Research on the Analysis and Quality Improvement of University Students’ Learning Behavior from the Perspective of Big Data
- DOI
- 10.2991/978-94-6463-034-3_136How to use a DOI?
- Keywords
- Big Data; Learning Behavior; Quality Improvement
- Abstract
The era of big data is not only a technological revolution, but also a revolution in thinking and paradigm. The essence of teaching quality in universities is to analyze the learning behavior of university students and improve the quality of learning. This research uses big data to track and investigate the learning behavior of S university students in the four academic years of 2016–2020, through the study activities, extracurricular learning, academic activities and graduation thesis/design, etc. Investigation and analysis, using quantified data to present the status and problems of university students’ learning behavior, and proposing to establish a learning quality support system with a “three-in-one” learning behavior data collection system, a learning quality evaluation system, and a learning quality continuous improvement system. The refined teaching in the data age provides a kind of reference and reference.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jiaming Sun PY - 2022 DA - 2022/12/23 TI - Research on the Analysis and Quality Improvement of University Students’ Learning Behavior from the Perspective of Big Data BT - Proceedings of the 2022 3rd International Conference on Big Data and Informatization Education (ICBDIE 2022) PB - Atlantis Press SP - 1323 EP - 1330 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-034-3_136 DO - 10.2991/978-94-6463-034-3_136 ID - Sun2022 ER -