Restructuring and Effectiveness Analysis of the Teaching Model of “Data Mining” Driven by Rain Classroom

DOI

10.2991/978-2-38476-601-7_10How to use a DOI?

Keywords

Rain Classroom; Teaching Model; Data Mining; Teaching Reform

Abstract

Addressing the main drawbacks, such as the disconnect between theory and practice, insufficient teacher-student interaction and lack of formative assessment, of traditional lecture-based teaching in Data Mining courses. this study utilizes the Rain Classroom (RC) smart teaching tool to construct a three-stage online-offline collaborative teaching model: “pre-class preparation guidance – in-class interactive deepening – post-class consolidation and extension”. Through a closed-loop of pre-class resource delivery, real-time interactive Q&A during class, and post-class Q&A feedback, a deep two-way interaction between teachers and students is achieved. Practice results show that this model significantly improves learning outcomes: attendance rate increased by 7 percentage points, the highest completion rate of in-class exercises reached 96%, and the average score on homework increased by 7 points; the median final grade improved, the proportion of high-scoring students increased, the proportion of low-scoring students decreased, and the dispersion of grades decreased. This teaching model narrows the gap of learning outcomes between students and improves the whole learning outcomes, which may provide a practical reference for the reform of smart teaching in engineering courses in universities.

Copyright

© 2026 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.

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TY  - CONF
AU  - Xiangyun Guo
AU  - Tian Zhao
AU  - Min Hu
AU  - Li Li
PY  - 2026
DA  - 2026/08/06
TI  - Restructuring and Effectiveness Analysis of the Teaching Model of “Data Mining” Driven by Rain Classroom
BT  - Proceedings of the 2026 2nd International Conference on Education Reform, Ideology and Politics (ERIP 2026)
PB  - Atlantis Press
SP  - 72
EP  - 81
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-601-7_10
DO  - 10.2991/978-2-38476-601-7_10
ID  - Guo2026
ER  -