Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks

Jin, Yanliang; Yang, Yiwen; Yang, Baican; Zhang, Yunfu

doi:10.1155/2021/6275096

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…As human society enters the information age, education also enters the information age with human society, knocking at the information age of education [1]. In the information age, the introduction of new educational technology promotes the rapid growth of educational informatization, and massive data are generated and accumulated in the database.…”

Section: Introductionmentioning

confidence: 99%

Application of the Data Mining Model in Smart Mobile Education

Wang

Ding

2022

Mobile Information Systems

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In order to solve the problem that education departments urgently need intelligent and efficient information technology to deal with massive data, so as to mine valuable information for management decision-making, an overall scheme framework of education informatization is proposed. The framework takes data mining as a tool, combined with the theoretical knowledge of cloud computing, and takes the student data of a school as an example to verify the practicability of the framework system. The results are as follows: the framework clusters the students of a school. The overall level of students in cluster 1 is high, accounting for 45.07% of the total number. These kinds of students have solid basic theory, strong logical thinking ability, and excellent professional knowledge learning. The overall level of students in cluster 2 is average, accounting for 38.03% of the total number. This kind of students should pay attention to the study of professional courses and the cultivation of professional skills. The rest are students in cluster 3. These kinds of students have weak basic professional knowledge and poor thinking and logic ability. Through the accuracy before and after pruning output from the model, the accuracy before pruning is 87.32% and the accuracy after pruning is 97.37%. The noise data are eliminated in the process of pruning. The framework established in this study provides a certain decision-making basis for education and teaching and explains the feasibility and effectiveness of the application of data mining technology in educational informatization.

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Section: Introductionmentioning

confidence: 99%

Application of the Data Mining Model in Smart Mobile Education

Wang

Ding

2022

Mobile Information Systems

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“…[3] [4]. The use of neural networks for teaching evaluation was investigated by Y Jin et al [5]. A study of the Numeracy Learning Platform for Adolescents was conducted by Shafie et al [6].…”

Section: Introductionmentioning

confidence: 99%

Research on International Talent Cultivation Mode of Internet of Things Professional Cluster under Digital Intelligence

Zhang

2024

SHS Web Conf.

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This paper researches the current talent cultivation mode under the new ecology of IoT professional group by analyzing the real demand of China-ASEAN for composite talents of IoT professional group, discussing the influence of digital intelligence on the cultivation of new engineering talents, and designing the mechanism framework of “ASEAN+Digital Intelligence”. The mechanism studied can effectively solve the problems of poor integration of talent cultivation, lack of industry-driven curriculum system and single type of output on the supply side of teaching. It has certain guidance and reference significance for the new mode of talent cultivation in the new engineering professional group in digital intelligence.

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“…Such an evaluation method needs to consider the needs and characteristics of different students and ensure the fairness and effectiveness of the evaluation. The research on the evaluation methods of the quality of higher integrated education has important theoretical and practical significance [3][4][5]. Firstly, through the study of different course evaluation methods, it enables educational institutions to assess the teaching effect and student learning situation of the integrated courses accurately and provides a scientific basis for teaching.…”

Section: Introductionmentioning

confidence: 99%

Course Quality Evaluation of Higher Integrated Distance Education Based on Group Decision-making Algorithm

Wang,

Wang

2024

Preprint

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Higher integrated distance education courses involve an educational process where students with and without disabilities learn together. The course quality evaluation methods of these courses require indicators that fully consider the conditions of different students. Due to the significant differences among students, the methods primarily based on the superposition of personal evaluations cannot be applied to these two special student groups. It leads to a lack of consensus and balance in the course quality evaluation and lower quality and accuracy in decision-making. To solve this problem, this paper proposes a group decision-making algorithm for the quality evaluation of higher integrated distance education courses. In a large, differentiated student group, the decision-making results are formed by collecting, integrating, and weighing group members' opinions, knowledge, experience, and other information. Firstly, the algorithm proposed uses the K-means algorithm to cluster and collect the learning behavior data of students into predefined K clusters based on feature similarity. Secondly, this algorithm constructs course quality evaluation indicators from multiple perspectives under the support of multiple evaluation principles. Finally, this algorithm brings together the personal preferences of each decision-maker into a collective preference, allowing these decision-makers to rank the decision-making algorithm by their preference in this paper. Students are regarded as decision-makers, the higher integrated education courses are regarded as decision-making options, the learning behaviors and preferences of students are regarded as attribute decision variables. Thus, the quality evaluation of higher integrated distance education courses can be regarded as a group decision-making process according to the problem description above. This process makes decisions through iterative optimization. Experimental results show that this algorithm performs excellently in the metrics consistency ratio and comprehensive score and has practical application value.

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Evaluation Model of Educational Curriculum in Higher Schools Based on Deep Neural Networks

Cited by 6 publications

References 36 publications

Application of the Data Mining Model in Smart Mobile Education

Application of the Data Mining Model in Smart Mobile Education

Research on International Talent Cultivation Mode of Internet of Things Professional Cluster under Digital Intelligence

Course Quality Evaluation of Higher Integrated Distance Education Based on Group Decision-making Algorithm

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