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題名:國中細胞分裂單元電腦適性學習系統建置與應用
作者:蔡顯麞
校院名稱:國立彰化師範大學
系所名稱:科學教育研究所
指導教授:郭重吉
張惠博
學位類別:博士
出版日期:2010
主題關鍵詞:細胞分裂貝氏網路電腦化適性診斷測驗建構反應題補救教學Cell divisionBayesian NetworksComputerized adaptive learningConstructed-Response ItemsRemedial teaching
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本研究嘗試以國中自然與生活科技「細胞分裂」單元的教學目標,進行一套完整的評量、診斷與數位化適性學習模式之研發,並評估其成效。因此,本研究運用結合知識結構與貝氏網路為基礎之電腦化適性診斷測驗,並配合建構反應題的使用,以進行學生的學習診斷與適性學習的成效評估,並將補救教材數位化,呈現活潑有趣的適性化學習內容,使學生獲得立即的學習回饋,由電腦可提供符合學生個別需求之學習媒體,針對學習不足的地方進行重新學習,藉以減輕教師閱卷、個別指導所造成的負擔,並幫助教師掌握學生的迷思概念及學習狀況。
本研究之研究結論分述如下:
一、以貝氏網路為推論工具的診斷系統,在「細胞分裂」單元的錯誤類型、子技能之整體辨識率為89.26%,診斷精確度已有不錯的水準。
二、紙筆測驗轉換成電腦化適性測驗後,能有效於前測節省25%、後測節省24%以上之題目,且能於前測達到95.75%、後測達到95.79%的預測精準度。
三、以自編「細胞分裂」單元教材,配合認知診斷之試題結構與教師經驗,進行課後輔導教學 (課後輔導教學組) 可以有效地降低學生錯誤類型出現的比例。
四、以自編「細胞分裂」單元教材,配合認知診斷之試題結構以建構多媒體適性學習系統 (多媒體適性學習) 可以有效地降低學生錯誤類型出現的比例。
五、進行多媒體適性學習對於降低學生錯誤類型出現率之成效優於實施課後輔導教學組。
六、實施課後輔導教學後,除高分組在前、後測成績無顯著差異外,其餘各組,以及各組併計後之全體,其後測成績均優於前測成績,且達顯著差異。
七、經過多媒體適性學習後,無論是高、中、低分組,或是全體學生併計,其後測成績均優於前測成績,且達顯著差異。
八、進行多媒體適性學習對於學生學習成效之提昇效果優於實施課後輔導教學,且達顯著差異。
九、學生在「細胞分裂」單元之建構反應題診斷測驗之錯誤類型較選擇題型之錯誤類型更詳盡、多元,且由診斷出的錯誤類型與其他學者之研究相互比較後發現本研究之建構反應題在偵測出學生之錯誤類型之成效卓著。
關鍵詞:細胞分裂、貝氏網路、電腦化適性診斷測驗、建構反應題、補救教學
Based on the goal of the unit of “Cell Division” in junior high school science education, the study has tried to develop a computerized adaptive learning courseware with comprehensive evaluation and diagnosis. A computerized adaptive testing system based on knowledge structure and Bayesian networks together with the development of brief constructed response items are used to diagnose learning and evaluate the effect of adaptive learning. The supplementary teaching materials are computerized to present contents that are lively and interesting for adaptive learning. The computer-based teaching materials allow students to obtain immediate feedback on learning. Computers also offer learning media that meets student’s needs, abilities and interests, and are capable of enhancing areas that require improvement. Computer-based teaching also reduces the time teachers spend on grading and tutoring and help teachers understand student misconceptions and how students are progressing and where they are having trouble.
Based on the findings of this study, the following conclusions are reached:
1.The diagnosis system that uses Bayesian Networks as an inference tool is capable of identifying 89.26% of error patterns and sub-skills in the Unit of “Cell Division”, giving very high accuracy of diagnosis.
2.Changing paper testing into computerized adaptive testing saves effectively examination questions by 25% for pretests and 24% for posttests. The forecast accuracy for pretests and posttests reaches as high as 95.75% and 95.79% respectively.
3.With reference to item structure and the help/guidance of teachers, after school enhancement programs using teaching materials collected, prepared and arranged by teachers for the unit of “Cell Division” can effectively reduce the frequency of occurrence of student error patterns.
4.The multimedia adaptive learning system developed with reference to item structure and based on teaching materials collected, prepared and arranged by teachers for the unit of “Cell Division” can effectively reduce the incidence of student error patterns.
5.The multimedia adaptive learning performs better in the reduction of the incidence of student error patterns than after school enhancement programs.
6.With after school enhancement programs, except high score group that exhibits no significant difference in either pretests or posttests, posttests of all the other groups surpasses pretests significantly in excellence of performance.
7.With the multimedia adaptive learning system, posttests of the high-, medium-, and low-score groups surpasses pretests significantly in excellence of performance, either calculated individually or together.
8.The multimedia adaptive learning outperforms significantly after school enhancement programs in the improvement of student learning.
9.In the unit of “Cell Division”, student error patterns occurring in Constructed-Response Items are more detailed and diversified than those in choice type questions. Comparison with other researches and studies indicates that constructed-response items developed by the study can effectively identify error patterns of students.
Keywords: cell division, Bayesian Networks, computerized adaptive learning, Constructed-Response Items, remedial teaching
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