| 教师技术采纳:以教学制品质量为依据 |
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| 引用本文： | 郑隆威,阮佳慧,冯园园,顾小清.教师技术采纳:以教学制品质量为依据[J].开放教育研究,2022,28(1):93-106. |
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| 作者姓名： | 郑隆威 阮佳慧 冯园园 顾小清 |
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| 作者单位： | 华东师范大学上海智能教育研究院,上海200062;华东师范大学教育学部教育信息技术学系,上海200062;华东师范大学外语学院教育技术中心,上海200241 |
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| 基金项目： | 2020年度国家自然科学基金青年项目“基于教师制品的教师技术采纳评价与归因研究”(62007008);2020年度人力资源与社会保障部博士后基金面上项目“基于数字化教学制品的教师信息素养画像研究”(2020M681214);2021年度人力资源与社会保障部博士后基金站特别资助项目“基于教学制品异质网络的教师信息素养评价与干预研究”(2021T140210)。 |
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| 摘 要： | 教师技术采纳是教育领域长期的研究热点,已形成了相关理论和研究范式,但存在数据采集依赖自我报告、量化方法脱离教学情境等问题。为了突破上述难点,本研究引入“教学制品”概念,通过检测制品质量以追踪生产者的知识和技能,从而将教师技术采纳的观测转化为数字化教学制品各项指标的动态演化。本研究利用一系列深度学习方法标注和量化制品质量特征,从技术运用、内容设计、教学设计等层面表征及量化教学制品质量,洞悉教师对技术理解的演变。研究发现,有关制品质量的证据对教师技术采纳具有更强的解释力,尤其是当教师接纳了新技术后,制品质量更能体现教师对技术理解的演变。分析结果表明,随着对技术理解的深入,教师在技术运用的灵活度、数字媒体学习设计的丰富度、师生会话中教学策略的掌控等方面都有所提升,这些结果可为教师技术采纳研究提供新的数据采集思路与量化评价方向。
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| 关 键 词： | 教学制品 教师技术采纳 制品质量 技术采纳量化 深度学习 |
Using Teaching Artifacts to Analyze Teachers' Technology Adoption |
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| ZHENG Longwei,RUAN Jiahui,FENG Yuanyuan,GU Xiaoqing.Using Teaching Artifacts to Analyze Teachers' Technology Adoption[J].Open Education Research,2022,28(1):93-106. |
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| Authors: | ZHENG Longwei RUAN Jiahui FENG Yuanyuan GU Xiaoqing |
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| Institution: | (Institute of AI Education SH, East China Normal University, Shanghai 200062, China;Department of Education Information Technology, Faculty of Education, East China Normal University, Shanghai200062, China;Center of Educational Technology, School of Foreign Languages, East China Normal University, Shanghai 200241, China) |
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| Abstract: | Teacher technology adoption is a long-term research issue in the education field.Existing work has formed a variety of research paradigms ranging from theory to analytical methods,but there are problems such as data collection relying on self-reported data and quantitative methods out of the teaching context.In order to break through the existing difficulties,this article introduces the concept of"teaching artifact",which can track the knowledge and skills of the creator by detecting the quality of products,thereby transforming the observations adopted by teachers into the evolutionary dynamics of the performance of various indicators in digital teaching artifacts.This article uses a series of deep learning methods to label and quantify artifact quality to gain insights into the evolution of teachers’technology understanding.This study found that the evidence surrounding artifact quality has a stronger explanatory power for teachers’adoption of technology,especially when teachers accept new technologies;artifact quality can better reflect the evolution of teachers’technology understanding.From the results of artifact analysis,it can be found that with the improvement of technology understanding,teachers have improved in the flexibility of technology use,digital media learning design,and the control of teaching strategies in teacher-student conversations.These results provide new approaches to data collection and quantitative evaluation. |
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| Keywords: | teaching artifact teachers’technology adoption analysis of artifact quality measuring of technology adoption deep learning |
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