技术赋能的课堂教学诊断:特征与发展空间  

Classroom Diagnosis Empowered by Technology:Characteristics and Development Space

作　　者：闫寒冰[1] 赵佳斌 王巍 YAN Hanbing;ZHAO Jiabin;WANG Wei(East China Normal University,Shanghai 200062)

机构地区：[1]华东师范大学开放教育学院(上海教师发展学院),上海200062 [2]华东师范大学教育信息技术学系,上海200062

出　　处：《现代远距离教育》2022年第2期3-11,共9页Modern Distance Education

基　　金：国家社会科学“十三五”规划2019年度教育学一般课题“面向工作胜任力的教师培训精准测评体系研究”(编号:BCA190083)。

摘　　要：人工智能、大数据等技术的快速发展为课堂教学诊断带来了新的助力。技术可以在哪些方面为课堂教学诊断提供支持?当前技术支持的课堂教学诊断具有哪些特征和发展空间?为了回答上述问题,本研究通过文献筛选出41个典型的技术支持的课堂教学诊断案例,对其从诊断范式、诊断层次、数据采集、数据处理和数据呈现五个维度进行描述剖析,并提出技术赋能的教学诊断模型(TTDM)。研究发现,现有案例存在诊断范式切片化、数据采集多模态、编码分析自动化、采集证据客观化和呈现形式可视化等特征。结合案例现状和新兴技术分析发现,课堂教学诊断在标准制定、结果验证、人机协同、功能诊断、无干扰采集、可操作见解、实时反馈和个性推荐等方面具有较大的发展空间。The rapid development of artificial intelligence, big data, and other technologies has brought new boosts to classroom teaching diagnosis. In what aspects can technology provide support for classroom teaching diagnosis? Which characteristics of current technology-supported classroom teaching diagnosis are there, and what are the development possibilities? To answer these questions, this study selects 41 typical cases of technology-supported classroom teaching diagnosis through the literature. The five dimensions of diagnostic paradigm, diagnostic level, data collection, data processing, and result presentation are described and analyzed, and the technology-enabled teaching diagnosis model(TTDM) is proposed. The study found that diagnostic paradigm slicing, data collection multi-modality, coding analysis automation, collected evidence objectification, and presentation form visualization are the characteristics of existing cases. Based on the current situation of cases and emerging technologies, we propose the development space in standard-setting, result verification, human-machine collaboration, functional diagnosis, interference-free acquisition, actionable insights, real-time feedback, and personalized recommendation in classroom teaching diagnosis.

关 键 词：课堂教学诊断 诊断技术 诊断标准 案例分析 

分 类 号：G434[文化科学—教育学]