Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm

Abstract

Introduction: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. Objectives: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. Methods: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. Results: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. Conclusion: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

Construction of Teaching Management Platform for Universities Based on Big Data

“…At present, more and more high university teaching management platforms begin to try to adopt big data technology and establish the teaching management platform, but most universities' teaching management platforms are relatively independent and lack communication, and there are many "information silos," which restrict the integration and sharing of educational data [11,12]. erefore, colleges and universities need to build a scientific online education resource exchange and sharing mechanism through mutual communication to promote the intermingling of big data and build a data integration platform.…”

College English Intercultural Teaching Strategies Based on Network Construction under the Concept of Teaching to the Future

“…It targets different learning objects, carries out different teaching contents, and achieves different teaching goals. SPOC also has the characteristics of a flipped classroom, and the course learning is completed through SPOC English learning [7]. This paper helps teachers to carry out heuristic teaching in the classroom and focus on explaining the key knowledge structure of the course.…”

Supervised learning through k-nearest neighbor, used in the prediction of university teaching performance

“…Qu et al [34] it is pointed out that an accuracy of 97.2% and a sensitivity of 96.5% validate the capacity of the optimal performance of the classifier algorithm. Similarly, Lee et al [33], accuracy, precision and sensitivity values of 87.44%, 52.84% and 50.68%, respectively, were obtained, which will be applied in the prediction of students who graduate on time. As indicated in [35] a high percentage of sensitivity is important because it reflects the ability of the supervised learning model to predict positive classes and a high percentage of accuracy reflects the ability to distinguish only relevant data.…”

“…Likewise, it is similar to the study carried out in [32] where it is indicated that the K-nearest neighbor algorithm presents the best prediction results of users in virtual education environments with an accuracy of 91%. Lee et al [33] where the authors point out that an accuracy greater than 90, it is possible to optimally predict the graduation of the students. This statement is supported by [34], in this study the authors state that in supervised learning the precision of the algorithm depends on the accuracy, for this reason it is important that this indicator provides us with optimal values.…”