建構中小學教師數位教學能力成熟度模式之研究   免費試閱

The Study of Constructing a Capability Maturity Model for Digital Teaching for Elementary and Junior High School Teachers

本研究目的在於運用模糊德菲法與層級分析法，建構數位教學能力成熟度模式指標向度項目與架構。數位教學能力成熟度模式係以能力度及成熟度為基礎，以確保不同教學者教材品質的一致性及不同單元學習架構的穩定性，將有助於提升教學者製作數位教材的專業職能。依據資料分析顯示，數位教學能力成熟度模式權重值為：初始化（ .29）、管理（ .26）、定義（ .08）、可預測（ .20）、優化（ .17）。在初始化下之準則權重值為：數位教學的媒體應用（ .46）、數位教學概念與實施類型（ .28）、科技的操作與觀念（ .25）；在管理下之準則權重值為：教學與教材設計概念（ .41）、數位資訊融入教學環境之經營（ .29）、數位教學活動之管理（ .29）；在定義下之準則權重值為：學習環境與經驗（ .43）、數位課程的教與學（ .38）、數位教學能力標準化（ .19）；在可預測下之準則權重值為：學生成效評估（ .38）、專業實務精進（ .32）、教學實施與評估（ .30）；在優化下之準則權重值為：數位科技與教學之應用性（ .56）、數位教學能力之典範（ .26）、數位科技前瞻性（ .18）。最後，研究者依據各層級及其流程領域之內涵，提出具體之對應實施目標，以供未來實務教學之應用。

This study incorporated the fuzzy Delphi method and analytic hierarch process to construct the indices and frameworks of a digital teaching capability maturity model (D-TCMM). The D-TCMM was based on capability levels and maturity levels (MLs), and aimed to ensure consistency among teachers in their teaching material quality and stability in the learning structures of various course units. Overall, the model can improve the professional competencies of teachers creating teaching materials. According to the data analysis results, the weights of the MLs were .29 for initial, .26 for managed, .08 for defined, .20 for predictable, and .17 for optimizing. At the initial level, the weights of the process areas (PAs) were .46 for the application of digital teaching media, .28 for the concepts and implementation of digital teaching, and .25 for the concepts and application of technology. At the managed level, the weights of the PAs were .41 for the design concepts of teaching processes and materials, .29 for integrating digital information in a teaching environment, and .29 for digital teaching activity management. At the defined level, the PA weights were .43 for learning environment and experience, .38 for teaching and learning in digital courses, and .19 for standardizing digital teaching capabilities. At the predictable level, the PA weights were .38 for student learning evaluation, .32 for the sophistication of professional practices, and .30 for implementation and evaluation of teaching method. Finally, the weights of the PAs at the optimizing level were .56 for the applicability of digital technology and teaching, .26 for the potentials of digital technology, and .18 for the paradigms of digital teaching capabilities. According to the implications of each ML and PA, specific implementation goals were subsequently proposed for future teaching practices.