Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning
With the globalization of network education and the design and construction of all aspects of engineering, network science education is playing an increasingly important role in higher education and even the lifelong education system of college students. The purpose of this article is to study emoti...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/1231791 |
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| author | Pengjiao Li Qian Meng |
| author_facet | Pengjiao Li Qian Meng |
| author_sort | Pengjiao Li |
| collection | DOAJ |
| description | With the globalization of network education and the design and construction of all aspects of engineering, network science education is playing an increasingly important role in higher education and even the lifelong education system of college students. The purpose of this article is to study emotional interaction in deep learning network education and analyze the status quo of its behavioral decision-making mechanism. It uses research literature method, algorithmic statistical method, and questionnaire survey method to investigate specific groups of people; analyzes the status quo of emotional interaction and behavioral decision-making mechanism; improves statistical algorithms; and explores an old style emotional cognitive decision-making model. In this paper, a questionnaire survey of a university shows that the proportion of students whose online learning time is 1.5–2 hours is about 10.3% and the proportion of 1–1.5 hours is about 6.8%. The study time of students’ online courses is mainly concentrated. The study time between 0.5 and 1 hour accounts for about 83.2%; about 2.3% of learners rarely use the Internet, less than 0.5 hour; and 1% of students hardly use online courses and may rely more on traditional classroom teaching. Further research showed the behavior of their emotional interaction: interactive teaching network in six modules reached the upper level, the peak value of the curve was 0.737, the bottom value was 0.115, and the transitivity was above 0.115. From deep statistical learning algorithms to completing network science education, designing or modifying more comprehensive and faster bpq-l learning algorithms based on traditional learning algorithms can allow us to find target sentiments. |
| format | Article |
| id | doaj-art-1ef4fbbc01f343f3bc3c43ef0164cec4 |
| institution | Kabale University |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-1ef4fbbc01f343f3bc3c43ef0164cec42025-02-03T01:08:01ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/1231791Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep LearningPengjiao Li0Qian Meng1College of EducationCollege of EducationWith the globalization of network education and the design and construction of all aspects of engineering, network science education is playing an increasingly important role in higher education and even the lifelong education system of college students. The purpose of this article is to study emotional interaction in deep learning network education and analyze the status quo of its behavioral decision-making mechanism. It uses research literature method, algorithmic statistical method, and questionnaire survey method to investigate specific groups of people; analyzes the status quo of emotional interaction and behavioral decision-making mechanism; improves statistical algorithms; and explores an old style emotional cognitive decision-making model. In this paper, a questionnaire survey of a university shows that the proportion of students whose online learning time is 1.5–2 hours is about 10.3% and the proportion of 1–1.5 hours is about 6.8%. The study time of students’ online courses is mainly concentrated. The study time between 0.5 and 1 hour accounts for about 83.2%; about 2.3% of learners rarely use the Internet, less than 0.5 hour; and 1% of students hardly use online courses and may rely more on traditional classroom teaching. Further research showed the behavior of their emotional interaction: interactive teaching network in six modules reached the upper level, the peak value of the curve was 0.737, the bottom value was 0.115, and the transitivity was above 0.115. From deep statistical learning algorithms to completing network science education, designing or modifying more comprehensive and faster bpq-l learning algorithms based on traditional learning algorithms can allow us to find target sentiments.http://dx.doi.org/10.1155/2022/1231791 |
| spellingShingle | Pengjiao Li Qian Meng Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning Advances in Multimedia |
| title | Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning |
| title_full | Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning |
| title_fullStr | Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning |
| title_full_unstemmed | Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning |
| title_short | Emotional Interaction and Behavioral Decision-Making Mechanism in Network Science Education Based on Deep Learning |
| title_sort | emotional interaction and behavioral decision making mechanism in network science education based on deep learning |
| url | http://dx.doi.org/10.1155/2022/1231791 |
| work_keys_str_mv | AT pengjiaoli emotionalinteractionandbehavioraldecisionmakingmechanisminnetworkscienceeducationbasedondeeplearning AT qianmeng emotionalinteractionandbehavioraldecisionmakingmechanisminnetworkscienceeducationbasedondeeplearning |