Constructing an AI-Assisted Teaching Model for Data Analysis and Data Visualization Courses in Higher Education: An Empirical Study Based on Student Performance Data

DOI

10.2991/978-2-38476-628-4_7How to use a DOI?

Keywords

AI-assisted teaching model; data analysis and data visualization; AI-TPACK; higher education; educational data mining; instructional framework

Abstract

The integration of AI tools in higher education is accelerating, yet few studies systematically embed them into data analysis and visualization instruction. This paper proposes the AI-DAV teaching model, a three-dimensional, AI-TPACK-grounded closed-loop framework spanning AI-assisted teaching, learning, and assessment. Deployed over a semester in an ND2009 course at a Chinese university (five classes, 244 students), the model was evaluated via a practicum project scored on five dimensions: data preprocessing, analysis, visualization, technique application and description, and report writing. The evaluation results reveal that Students scored highest in data visualization (80.4%), which received the most direct AI support, and lowest in data preprocessing (75.6%). One-way ANOVA showed significant inter-class differences ( F ( 4,239 ) = 2.84 , p = 0.025 , η 2 = 0.045 ), with the Information Systems Development class significantly outperforming others. The between-class gap was narrowest in visualization (5.7 points) and widest in preprocessing (8.9 points)—consistent with a compensatory effect, although bootstrap contrasts are not statistically distinguishable at this sample size. These findings empirically validate the model and yield actionable implications for AI-era instructional design.

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  - Ziwen Wang
AU  - Chunxu Li
AU  - Huisi Li
PY  - 2026
DA  - 2026/10/30
TI  - Constructing an AI-Assisted Teaching Model for Data Analysis and Data Visualization Courses in Higher Education: An Empirical Study Based on Student Performance Data
BT  - Proceedings of the 2026 2nd International Conference on Education Research and Training Technologies (ERTT 2026)
PB  - Atlantis Press
SP  - 61
EP  - 79
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-628-4_7
DO  - 10.2991/978-2-38476-628-4_7
ID  - Wang2026
ER  -