研究生: |
陳鴻任 Chen, Hong-Ren |
---|---|
論文名稱: |
國中數理資優生才能發展因子與自我調節學習在生物表現之關聯探討 Investigation of the Relationship between Talent Development Factors and Self-Regulated Learning in Biological Performance among Junior High School Mathematically and Scientifically Gifted Students |
指導教授: |
于曉平
Yu, Hsiao-Ping |
口試委員: |
于曉平
Yu, Hsiao-Ping 潘裕豐 Pang, Yu-Fong 陳振明 Chen, Chen-Ming |
口試日期: | 2024/05/21 |
學位類別: |
碩士 Master |
系所名稱: |
特殊教育學系 Department of Special Education |
論文出版年: | 2024 |
畢業學年度: | 112 |
語文別: | 中文 |
論文頁數: | 189 |
中文關鍵詞: | 資優學生 、才能發展因子 、自我調節學習 、生物表現 、結構方程模型 |
英文關鍵詞: | gifted students, talent development factors, self-regulated learning, biological performance, structural equation modeling |
研究方法: | 調查研究 |
DOI URL: | http://doi.org/10.6345/NTNU202401631 |
論文種類: | 學術論文 |
相關次數: | 點閱:38 下載:1 |
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自然學科的才能發展需要學生長期的澆灌與培養,因此才能發展因子與自我調節學習被認為是為重要的因素。本研究旨在探討國民中學數理學術性向一年級資優生在才能發展因子、自我調節學習與生物表現之間的現況與相互關係。主要目的為發展「國民中學生物才能發展因子量表」與「國民中學生物自我調節學習量表」,分析不同背景變項下的國民中學數理學術性向資優生生物表現才能發展量表中各分量表之現況,然後分別檢測「國民中學生物才能發展因子量表」與「國民中學生物自我調節學習量表」的測驗工具品質;最後,建立才能發展因子、自我調節學習與生物表現之中介模型。
本研究為量表調查法,研究工具為自編量表,正式研究對象則是全國共分成北中南東地區,共369人,調查結果將經由探索性因素分析、描述性統計與結構模型進行驗證,最後發現如下:
一、「國民中學生物才能發展因子量表」與「國民中學生物自我調節學習量表」具有高信效度。
二、相較於一般生,資優生在才能發展因子、自我調節學習與生物表現之表現良好。
三、資優生身分別、對生物有興趣、小學曾是資優生和得過科學相關競賽獎項之學生在才能發展因子、自我調節學習與生物表現中具有顯著差異。
四、才能發展因子、自我調節學習與生物表現之中介模型具有良好的可靠度、可信度與解釋力。
最後根據上述研究結果,提供學校與家長了解資優學生獨特的才能發展因子與自我調節學習策略,為資優生創造生物學習全面性的才能發展。
關鍵詞:資優學生、才能發展因子、自我調節學習、生物表現、結構方程模型
The development of talents in natural sciences requires long-term nurturing and cultivation for students. Therefore, talent development factors and self-regulated learning are considered important elements. This study aims to explore the current status and interrelationships among talent development factors, self-regulated learning, and biological performance in first-year academically gifted junior high school students with a mathematical and scientific aptitude. The main objectives are to develop the "Junior High School Biological Talent Development Factor Scale" and the "Junior High School Biological Self-Regulated Learning Scale," to analyze the current status of each subscale in the Biological Talent Development Scale among mathematically and scientifically gifted students with different backgrounds, and to assess the quality of the "Junior High School Biological Talent Development Factor Scale" and the "Junior High School Biological Self-Regulated Learning Scale" as testing tools. Finally, the study aims to establish a mediating model of talent development factors, self-regulated learning, and biological performance.
This study adopts a survey method using self-developed scales. The formal study participants are divided into northern, central, southern, and eastern regions of the country, with a total of 369 participants. The survey results will be validated through exploratory factor analysis, descriptive statistics, and structural equation modeling. The conclusions drawn are as follows:
1.The "Junior High School Biological Talent Development Factor Scale" and the "Junior High School Biological Self-Regulated Learning Scale" exhibit high reliability and validity.
2.Compared to general students, gifted students perform well in talent development factors, self-regulated learning, and biological performance.
3.Students identified as gifted, interested in biology, formerly gifted in elementary school, and those who have won science-related competition awards show significant differences in talent development factors, self-regulated learning, and biological performance.
4.The mediating model of talent development factors, self-regulated learning, and biological performance demonstrates good reliability, credibility, and explanatory power.
Based on the above research results, recommendations are provided for schools and parents to understand the unique talent development factors and self-regulated learning strategies of gifted students, aiming to create comprehensive talent development in biological learning for gifted students.
Keywords: gifted students, talent development factors, self-regulated learning, biological performance, structural equation modeling
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