基于k近邻的教师敬业度分类分析及干预措施的实施

Sucianna Ghadati Rabiha, Sasmoko, Y. Indrianti
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引用次数: 1

摘要

在本研究中,我们使用k近邻方法根据先前通过印度尼西亚教师参与指数应用程序获得的指数值对教师档案进行分类。我们发现,如果使用等于88.94%和RMSE 0.300的小数据集进行测试,k -最近邻的准确性是相当好的。然而,如果再次测试更大的数据,结果的准确性下降到79.75%和RMSE 0.373。一些与意义性相关的干预设计通过有意义和充实的教育项目、有意义和充实的工作生活、有意义和充实的性格以及有意义和充实的领导者来提高教师的敬业度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implementation of K-Nearest Neighbor for Classification Teacher Engagement Profiling and Interventions
In this study we used the K-Nearest Neighbor approach to classify teacher profiles based on the Index values obtained previously through the Indonesian Teacher Engagement Index application. We found that the accuracy of the K-Nearest Neighbor is quite good if it is tested using a small data set that is equal to 88.94% and RMSE 0.300. Whereas if tested again into greater data the resulting accuracy decreases to 79.75% and RMSE 0.373. Some intervention designs related to meaningfulness to increase teacher engagement are through Meaningful and Fulfilling Education programs, Meaningful and Fulfilling Work Life, Meaningful and Fulfilling Character, and Meaningful and Fulfilling Leaders.
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