摘要
坚毅力是学生综合素质中必备的优秀品质之一,也是我国未来人才培养的核心目标之一,对其开展测评具有重要的现实意义。然而,现有坚毅力测评理论框架的相对宽泛,以及传统主观测评方法的自我局限已不适宜多模态数据支持的测评趋向。为进一步解决这些问题,该研究结合扎根理论、Delphi法构建了包含坚毅力的行为性、情感性、认知性等3个一级指标,专注性、坚持性、积极情感、消极情感、目标意识、自我监控等6个二级指标的学生坚毅力测评理论模型,并以此为框架设计了面向科学探究活动场景的学生坚毅力测评的表现性评价工具。结合理论模型与测评工具,研究对学生坚毅力测评具体指标的数据表征进行了设计与说明。该研究将能够为未来基于多模态数据融合计算的学生坚毅力测评提供理论与工具支持。
Grit is one of students’essential qualities and one of the core objectives of future talent training in China,so it is important to assess it.However,the relatively broad theoretical framework of the existing grit assessment and the self-limitation of the traditional subjective assessment methods are no longer suitable for the trend of the assessment supported by multimodal data.To further address these issues,the study combined Grounded Theory and Delphi to construct a theoretical model for measuring students’grits that contains three primary indicators,including behavioral,emotional,and cognitive indicators,and six secondary indicators,including concentration,persistence,positive emotion,negative emotion,goal awareness,and self-monitoring.Furthermore,the study used the theoretical model as a framework to design a performance assessment tool for measuring students’grits in scientific inquiry activity scenarios.Combining the theoretical model and the assessment tool,the study designed and explained the data feature of the specific indicators of students’grits assessment.The study will be able to provide theoretical and instrumental support for students’grits assessment based on multimodal data fusion and computation in the future.
作者
郭利明
郑勤华
齐欣
Guo Liming;Zheng Qinhua;Qi Xin(The Research Center of Distance Education,Beijing Normal University,Beijing 100875;The Center of Exhibition and Education,China Science and Technology Museum,Beijing 100101)
出处
《中国电化教育》
北大核心
2023年第7期109-117,共9页
China Educational Technology
基金
国家自然科学基金面上项目“基于多模态数据融合计算的中小学生坚毅力测评技术与溯源研究(项目编号:62277004)”阶段性研究成果。
关键词
坚毅力
坚毅力测评
表现性评价
科学探究活动
数据指标
多模态数据
grits
grits assessment
performance assessment
scientific inquiry activities
data indicators
multimodal data