- 期刊
基于SOLO分类理论的思维发展表现性评价研究
Research on Performance Assessment of Thinking Development Based on the Structure of the Observed Learning Outcome Theory (SOLO)
摘要
指向思维发展的表现性评价将评价融合于思维发展的综合实践活动之中，通过学生完成建构性任务过程中的表现来刻画其思维发展水平。以SOLO分类理论为依托，将思维能力运用映射为建构性问题解决过程，并提供目标－任务－标准三个关键环节的思维发展表现性评价框架，基于此，以面向小学生计算思维的表现性评价为案例，探讨面向思维发展的表现性评价设计要素与环节。研究的主要贡献之处在于将SOLO分类理论用于刻画思维运用的连续认知过程、分解表现性评价任务以及设定评价标准，建立思维发展的表现性评价设计框架，并通过实证研究论证了评价框架的可行性。未来研究可以结合新课标提倡的学科综合实践活动应用场景，探索学科思维发展与思维表现性评价融合的具体路径；借助语音与图像识别、机器学习等方法支持表现性行为数据的提取与处理分析，实现表现性评价与伴随式反馈相结合，从而发挥表现性评价的发展性价值。
並列摘要
The performance assessment of thinking development integrates the assessment into the comprehensive practical activities of thinking development, and portrays the level of thinking development through students' performance in the process of completing constructive tasks. Based on the theory of Structure of the Observed Learning Outcome(SOLO), this study maps the use of thinking skills to the process of constructive problem solving, provides a framework for the performance assessment of thinking development in the three key areas of goals-tasks-criteria, and finally discusses the elements and procedures of the design of performance assessment for thinking development by taking the performance assessment of computational thinking for primary school students as a case study. The main contribution of the study is that the Structure of the Observed Learning Outcome theory is used to portray the continuous cognitive process of thinking, decompose the performance tasks and set the assessment criteria, so as to establish a framework for performance assessment of thinking development, and the feasibility of the assessment framework has been demonstrated through the empirical study. The future study can explore the integration of thinking development and performance assessment of the disciplines with the application of the integrated practical activities of the disciplines advocated by the new curriculum standard, and support the extraction and processing and analysis of assessment behavioural data by means of voice and image recognition, machine learning, and other methods, so as to realize the combination of performance assessment and concomitant feedback, and thus bring into play the developmental value of performance assessment.