| Autor: |
Yi Guiyun, Wang Xiaodong, Jia Jianbo, Huang Shanxiu, Zhu Can, Kang Weiwei, Guo Xiangkun |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2444-8656 |
| DOI: |
10.2478/amns-2024-2101 |
| Popis: |
Classroom teaching mode is an important factor affecting classroom quality and students’ performance. The use of science and technology combined with advanced teaching concepts to rationalize classroom teaching innovation and change has become a research hotspot in the field of education. In this paper, we use the BOPPPS teaching mode on the basis of big data technology to promote teaching innovation in the existing classroom teaching chemical principles. Using a questionnaire survey to study the teaching status of chemical principles in a city university, it is found that the current chemical principle classroom teaching is generally characterized by the problems of solidified teaching mode design, single teaching evaluation method, random design of teaching objectives, etc. The BOPPPS model is applied to the teaching of chemical principles at a city university. The BOPPPS model is applied to the teaching practice of chemical principles in a university, and the student’s classroom performance before and after the change of teaching mode shows obvious differences. After the experiment, 64.17% of the students expressed hope that the BOPPPS teaching model could be promoted in other disciplines. Cluster analysis of students’ performance before and after the experiment using an optimized K-means clustering algorithm shows that the performance of the seven students extracted from the experiment significant improvement after the experiment. The use of the BOPPPS teaching mode makes the students’ score range increase from 60-70 before the test to 80-90 after the test. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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