
Research Article
Visualization Techniques for Analyzing Learning Effects – Taking Python as an Example
@INPROCEEDINGS{10.1007/978-3-031-50580-5_4, author={Keshuang Zhou and Yuyang Li and Xue Han}, title={Visualization Techniques for Analyzing Learning Effects -- Taking Python as an Example}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Artificial Intelligence Python Visualization Word Cloud}, doi={10.1007/978-3-031-50580-5_4} }
- Keshuang Zhou
Yuyang Li
Xue Han
Year: 2024
Visualization Techniques for Analyzing Learning Effects – Taking Python as an Example
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_4
Abstract
With the advent of the information age, data visualization technology has gradually shown its unique features in various information fields, and its importance has gradually been attached importance by various governments and commercial departments. In teaching in our country, most schools simply use office software to create pie chart, histogram or table to realize visualization. This kind of teaching creates a single chart that doesn’t change at all. The content of the data visualization course of many school courses is outdated, and the way of visualization is not in line with the needs of The Times, that is, the content has been criticized as abstract, language mechanization, format and public culture [1]. In order to analyze the teaching quality and evaluate and improve it, this paper processes and analyzes the data exported from the teaching administration system based on python, mainly from three aspects: data acquisition, data processing and data analysis. Firstly, the python crawler technology is used to obtain students’ grades, secondly, the invalid data is processed, and finally, the matplotlib library is used to visualize the processed data, and the learning status of students in the class is analyzed and evaluated by combining the obtained images. Through the data processing of this paper, it realizes the hiding of the student’s name, protects the privacy of the student, and uses the graph to intuitively reflect the student’s grade distribution, which makes the grade analysis more convenient.