本研究之研究目的為：(一) 編製國內國中生適用之認知好奇心量表，並分析其信、效度；(二) 進行認知好奇心量表潛在—狀態特質分析；(三) 考驗本研究建構之好奇心歷程模式與觀察資料是否適配。根據前述研究目的，本研究共抽取三批樣本。第一批樣本 ( N = 165 )用於分析認知好奇心量表的信、效度；第二批樣本 ( N = 265 )用於進行認知好奇心量表潛在—狀態特質分析；第三批樣本 ( N= 495 )則用於各項理論模式的檢驗。第一批樣本接受單次認知好奇心量表的施測，並進行探索性因素分析及信度分析；第二批樣本進行三次認知好奇心量表的施測，並進行潛在狀態—特質分析；第三批樣本則依序測量認知好奇心、學業情緒及學習涉入量表，進行認知好奇心量表的驗證性因素分析，並以結構方程模式（SEM）分析本研究建構之認知好奇心歷程模式一及模式二模式與觀察資料的適配度。

本研究的結果與發現如下：

一. 本研究自編之認知好奇心量表信、效度均佳，且其二階驗證性因素分析模式與觀察資料適配，顯示本量表具良好的建構效度。

二. 潛在—狀態特質分析的結果顯示：興趣型及匱乏型特質好奇心與單一特質—多狀態（STMS）模式最為適配，並兩者的特質成分比率皆明顯高於狀態，表示以本研究編製的興趣型好奇心分量表與匱乏型特質好奇心分量表的測量構念雖然兼具狀態與特質的成分，但確實偏向穩定的特質。狀態好奇心則與多狀態（MS）模式最為適配，表示以本研究編製的狀態好奇心分量表的測量構念確實屬於狀態成分。此結果亦顯示本量表具良好的建構效度。

三. 本研究建構之認知好奇心歷程模式一與觀察資料適配，分析結果顯示：
興趣型特質好奇心能正向預測正向學業情緒及學習涉入，且能透過正向學業情緒對學習涉入產生正向預測效果；狀態好奇心能正向預測學習涉入；正向學業情緒能正向預測學習涉入。而認知好奇心歷程模式二亦與觀察資料適配，分析結果顯示：興趣型特質好奇心能負向預測負向學業情緒並正向預測學習涉入，且能透過負向學業情緒對學習涉入產生正向預測效果；匱乏型特質好奇心能正向預測負向學業情緒，並透過負向學業情緒對學習涉入產生負向預測效果；狀態好奇心能負向預測負向學業情緒並正向預測學習涉入，且能透過負向學業情緒對學習涉入產生正向預測效果；負向學業情緒能負向預測學習涉入。

本研究根據研究結果，針對國中生的教學輔導及未來研究提供建議。

This study tried to develop an inventory that combined two dimensions of curiosity and investigate the relations between curiosity, academic emotion, and learning engagement in students’ learning process. The purpose of this study was to (a)develop the Epistemic Curiosity Scale and to examine the reliability and validity of it, (b) analyze the Epistemic Curiosity Scale by using Latent State-Trait Analysis, (c)examine the goodness of fit between the epistemic curiosity process model proposed by the author and the observed data. To attain these goals above, we collect three groups of samples. The first group was used to test the reliability and validity of the Epistemic Curiosity Scale, including 165 students. The second group was used to do Latent State-Trait Analysis, including 265 students. And the third group was used to examine the model fit of the epistemic curiosity process model, including 495 students.

The results of this study were summarized as follows: (a) The Epistemic Curiosity Scale developed in this study had good reliability and validity. (b) Interest-trait epistemic curiosity and deprivation-trait epistemic curiosity were the best fit with the single trait multiple state (STMS) model. And state epistemic curiosity was the best fit with the multiple state (MS) model. (c) In this study, the epistemic curiosity process model was separated into two sub-model according to the valence of academic emotion. One of them was the positive emotion model, and the other was the negative emotion model. Both of them fitted well and could be used to explain the observed data.

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