資訊科技融入國小六年級圓形圖單元 教學與評量之行動研究

An Action Research on Integrating Information Technology into the Teaching and Evaluation of the Circular Diagram Unit for the Sixth Graders

洪祥堯(Hung HSIANG YAO)

指導教授 ： 施淑娟 郭伯臣

亞洲大學/資訊學院/資訊工程學系碩士在職專班/碩士(2010年)

摘要

摘 要
本研究是以九年一貫能力指標6-d-01(圓形圖)相關內容為研究領域，研發編製以知識結構為基礎的教學教材及補救教材，探討教材應用後的成效。研究者以台中縣某國小四個班級為教學實驗對象，其中兩班為實驗組，採用研究者自編教學教材進行教學，另外兩班以現行課本教材進行教學，教學實驗後以BNAT系統進行電腦化適性診斷前測，驗證自編教材成效，然後根據診斷出錯誤類型，實驗組以研究者自編補救教材施行補救教學，控制組以現行教材進行補救教學，再用BNAT系統做後測，同樣檢驗自編補救教材之成效。此外並探討以貝氏網路為推論工具，應用於診斷學生子技能與錯誤類型有無之預測精準度，及BNAT適性診斷測驗能節省之題數、預測精準度；最後配合填寫學習風格問卷，分析不同學習風格型態學生在學習成效上之差異。
研究結果如下：
一、使用研究者自編指導教材進行教學後，實驗組前測分數為76.30分，控制組為72.87分，經檢定達顯著差異，顯示自編指導教材具有學習成效。
二、實驗組前測平均分數是76.30分，經過補救教學後，後測平均分數是83.83分，經檢定達顯著差異，表示研究者自編之補救教材，的確能幫助學生之學習。
三、電腦適性診斷測驗(BNAT)系統，能有效節省34％以上之題目，且能達到96％以上的預測精準度。
四、電腦適性診斷測驗(BNAT)系統以貝氏網路進行推論，在適性和完整作答情況下，對錯誤類型與子技能的一致性，前測達97.78％，後測達98.37％。
五、學生不同學習風格會對學業成就造成一定程度的影響。

關鍵字

圓形圖、電腦化適性診斷測驗、知識結構、貝氏網路、補救教學、學習風格、BNAT

並列摘要

Abstract
By taking the competence indicator 6-d-01 of the circular diagram unit and its related contents of the Nine-Year Integrated Curriculum as the domain of the study, the purpose of the research is not only to develop the teaching materials for mathematical and remedial instructions based on the knowledge structure theories but also to explore the teaching effects of these materials.
In this study, the experimental instructions conducted by the researcher are proceeded with the four classes of the sixth grade in an elementary school of Taichung County. As the subject of the research, students of the sixth grade are divided into two groups, half of them who receive instructions with teaching materials designed by the researcher are classified as the experimental group, and those who receive instructions with textbooks are classified as the control group.
After the implementation of experimental teachings, the Bayesian Network based Computerized Adaptive Test (BNAT) is carried on as a pretest to testify the efficiency of the self-designed teaching materials. With the error patterns analyzed from the pretest, the researcher offered the remedial instructions for students in the experimental group with the self-designed teaching materials and students in the control group with current teaching materials. After that, the computerized adaptive diagnostic test based on the Bayesian network is provided again as the posttest to explore the learning results of the self-designed teaching materials used in the remedial instructions.
Furthermore, by means of the Bayesian Network, the accuracy rate of prediction for diagnosing students’ mistaken types and sub-skills are discussed in this study. And the rate of reducing test items and the forecast precisions of the Bayesian Network based Computerized Adaptive Test (BNAT) are estimated as well. Finally, through the learning style questionnaires, the differences in learning results due to students’ various learning styles are analyzed.
The findings of this study are listed bellowed:
1. By using the self-designed teaching materials developed by the researcher for the remedial instructions, it is found that the average score of the experimental group in the pretest is higher than the average score of the control group (76.30>72.87). The remarkable difference after the examination shows that the self-designed teaching materials are effective for students’ learning.
2. After taking the remedial instructions, students of the experimental group have made great progress with the posttest. The average score of the experimental group in the posttest is 83.83. Comparing with the pretest, the notable difference attained by the examination shows that the remedial teaching materials designed by the researcher are truly helpful for students’ learning.
3. It is estimated that 34% of questions can be saved effectively by using the Bayesian Network based Computerized Adaptive Test (BNAT)and the accuracy rate of predication is up to 96%.
4. By means of the Bayesian Network, the uniformity of the mistaken types and sub-skills deduced from the Bayesian Network based Computerized Adaptive Test (BNAT) system is calculated. When the test is done to the full and well adapted, the uniformity rate of the mistaken types and sub-skills can reach 97.78% in the pretest and 98.37% in the posttest.
5.It is found that different learning styles can have certain influences on students’ learning achievements.

並列關鍵字

circular diagram ； the computerized adaptive diagnostictest ； knowledge structure theories ； Bayesian network ； remedial instructions ； learning style ； Bayesian Network based Computerized Adaptive Test (BNAT)

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