Characterization of Deeper Learning: The Development and Application of Evaluation Tools: Based on Mathematics Learning of Primary School Students in Four Cities

doi:10.16382/j.cnki.1000-5560.2021.11.004

Abstract

Abstract:

Deeper learning quality includes individual learners, groups and deeper learning system. On the one hand, it is highly recognized by theoretical research; on the other hand, the difficulty of evaluation hinders in-depth research and classroom practice. Schema and SOLO theory provide a psychological basis for deeper learning evaluation, while the thinking visualization technology provides evaluation tools, so the learning thinking visualization system is designed and developed, including learning strategy software, cognitive structure software and PDA products. Using the developed evaluation system, the study diagnosed the math learning of more than 4,000 primary school students in S (east China), C (northeast China), W (central China) and K (southwest China). Through the analysis of a large amount of data, three conclusions are drawn: first, learning strategy and cognitive structure can be used as the effect quantity of deeper learning quality; second, the visualization system of learning thinking is effective in evaluating deeper learning; third, deeper learning classroom should be improved from the following three aspects: (1) learning content, we should pay attention to the integration of subject content, learning strategy and cognitive structure; (2) learning mode, we should focus on the operation mode and the structure of personalized learning and cooperative learning; (3) thinking quality, we should pay attention to the construction and generation of complex schema. The development and application of deeper learning quality evaluation tools will effectively improve the operability of deeper learning implementation.

Key words: quality of deeper learning, evaluation tool, development, application

CLC Number:

G434

Hang Hu, Xue Mi, Yaxin Li, Qi’e Lang. Characterization of Deeper Learning: The Development and Application of Evaluation Tools: Based on Mathematics Learning of Primary School Students in Four Cities[J]. Journal of East China Normal University(Educational Sciences), 2021, 39(11): 73-85.

Figures/Tables 14

References 0

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一级指标	二级指标	三级指标
元认知策略	计划、评价反思等	1. 我会根据数学内容的难易程度安排合适的复习计划
		5. 除了老师布置的作业外，我有自己的数学学习计划
		9. 当一天结束时，我会想象今天数学完成了什么或者明天要做什么
认知策略	基本认知策略等	2. 上数学课时，我注意听老师强调的内容
		6. 我常常用自己的语言来理解数学课本上的内容
		10. 我常常向自己提问以检验自己的数学学习效果
寻求支持策略	他助策略等	4. 我经常与同学互相推荐或者交换阅读数学学习资料
		12. 我经常向老师请教数学问题
		20. 我经常和同学一起讨论有关数学学习的问题

SOLO层次	概念内涵	结构形式
前结构	学习者参与到学习任务中，但被学习情境中的无关知识信息所迷惑或误导，对问题的反应无任何意义	无
单一结构	学习者只关注与问题解决相关的一个知识信息
多元结构	学习者使用多个孤立的知识信息来解决问题，但缺乏有机整合能力，没有建立知识信息之间的联系
关联结构	学习者整合对所有相关知识信息的理解，建立所有知识信息之间的联系，形成一致的知识结构或意义，来解决较为复杂的具体问题
抽象拓展结构	学习者在关联的基础上，对问题进行更全面的思考，以概括出更抽象的特征，生成一般性的假设并应用到新情境中，拓展问题本身的意义

	元认知策略			认知策略					寻求支持策略		均值
	计划策略	监控调节策略	评价反思策略	基本认知策略	概念学习策略	计算学习策略	解题策略	几何知识学习策略	他助策略	自助策略	均值
M	3.46	3.82	3.63	3.48	3.46	3.53	3.60	3.55	3.16	2.75	3.38
SD	0.994	1.053	0.866	1.098	1.176	0.945	0.965	1.024	0.997	1.100	0.672

		元认知策略	认知策略	寻求支持策略
元认知策略	Pearson相关性	—	0.850^**	0.543^**
元认知策略	显著性（双侧）	—	0.000	0.000
认知策略	Pearson相关性	0.850^**	—	0.613^**
认知策略	显著性（双侧）	0.000	—	0.000
寻求支持策略	Pearson相关性	0.543^**	0.613^**	—
寻求支持策略	显著性（双侧）	0.000	0.000	—

差异来源	SS	df	MS	F	Sig.
地区	3.606	3	1.202	2.750	0.042^**
年级	2.512	2	1.256	2.874	0.057
性别	4.113	1	4.113	9.411	0.002^**
地区 * 年级	0.395	2	0.197	0.451	0.637
地区 * 性别	1.559	3	0.520	1.189	0.313
年级 * 性别	0.034	2	0.017	0.039	0.962
地区 * 年级 * 性别	0.256	2	0.128	0.293	0.746