首页> 中文期刊> 《电化教育研究》 >高校混合教学就绪指数构建与评估应用

高校混合教学就绪指数构建与评估应用

         

摘要

混合教学是一个复杂的动态系统,如何将高校开展混合教学的能力表征为可量化、易理解的指标体系,以指导混合教学实践,具有重要意义.在借鉴国内外相关就绪指数研究成果基础上,构建了包含"准备、应用、影响"全周期的混合教学就绪指数概念模型;并从利益相关者视角对混合教学影响因素进行统计、取舍和归类,建立了"个体-机构-阶段"三维就绪指标识别框架;进而通过开放问卷、深度访谈等调查方法收集数据填补和丰富框架内容;最后采用Delphi-AHP法收敛和提炼指标并确定权重系数,得到5个维度包含ICT设备、网络接入、学习空间、资源与技术支持、战略目标、组织与规划、激励措施、政策与制度、在线课程建设、混合教学应用、学习支持服务、质量保障与评价、认知与意愿、教学能力、培训与发展、教学团队建设、教学改革、创新扩散等18个具体指标的就绪指数评价体系;以广东省5所高校为例开展了试验性评估应用,从数据采集、象限分析等方面展示了就绪指数的应用过程、可行性和有效性.%Blended teaching is a complex and dynamic system. It is of great significance to turn the competency of implementing blended teaching into a quantifiable and easy-to-understand index system to guide the practice of blended teaching in colleges and universities. Based on the research results of readiness index at home and abroad, a conceptual model of Blended Teaching Readiness Index (BTRI) including whole cycle of "preparation, application and influence" is constructed. And from the perspective of stakeholders, the influencing factors of blended teaching are statistically analyzed and a three-dimensional framework of "individual-organization-stage" for identifying BTRI is established. In addition, the framework is enriched by data collected through open questionnaires and in-depth interviews. Finally, the Delphi-AHP method is used to refine the index and determine the weight coefficient, and a final BTRI system including 5 dimensions and 18 indexes is constructed, including ICT equipment, network access, learning space, technical support, strategic objectives, organization and planning, incentives, policies and system, online curriculum development, blended teaching applications, learning support service, quality assurance and evaluation, cognition and willingness, teaching competency, training and development, teaching team building, teaching reform and innovation diffusion. An experimental application of BTRI was carried out in five universities in Guangdong Province. The application process, feasibility and effectiveness of readiness index were demonstrated from the aspects of data collection and quadrant analysis.

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