Volume 3,Issue 7
Research on Visualization Analysis and Decision Support Application of Teaching Supervision Classroom Observation Data from the Perspective of Big Data — A Case Study of Changji Vocational and Technical College
With the in-depth development of educational informatization, vocational colleges have accumulated massive amounts of teaching supervision classroom observation data in daily teaching management. Traditional data processing methods are insufficient to explore the in-depth value of data and provide intuitive support for decision-making. Taking the supervision data of Changji Vocational and Technical College from the 2022-2023 academic year as a sample, this study explores how to use technology stacks such as ECharts, Vue.js, and Python (Pandas, Scikit-learn) to build a decision support platform that integrates data integration, analysis, and visualization[1]. The platform realizes functions including global situation awareness, multi-dimensional drill-down analysis, time trend tracking, individual portrait and correlation analysis, and incorporates algorithms such as K-Means clustering and correlation analysis to deeply explore data value. This application converts abstract scoring data into intuitive charts and dashboards, aiming to help school administrators quickly gain insights into the distribution of teaching quality, identify weak links in teaching and dominant disciplines. Thereby, it provides scientific and efficient data support for teaching reform, the allocation of teacher training resources and strategic decision-making, and promotes the continuous improvement of teaching quality[2].
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[2] Li X J, 2024, Research on data visualization in the era of big data [Master's Thesis]. Hebei University. DOI: CNKI: CDMD:2.1014.040389.
[3] Yang X M, 2024, Design and Implementation of Precision Teaching Visualization Based on Data Driven. Computer Knowledge and Technology, 20(30): 67-70.
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[7] Song W W, Sun L Q, 2019, Comparative analysis of data loading modes for big data visualization. Computer Knowledge and Technology (Academic Edition), 15(12X): 2.
[8] Cui P, 2019, Application of ECharts in data visualization. Software Engineering. DOI: CNKI: SUN:ZGGC.0.2019-06-012.
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