship through startup incubators demonstrated a 25% higher likelihood of launching their own ventures within one year of graduation.

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Section: Introductionmentioning

confidence: 99%

Recurrent Chain Feedforward Bayesian Statistic Network (Rcf-Bsn) Model: A Fuzzy Based Approach to Recognize Entrepreneurship and the Imperative for Education to Adapt

Fanpeng Meng, Dongquan Han

2024

jes

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“…The automatic recognition of student learning emotions is essential for creating supportive and adaptive learning environments [8][9][10]. By monitoring students' emotional states, teachers can adjust teaching strategies in a timely manner to better meet students' personalized needs [11,12]. At the same time, a student emotion recognition system can also help students self-regulate their learning processes, enhancing learning efficiency [13,14].…”

Section: Introductionmentioning

confidence: 99%

The Application and Optimization of Deep Learning in Recognizing Student Learning Emotions

Wu,

Pan

2024

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With the widespread application of deep learning technology across various fields, its potential value in educational technology, particularly in recognizing student learning emotions, has begun to gain attention. The real-time and accurate identification of learning emotions is crucial for facilitating personalized teaching and enhancing learning efficiency. This paper focuses on the automatic recognition of student learning emotions based on deep learning technology, aiming to improve the accuracy and practicality of recognition by optimizing the preprocessing of facial expression images and the temporal expression recognition model. The research starts with facial detection using Haar-like features and the Adaboost cascade method, followed by normalization of the detected facial images in scale, angle, and grayscale to enhance the system's robustness to facial image variations. Subsequently, a temporal expression recognition model based on a multi-attention fusion network is proposed. This model utilizes both shallow and deep features of deep learning, along with the prior knowledge of Facial Action Coding System (FACS), to capture the dynamic changes in facial expressions more intricately. Finally, by introducing three different attention mechanisms, this study significantly improved the efficiency and accuracy of emotion feature recognition in sequential data. The findings of this paper not only advance the technology of learning emotion recognition but also provide valuable insights for educational practice.

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“…A supportive educational environment is quite important for college students' entrepreneurship. A good environment can provide the necessary knowledge, skills and guidance for student entrepreneurs and help them transform their achievements successfully [10][11][12][13]. However, current research pays little attention on the coupling correlation between the support of educational environment and the transformation of student entrepreneurs' achievements [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Coupling Correlation between the Support of Educational Environment and the Transformation of Students’ Entrepreneurial Achievements

Wang¹,

Qu²,

Wang³

2023

Int. J. Emerg. Technol. Learn.

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College students often lack entrepreneurial experience and business skills, which makes them face many difficulties in the process of starting a business, in such case, a supportive educational environment can play an important role, but current research pays little attention on the coupling correlation between the support of educational environment and the transformation of student entrepreneurs’ achievements. In fact, a thorough understanding of this coupling correlation could provide effective support for colleges and policy makers to formulate educational policies and strategies, thereby increasing the success rate of student entrepreneurs. To fill in this research blank, this paper aims to study the coupling correlation between the support of educational environment and the transformation of student entrepreneurs’ achievements. At first, a College Student Entrepreneurship (CSE) network under the support of educational environment was created, and the cooperation mode of student entrepreneurs under the support of educational environment was presented. Then, the transformation ability of student entrepreneurs’ achievements was measured based on the Stochastic Block Model (SBM), a model for measuring the coupling between the support of educational environment and the transformation of student entrepreneurs’ achievements was constructed, and the modeling steps were introduced in detail. At last, the validity of the proposed network and models was verified by experimental results.

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The evaluation of college students’ innovation and entrepreneurship ability based on nonlinear model

Cited by 5 publications

References 11 publications

Recurrent Chain Feedforward Bayesian Statistic Network (Rcf-Bsn) Model: A Fuzzy Based Approach to Recognize Entrepreneurship and the Imperative for Education to Adapt

Recurrent Chain Feedforward Bayesian Statistic Network (Rcf-Bsn) Model: A Fuzzy Based Approach to Recognize Entrepreneurship and the Imperative for Education to Adapt

The Application and Optimization of Deep Learning in Recognizing Student Learning Emotions

Coupling Correlation between the Support of Educational Environment and the Transformation of Students’ Entrepreneurial Achievements

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