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教育心理學報 ScopusTSSCI

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篇名 知情意整合的國中生數學學習歷程模式之建構
卷期 35:3
並列篇名 The Establishment of the Cognitive-Affective-Volitional Integrated Model of Mathematical Learning Process for Junior High School Students
作者 藍雅慧張景媛
頁次 201-219
關鍵字 數學學習動機情感結構方程模式意志AffectionMathematical learningMotivationStructural equation modelSEMVolitionTSSCIScopus
出刊日期 200403

中文摘要

本研究採用結構方程模式中的模式產出取向建構知情意整合的國中生數學學習歷程模式(簡稱知情意模式)。研究者首先提出包含情感∕動機、意志控制、學習策略、學習表現等一個潛在變項之知情意整合的國中生數學學習歷程初始模式(簡稱初始模式),以國內國中二年級學生共447人為研究參與者,使用『數學學習情感∕動機量表』、『數學學習意志控制量表』、『數學學習策略量表』、『數學學習表現量表』等工作。蒐集得的資料先以PRELIS 2.50版統計套裝軟體進行多變項常態分配假設的考驗,再使用LISREL8.50版統計套裝軟體,以一般加權最小平方法進行模式的參數估計。結果發現,初始模式無法合理解釋國中生數學學習歷程,在統計上亦不合理,因此,本研究進一步進行模式的修改,並對修改後的模式進行參數估計。經評估的結果發現,修改後的知情意模式能有效地用來說明實徵資料。然而,此模式在產出的過程經過修改,未來應再以其他觀察資料進行該模式的檢驗,以探討其通則化的可能性。

英文摘要

The purpose of this research aims to construct the Cognitive-affective-volitional Integrated Model of Mathematical Learning Process for Junior High School Students (the Cognitive-affective-volitional Model, for sort) by using structural equation model in the approach of model generating. In the first place, the researcher proposed the initial model, which included the latent variables of affection/motivation, volition control, learning strategies, and learning performance. The research was applied to 447 second-graders in junior high school for the researcher to collect empirical data. The researcher first tested the acquired data on the hypothesis of multivariate normality distribution with PRELIS 2.50, and then estimated the parameters of the model by means of generally weighted least-squares (WLS) with LISREL 8.50. The result showed that the initial model could not give a good account of the mathematical learning process for junior high school students, and the statistic results did not seem reasonable, either. Therefore, the researcher revised the model, and evaluated the parameters of the revised model. After revision, the evaluation results showed that the Cognitive-affective-volitional Model could effectively explain the data. Though the process of model generating has been revised, the model however, should be tested with other observation data in the future to see if the model could further be generalized.

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