摘要
為了因應強調培養學生推理與問題解決能力的教育改革，現今的評量工具應讓教師得以有效且穩定地測得學生的這些能力，進而設計出幫助學生培養上述能力的課程活動。誠然，科技的進步已幫助教師測量與評估許多過去無法測得的學生能力，諸如數學能力、科學概念，甚或是學生於每一個學習環節所使用的解題策略。但是，值得注意的是，不論是學校的科技資源，用於設計與執行評量的教學時數，以及教師如何執行評量，評量本身與實際教學的一致性都有可能影響科技融入評量能否在學校中順利實施。而不論是教師如何執行評量與評估評量的成效或是教師對學校資源的覺察，又都與教師信念有關。
因此本系列研究透過3個子研究力求探索台灣科學教師對於科技融入評量的信念成分為何，驗證其信念與實務之間的關係，並評估教師對於科技融入評量的信念與實務如何調節學生的學習成效。在本系列研究的執行過程中，教師信念的相關文獻與理論如：technology acceptance model (TAM) 與decomposed theory of planned behavior (DTPB) 則構成了貫串全文的編碼架構與理論模型。
首先，在子研究1當中，藉由40位專家教師的晤談與編碼分析，我找出了10項主要的科技融入評量的信念成分及其特徵。此外，受訪的教師亦依據其實際使用科技融入評量的情形而被分為經常使用者、偶爾使用者、與未使用者三類。交叉比對這些老師的信念與實務則發現到，不同使用程度使用者所持有的信念亦有所不同。例如，隨著使用頻率的提高，教師所持的正向信念亦會越高；然而負面信念卻是由偶爾使用者拔得頭籌。
接著，為了進一步檢驗子研究1所發現的10項信念成分是否真的是組成教師科技融入評量信念的成分，我將子研究1的10項信念成分改寫為子研究2的信念問卷，並邀請494台灣的高中科學教師填寫，並藉由因素分析的結果確認此分問卷具備足夠的信效度來測量教師的科技融入評量信念。最後藉由結構方程式分析 (structural equation modeling) 所提供的路徑分析，我發現了除了從未使用者之外，其餘教師的有效性信念、方便性信念、相容性信念都會顯著且正向地影響其對科技評量的態度，而態度又能決定其使用科技評量的傾向。
在此系列的尾聲，為了評估教師對於科技融入評量的信念與實務如何調節學生的學習成效，子研究3採用2階層的階層線性模式的分析 (hierarchical linear modeling)，除了從學生個體層次探討其課堂參與、電腦使用經驗如何影響其學習成效，亦由學校層次探討屬於該校特色的教師科技評量使用時數、教師科技評量使用傾向、學校平均電腦使用經驗如何影響學生學習成效，更呈現學校層次的變數如何調節學生層次變數與其學習成效的關係。結果顯示，學生層次變數均對其學習成效有顯著正向影響，學校層級變數則對其學習成效有調節作用卻無直接影響。

Abstract
This series of studies aimed at not only investigating the components and features of teachers’ beliefs about technology-based assessments (TBAs), but also revealing the relationship between teachers’ beliefs about TBAs and their assessment practices and estimating the effects of teachers’ practice and beliefs about TBAs on students’ performances.
On the basis of these purposes, three studies that combined the qualitative data analysis, a structural equation modeling (SEM) analysis that combines both confirmatory factor analysis (CFA) and path analysis, and the two-level hierarchical linear modeling (HLM) were conducted. In the study 1, the main components and features of teachers’ beliefs and practice about TBAs were investigated and analyzed based on the qualitative data from 40 technology-experienced science teachers. In the study 2, I developed a specific questionnaire from the result of the study 1 to elicit another 494 high school science teachers’ beliefs about TBAs and conducted a confirmatory factor analysis to ensure unobserved beliefs could be identified by the questionnaire to take the study 1 a step further. Besides, the study 2 specified the possible relationships among teachers’ beliefs about, attitudes toward, and intention to use TBAs by conducting a SEM analysis. Finally, a HLM analysis was conducted to estimate the effects of teachers’ practice and beliefs about TBAs on students’ performances. The same 494 science teachers in the study 2 and their 1,774 students from eighth and 11th grades from 32 secondary schools participated in the study 3.
Based on the definitions of teachers’ beliefs systems and the literature review, this series of studies adapted both the elements and hierarchical structure of the technology acceptance model (TAM) and decomposed theory of planned behavior (DTPB) to form the coding scheme of the study 1 to explore the substantial components of science teachers’ TBAs beliefs and portraying the features of science teachers’ TBAs beliefs as comprehensive as possible. And then I modified TAM and DTPB based on the result of the study1 to form a prosed model to be examined in the study 2. Finally, on the basis of the study 2, I conducted a hierarchical linear modeling analysis to investigate the relationships among teachers’ beliefs about TBAs, their usage of TBAs, and their students’ performances on a TBA.
The analysis of qualitative data in the study 1 showed that 10 components were substantial in the behavioral, control, and normative beliefs. While 34 teachers perceived TBAs as useful tools and identified a variety of usefulness, nearly 40% of the participants indicated the difficulties in using TBAs and their beliefs of ease of use were mainly negative. Also, teachers’ control beliefs about TBA focused on the external components such as time, supporting personnel, and infrastructure rather than the personal factors. In their normative beliefs, teachers tended to view school policies and parents’ opinions as constraints, whereas they also realized the benefits of using TBAs for learning. Furthermore, based on their usage of TBAs, teachers were identified and characterized as three groups: frequent, occasional, and non-users. Although some frequent users did not teach in resource-rich schools and faced constraints similar to those encountered by the occasional users, they seemed to actively look for more supports and solutions to overcome the lack of resources and the disapproval from the school administration.
To take the study 1 a step further, the questionnaire in the study 2 was developed from the result of the study 1 to elicit the teachers’ beliefs about TBAs and then I conducted a CFA to ensure unobserved beliefs could be identified by the questionnaire. The results of CFA showed that all of the items developed in the study 2 were validated as being adequate indicators for measuring teachers’ beliefs about TBAs. Furthermore, the results of SEM analysis suggested that with the exception of teachers who had never used TBAs previously, teachers’ beliefs about usefulness, ease of use, and compatibility were significant predictors of attitude, which could explain the intention of teachers to use TBAs. However, perceived behavior control and subjective norms beliefs did not influence teachers’ intention.
On the basis of the study 2, I utilized a HLM technique to investigate the relationships between science teachers’ beliefs about TBAs, their usage of TBAs, and their students’ performances on a TBA in the study 3. The results of the study 3 showed that there was significant variation between schools in terms of student performances. The results also showed that both of the variables at the student level, such as students’ inquiry‐related laboratory engagement and their PCs experience had significant positive effects on their learning performances. However, none of the variables at the school level, such as teachers’ TBA hours and teachers’ intentions to use TBA, had significant effects on students’ learning performances.
Finally, the relationship between students’ inquiry‐related laboratory engagement and their learning performances could be moderated by two different variables at the school level in different ways. In one way, Average PCs usage at the school level would positively moderate the relationship between students’ inquiry‐related laboratory engagement and their learning performances. In another way, the TBA hours at the school level would negatively moderate the relationship between students’ inquiry‐related laboratory engagement and their learning performances. The results highlight the role of teachers’ practice and its impact on students’ performances.

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