<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the context of the era of modern information technology to promote the digital transformation of education, it is also imperative to explore the intelligence of classroom teaching evaluation. This paper proposes a Bayesian probabilistic model based on PyMC3, weights it, and introduces the principle of incremental learning, which greatly reduces the error of the plain Bayesian algorithm in collecting and processing data teaching evaluation data. The Bayesian algorithm is used to classify and filter original classroom teaching evaluation data. The data analysis part of this paper examines the intelligent classroom, the teacher’s lecture scoring, and the student’s grades. With the help of the weighted plain Bayesian algorithm, the level of student participation in the traditional lecture mode and intelligent classroom were determined respectively, from which the classroom acceptance level and the probability of high or low evaluation of teaching were calculated. From the data collection, it can be seen that the full attendance rate of students in three months reached 54%, and the average satisfaction score of the algorithm on the teacher’s instruction reached 0.53. Therefore, the plain Bayesian algorithm used in this paper can effectively analyze the teaching evaluation data and accurately visualize the data association between student classroom engagement and teaching evaluation.</p>
</abstract>

In the context of the era of modern information technology to promote the digital transformation of education, it is also imperative to explore the intelligence of classroom teaching evaluation. This paper proposes a Bayesian probabilistic model based on PyMC3, weights it, and introduces the principle of incremental learning, which greatly reduces the error of the plain Bayesian algorithm in collecting and processing data teaching evaluation data. The Bayesian algorithm is used to classify and filter original classroom teaching evaluation data. The data analysis part of this paper examines the intelligent classroom, the teacher’s lecture scoring, and the student’s grades. With the help of the weighted plain Bayesian algorithm, the level of student participation in the traditional lecture mode and intelligent classroom were determined respectively, from which the classroom acceptance level and the probability of high or low evaluation of teaching were calculated. From the data collection, it can be seen that the full attendance rate of students in three months reached 54%, and the average satisfaction score of the algorithm on the teacher’s instruction reached 0.53. Therefore, the plain Bayesian algorithm used in this paper can effectively analyze the teaching evaluation data and accurately visualize the data association between student classroom engagement and teaching evaluation.

Research on the Evaluation of Deeply Intelligent Classroom Teaching and Learning in Colleges and Universities Based on Data Analysis

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the context of the era of modern information technology to promote the digital transformation of education, it is also imperative to explore the...

Research on the Evaluation of Deeply Intelligent Classroom Teaching and Learning in Colleges and Universities Based on Data Analysis

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 22 gru 2023

Przyjęty: 29 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0255

Słowa kluczowe
Plain Bayes, Intelligent classroom, Incremental learning, PyMC3, Teaching evaluation

© 2024 Fenglan Liang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the Evaluation of Deeply Intelligent Classroom Teaching and Learning in Colleges and Universities Based on Data Analysis

Data publikacji: 31 sty 2024

Zakres stron: -

Otrzymano: 22 gru 2023

Przyjęty: 29 gru 2023

DOI: https://doi.org/10.2478/amns-2024-0255

Słowa kluczowePlain Bayes, Intelligent classroom, Incremental learning, PyMC3, Teaching evaluation

© 2024 Fenglan Liang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Plain Bayes, Intelligent classroom, Incremental learning, PyMC3, Teaching evaluation