Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

October 21, 2023


Pages


DOI

Article

Research on the Reform of Film and Television Production Courses and Their Student Achievement Evaluation in Colleges and Universities in Shanxi Province Based on Random Forest Algorithm

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Authors

Tianyu Shi Affiliation:
Faculty of Social Sciences and Liberal Arts UCSI University. No.1, Jalan Menara Gading, UCSI Heights (Taman Connaught), 56000 Cheras, Kuala Lumpur, Malaysia.
and Vimala AP Govindasamy Affiliation:
Faculty of Social Sciences and Liberal Arts UCSI University. No.1, Jalan Menara Gading, UCSI Heights (Taman Connaught), 56000 Cheras, Kuala Lumpur, Malaysia.


Abstract

This paper classifies the data of the original data set by random forest algorithm, selects the nodes in the attribute space for iteration, and gets the number of decision trees in the random forest. Based on the decision tree, the information gain rate in the sample set of student achievement is calculated, the spatial distance matrix of the sample set is defined, and the centroids of each cluster of the matrix indicators are separated to get the evaluation results of indicators in the student achievement evaluation reform as superior. The indicator weights of student learning achievement are evaluated through five assessment indexes, in which the teacher rating weight is the highest 10. It shows that active use of the Internet is conducive to cultivating and delivering excellent film and television production professionals to society.


Keywords

Random forest algorithm, Decision tree, Sample set, Performance evaluation, Information gain rate, 97Q70


Citation

Shi, T. & Govindasamy, V. A. (2023). Research on the reform of film and television production courses and their student achievement evaluation in colleges and universities in shanxi province based on random forest algorithm. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.00713

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