Title: Analysis and research on the integrated English teaching effectiveness of internet of things based on stochastic forest algorithm

Authors: Xiaoying Hu

Addresses: Foreign Language Teaching Department, Inner Mongolia University for Nationalities, Tongliao, 028000, China

Abstract: In order to overcome the problem of low accuracy in the analysis of teaching effect, this paper proposes a new method for English teaching effect analysis. The decision tree model is constructed through data training, and the stochastic forest algorithm framework is built on this basis. Based on the binary data classification project, relying on data parallel units provided by the internet of things, and relying on integrated data, the sample division of current education data is completed and an open source platform to complete the internet of things docking is designed. The random algorithm is combined with the data indicators of the internet of things to obtain data clusters. According to the classification points of stochastic forest algorithm, datasets are merged to complete sub-aggregation, and the effect evaluation is achieved. The experimental results show that the aggregation rate of the evaluation data of the stochastic forest algorithm is 50% ≤ 80%, which is effective.

Keywords: stochastic forest; internet of things; teaching effect; framework of the algorithm.

DOI: 10.1504/IJCEELL.2022.121222

International Journal of Continuing Engineering Education and Life-Long Learning, 2022 Vol.32 No.1, pp.1 - 18

Received: 23 Aug 2019
Accepted: 10 Dec 2019
Published online: 01 Mar 2022 *

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