Open AccessAnalysis and Prediction of the Trend Features for Teaching Development Based on Knowledge Discovery
Author(s) -
Peng Su,
Yan Wang,
Ping Zhao,
Gao Ming-li,
Xiwen Liu,
Guiling Liu,
Changtian Wang
Publication year - 2022
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v17i05.29845
Subject(s) - computer science , chaotic , quality (philosophy) , mathematics education , development (topology) , artificial intelligence , psychology , mathematics , mathematical analysis , philosophy , epistemology
The existing research on teaching development of teachers fails to effectively quantify the teaching development trend. This paper deeply mines the evaluation data on the teaching quality of college teachers, before analyzing and predicting the trend features for teaching development of college teachers based on knowledge discovery. Firstly, the knowledge features of the teaching development trend of college teachers were examined. Next, the fluctuation features of the time series on the teaching quality development of college teachers were described based on chaotic time series. In addition, a prediction model for teaching development of college teachers was established for weighted first-order chaotic time series, and used to simulate the nonlinear features of the time series on the teaching quality development of college teachers. The prediction model was proved effective through experiments.