應用數據資料庫進行我國高中校務評鑑課程教學向度等第預測模型準確度及影響因素分析

Applying Data Databases to Analyze the Accuracy of Grade Prediction Models and Influencing Factors for Curriculum and Teaching Evaluations in Taiwan’s Senior High Schools

研究目的
本研究透過數據資料並輔以類神經網絡模型分析，探究我國高中校務評鑑結果預測模型之準確度，並進行數據資料實踐於教育評鑑的可能性之探討。考量國教署對於高中評鑑公布資料樣本數的分布，本研究主要建構我國高中評鑑課程教學向度中，獲得甲等與優等學校預測模型外，同時比較普通型高中與技術型高中預測模型的差異，找出提升校務經營的關鍵因素。
研究設計／方法／取徑
分析資料以國教署公告之高中校務評鑑成果，介接臺灣後期中等教育長期追蹤資料庫、教育部統計處學校背景資料進行分析，運用類神經網絡運算法進行模型預測正確度探討，了解數據預測評結果的可行性。
研究發現或結論
經分析結果得知：（一）高中校務評鑑應用資料庫數據預測率高於75%，普通型高中80%，技術型高中77.9%，故推動數據資料進行校務評鑑有其可能性；（二）臺灣高中學生對於學校滿意度，是預測學校評鑑課程及教學結果的關鍵因素；（三）普通型高中選修科目課程彈性與多元性，是預測學校評鑑課程及教學結果的關鍵因素；（四）技術型高中教師正向教學策略為影響學校課程及教學等第的重要預測因子。
研究原創性／價值
本研究利用後期中等教育長期追蹤資料庫與高中校務評鑑結果進行資料介接，並應用類神經網絡法建立評鑑結果預測模型。研究結果顯示，利用資料庫數據預測評鑑結果存在可能性，亦為驗證數據資料進行校務評鑑是可行的發展方向。
教育政策建議或實務意涵
本研究認為，數據資料庫發展有高度價值，除協助中央教育主管機關了解教育現況外，透過本研究顯示對於當前實務工作之應用有其可行性。本研究僅對課程與教學向度進行分析，若未來持續拓展與深化，將有助於降低教育行政有關部門與學校之間的行政壓力，同時相關研究成果與建議對於高中校務經營績效提升具有相當的參考價值。

Purpose
This study utilizes data analysis supplemented with neural network models to explore the accuracy of prediction models for high school evaluation results in Taiwan and discusses the feasibility of applying data practices in educational evaluations. Considering the distribution of sample sizes for high school evaluations published by the Ministry of Education, this study primarily constructed prediction models for schools receiving grades A and B in the curriculum and teaching dimensions of Taiwan’s high school evaluations. Additionally, it compares the differences in prediction models for general and vocational high schools and identifies the key factors for improving school management.
Design/methodology/approach
The analysis is based on high school evaluation results, linking long-term tracking data from Taiwan’s post-secondary education and school background data from the Ministry of Education’s Statistical Office. The feasibility of data-driven evaluation result prediction was understood using artificial neural network algorithms for model prediction accuracy exploration.
Findings/results
The analysis results reveal that (1) the prediction rate of high school evaluations using database data exceeds 75%, with general high schools at 80% and vocational high schools at 77.9%, indicating the feasibility of using data for school evaluations; (2) the satisfaction level of Taiwanese high school students with their schools is a key factor in predicting the outcomes of school evaluations in curriculum and teaching; (3) the flexibility and diversity of elective courses in high schools are critical predictors of the outcomes of school evaluations in curriculum and teaching; and (4) positive teaching strategies used by teachers in vocational high schools are significant predictors of school curriculum and teaching grades.
Originality/value
This study analyzed data from database information and high school evaluation results by employing neural network methods to establish a predictive model for evaluation outcomes. The research findings indicated the feasibility of using database data to predict evaluation results, validating that using data for school evaluations is a viable direction for development.
Suggestions/implications
This study believes that establishing data databases is valuable because it not only helps the Ministry of Education understand the current educational landscape but also demonstrates the feasibility of developing data-driven school evaluations. This study focused primarily on the dimensions of curriculum and teaching for analysis. If future efforts continue to expand and deepen this approach, it may serve to reduce administrative pressure between educational administrative departments and schools. Simultaneously, the research findings and recommendations offer significant reference values for enhancing the operational efficiency of high school management.