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題名:同時估計解題策略、概念與錯誤類型之認知診斷模型探究
作者:陳俊華
作者(外文):CHEN,CHUN-HUA
校院名稱:國立臺中教育大學
系所名稱:教育資訊與測驗統計研究所
指導教授:郭伯臣
吳慧珉
學位類別:博士
出版日期:2016
主題關鍵詞:認知診斷模型多重解題策略概念錯誤類型最大期望值演算法cognitive diagnostic modelsmultiple strategiesconcepterror patternEM algorithm
原始連結:連回原系統網址new window
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診斷能提供訊息幫助教師瞭解學生學習狀況,使得教師能作更好的教學設計與有效率的補救教學,提升學生學習成效。近年來認知診斷模型 (cognitive diagnostic models, CDMs)受到重視,許多CDMs已被開發應用在診斷測驗上。目前CDMs可以分為單一解題策略與多重解題策略的模型,單一解題策略模型有估計概念、估計錯誤類型或同時估計兩者。多重解題策略模型有估計概念或錯誤類型,然而尚未有同時估計概念與錯誤類型的模型。因此本研究提出兩個新模型,第一個新模型為在多重解題策略下,同時估計概念與錯誤類型的MS-SISM (multiple-strategy simultaneously identifying skills and misconceptions)模型,第二個新模型為MS-SISM混合模型,是將MS-SISM模型延伸為能估計解題策略的模型。此外,為了估計新模型的參數,同時也提出最大期望值 (expectation-maximization, EM)參數估計方法。本研究首先透過模擬研究探究兩個新模型的參數估計成效;其次比較當MS-SISM混合模型估計解題策略時,其與MS-SISM模型在概念與錯誤類型的估計成效;接著比較兩個新模型與傳統多重解題策略模型在概念與錯誤類型的估計成效,最後透過實徴研究比較兩個新模型在實徵資料的分析成效。研究結果顯示:
一、 當樣本數增加、題數增加與試題品質提升時,兩個新模型試題參數的估計準確度,以及概念與錯誤類型的正確分類率均提升。
二、 兩個新模型同時估計的概念與錯誤類型正確分類率較傳統多重解題策略模型個別估計的概念或錯誤類型正確分類率為高。
三、 MS-SISM混合模型能有效估計解題策略,其估計的概念、錯誤類型之正確分類率和專家一致性,與MS-SISM模型的估計結果相近。
Diagnosis in instruction can provide sufficient diagnostic information that allow for effective measurement of the learning progress of students, and allow for better designing of remedial instruction to improve students’ learning. In recent years, cognitive diagnostic models (CDMs) have been paid more attentions and developed for diagnostic tests. Current develpoed CDMs can be classified into two categories-single-stratgy and multiple-strategy models. Single-strategy models are designed for identifying concepts/skills, error patterns/misconceptions, or both. Multiple-strategy models are designed for identifying concepts/skills or error patterns/misconceptions, but not both yet. This study aims to propose two new multiple-strategy models, the first model called the multiple-strategy simultaneously identifying skills and misconceptions (MS-SISM) model is for simultaneously identifying concepts/skills and error patterns/misconceptions; the second model called the mixture MS-SISM model, which can identify strategy and is an extension of the MS-SISM model. In addition, for estimating the parameters of the new models, the expectation-maximization (EM) algorithm is also proposed. Simulation studies were conducted to investiage the performance of estimating parameters for the new models, to compare the classification on concepts and error patterns by the new models when the mixture MS-SISM estimates strategy, and to compare the classification on concepts and error patterns by the new models and traditional models. An emperical study was also conducted to demonstrate the application of the new models. The results showed that:
1. The accuracy of item parameter estimates and the correct classification rates for concepts and error patterns were improved by increasing sample size, number of items, or the quality of items.
2. The correct classification rates for concepts and error patterns provided by the two new models were both better than those separately provided by traditional models.
3. The estimation for strategy by the mixture MS-SISM model was effective, and the correct classification rates and correct agreement rates for concepts and error patterns provided by the mixture MS-SISM model were similar to those provided by the MS-SISM model.
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