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數學學習氣質評量分類規準效度議題之探討

The Validity Study for the Categorization Criterion of Mathematics Learning Dispositions

摘要


本研究旨在發展數學學習氣質評量的分類規準,並初步檢視其信、效度資訊。研究中將數學學習氣質構念,界定為學生正式與非正式數學學習表現和感受的特徵組型。本研究數學學習氣質量化指標包括三部分:數學學習情意評量,包含學習的自主(autonomy)、興味(playfulness)和挫折恢復(resilience)等三層面;非正式數學學習評量,是指學生在數學學習行動傾向電腦化評量的表現;正式數學學習評量,是指在校數學段考表現。研究中以結構方程模式(Structural Equation Modeling, SEM)進行數學學習行動傾向構念模式的驗證分析,結果顯示該構念模式與實徵資料的整體適配度理想。本研究同時探討氣質構念恆等性議題,研究中融合專家知識與統計資訊,採循環修訂歷程發展氣質分類規準。該規準試用在國小五年級213位學生之數學學習氣質分類,呈現相當合理的結構恆等性和分類一致性。跨作業氣質類型歸類一致性達75%,Kappa為0.67;相對於集群分析之統計分類,本研究所建構的分類規準在分類的邏輯以及類型的意義上,呈現類型意涵清晰簡明、利於溝通的優勢。

並列摘要


The main purpose of this study is to develop the categorization criterion for Mathematics Learning Dispositions, and to discuss its reliability and validity issues. The mathematical learning dispositions is operationally defined as the different profiles on mathematical learning affection, informal mathematical learning, and formal mathematical learning. There were three quantitative indicators of mathematics dispositions: (1) the computerized assessment of mathematics learning affection (autonomy, playfulness and resilience); (2) the computerized assessment of informal mathematics learning (mathematics game) actions, and (3) the scores of school mathematics achievement. The Structural Equation Modeling is applied to examine the model fitness. The results suggested that the construct model proposed for mathematical learning dispositions matches the collected data. The categorization criterion, revolved by expert judgement and statistical information feedback, demonstrated a good model constancy, too. The agreement of categorization on the learning disposition by different tasks is 75%, and the Kappa is 0.67. Comparing to the cluster analysis, the categorization criterion proposed by this study demonstrated the better interpretability of the categorization results.

參考文獻


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