基于欣赏探究属性的校领导积极特质水平辨析:对提高教师协作与校本管理水平的启示

Rosalia P Austria, Jollie N. Alson
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引用次数: 0

摘要

本研究采用定量、评估和描述性研究设计来确定学校领导的欣赏式探究(AI)实践水平,以及哪些AI实践预测了学校领导协作的改进,这些改进可用于制定AI策略。本研究采用单因素方差分析、人积差相关及多元回归分析。研究人员对157名学校领导进行问卷调查,并根据其行政职能进行分组。根据所收集的数据和所做的分析,得出了以下结论:在学校领导中,校长的人工智能水平最低,但在SBM组成部分的实践中,他们的感知结果最高;2. 在发现、梦想、设计、命运四个维度上,与校领导人工智能水平实践相关的所有变量与校领导人工智能水平实践呈低到中等正相关;3.发现SBM组成变量对更高的SBM水平“非常重要”和贡献因素。4. 在AI的阶段中,Design和Destiny预测高度协作,其中学校和学校领导的AI水平每增加1个单位,SBM绩效就会相应提高。研究的结论是,学校校长可以作为人工智能实践者树立良好的榜样,有效促进合作。学校可以使用建议的策略,因为它们是基于学校领导的最佳实践,并且被发现是高SBM绩效的预测因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differentiating the Level of the School Leaders’ Positive Traits Anchored on Appreciative Inquiry Attributes: Inputs to Improving Teachers’ Collaboration and the School-Based Management Level
This study used Quantitative, evaluative, and descriptive research designs to determine the school leaders’ Appreciative Inquiry (AI) level of practice and which among the AI practices predict improvements in the school leaders’ collaboration that can be used in crafting AI strategies. One-Way Analysis of Variance, Pearson-Product-Moment Correlation, and Multiple Regression were used in this study. The researcher made-questionnaires were administered to the 157 school leaders that were grouped based on their administrative function. The findings were articulated based on the data gathered and the analysis made: 1. School heads had the lowest AI level among the school leaders, but they were found to have the highest perception results when it comes to the SBM components practices; 2. there was a low to moderate positive correlation among all the variables related to SBM components and the school leaders’ AI level practices in terms of discovery, dream, design, and destiny; 3. the SBM components variables were found to be “very important” and contributory factor to a higher SBM level. 4. Among the phases of AI, Design and Destiny predict high collaboration in which, for every one (1) unit increase in the school and school leader’s AI level, there was a corresponding increase in the SBM performance. It was concluded that school heads may set good examples as AI practitioners to effectively promote collaboration. Schools may use the suggested strategies as they are anchored on the best practices of the school leaders and are found to be predictors of high SBM performance.
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