資訊融入教學與電腦化適性診斷測驗之學習成效－以「一元一次不等式」為例

Learning Effects of Information Technology Integrated into Instruction and Computerized Adaptive Diagnostic Test ─An Example of “Linear Inequalities in One Variable”

尤淑菁(Su Jing Yu)

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(2011年)

摘要

摘要
本研究以國中數學領域『一元一次不等式』單元為例，依據貝氏網路與單元知識結構來編製測驗題目及學習之數位教材，並於國中進行實驗以驗證其成效。教學實驗將受試者分成實驗組及對照組，實驗組以自編數位教材實施資訊融入教學及依電腦化適性診斷測驗之錯誤類型與迷思概念進行資訊融入補救教學；對照組以坊間教材實施傳統教學及考卷檢討方式進行補救教學，兩組在教學後進行電腦化適性診斷測驗，以測驗結果檢視兩組學習與補救教學成效，以及評估電腦化適性診斷測驗之效果。研究結果如下：
一、在學習回饋單分析中，有九成以上的學生認為資訊融入教學使學習更加生動及活潑、增加學習動機；有八成的學生認為電腦化診斷測驗是有幫助的。
二、資訊融入教學之實驗組教學成效明顯優於傳統教學之對照組，表示此套數位教材能提高學生學習成效。
三、數位教材之實驗組補救教學成效明顯優於傳統補救教學之對照組，表示此補救教材及補救教學模式較傳統補救教學更有益於學生學習。
四、不同教學模式下，實驗組之延後測平均略高於對照組，但並未達到顯著差異。
五、以貝氏網路為基礎之電腦化適性診斷測驗可以讓師生了解錯誤類型及迷思概念所在，進而實施適性補救教學。
六、「一元一次不等式」單元之最佳動態分類決斷值平均辨識率為88.73％，顯示此單元有不錯之貝氏網路推論準確度。
七、「一元一次不等式」單元之電腦適性測驗前測、後測及延後測省題率平均為30.63％，且能達到平均93.76％以上的預測精準度。
八、「一元一次不等式」單元在每次測驗之一致性平均為95.18％，顯示對錯誤類型、子技能及能力指標方面的電腦適性選題與全測診斷具有高一致性。

關鍵字：一元一次不等式、資訊融入教學、貝氏網路、電腦化適性診斷測驗、資訊融入補救教學

關鍵字

一元一次不等式 ； 資訊融入教學 ； 貝氏網路 ； 電腦化適性診斷測驗 ； 資訊融入補救教學

並列摘要

Abstract
The research takes the “Linear Inequalities in one variable” in mathematics field of junior high school, and designing the test items and digital materials of learning is based on Bayesian Networks and Unit Knowledge Structure, and experimented on junior high school students to prove its effects. In this teaching experiment, subjects were divided into the experimental group and the control group. Students in the experimental group were under the teaching of Information Technology Integrated into Instruction with self-compiled digital materials, and received Information Integrated Remedial Instruction according to the error patterns and misconception of Computerized Adaptive Diagnostic Test. Students in the control group were under traditional teaching with textbooks and received remedial instruction by test reviews. After teaching phase, students in these two groups took Computerized Adaptive Diagnostic Test. The results of the test are aimed to investigate the effects of learning and remedial teaching of the two groups, and estimate the effects of Computerized Adaptive Diagnostic Test. The research findings are as follows.
1. In learning feedback questionnaire analysis, over ninety percent of the students think Information Technology Integrated into Instruction makes their classes more lively, more active, and more learning motivation promoted. Eighty percent of the students think Computerized Adaptive Diagnostic Test is helpful.
2. The effect of the experimental group is significantly superior to that of the control group, showing this digital material can raise students’ learning effects.
3. The effect of the remedial instruction of the experimental group is significantly superior to that of the control group, showing this remedial material and remedial teaching mode are more helpful for students than traditional remedial instruction.
4. Under these two different teaching modes, the average of postponed test in the experimental group is a little higher than that in the control group, though not reaching significant difference.
5. Computerized Adaptive Diagnostic Test based on Bayesian Networks can make teachers and students understand learners’ error patterns and misconceptions so that adaptive remedial teaching can be carried out.
6. In the unit of “Linear Inequalities in one variable”, the average of the recognition rate of optimum threshold is 88.73% , revealing good inferential accuracy of Bayesian Networks.
7. In the unit of “Linear Inequalities in one variable”, the average of the question-saving ratio in the pre-test, post-test, and postponed-test of Computerized Adaptive Diagnostic Test is 30.63%., and the average of its predictive accuracy reached over 93.67%.
8. In the unit of “Linear Inequalities in one variable”, the average of the consistency of every test is 95.18%, revealing high consistency between Computerized Adaptive Diagnostic Test and whole-test diagnosis in the aspect of error patterns, sub-skills, and competence indicators.

Key words: Linear Inequalities in one variable, Information Technology Integrated into Instruction, Bayesian Networks, Computerized Adaptive Diagnostic Test, Information Integrated Remedial Teaching

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