| 人工智能时代下的精准减负：提升减负政策效能的关键——基于小学生学习投入与主观课业负担类型的划分及特征分析 |
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| 引用本文： | 张生,张平,曹榕,程姝,方丹.人工智能时代下的精准减负：提升减负政策效能的关键——基于小学生学习投入与主观课业负担类型的划分及特征分析[J].中国电化教育,2020(1):114-121. |
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| 作者姓名： | 张生 张平 曹榕 程姝 方丹 |
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| 作者单位： | 北京师范大学中国基础教育质量监测协同创新中心;北京市海淀区教育科学研究院 |
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| 基金项目： | 全国教育科学“十二五”规划2015年度教育部重点课题“基于学业负担评价的学校教学管理改进研究”(课题编号：DHA150277)研究成果 |
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| 摘 要： | 不同类型学生群体的个性特点与统一政策相冲突是减负政策实施的阻力之一,厘清投入和负担之间的非线性关系是科学减负的基础。研究采用聚类分析等方法探索了学生在校内外学习投入与主观课业负担的潜在分类,并从个体的学业成就、学习品质及其人际关系等角度探索了各类学生的发展特征。研究发现:(1)学生的负担情况不能一概而论,有的需要减负、有的需要增加时间投入、有的需要提升学习品质,学生可根据学习投入与主观课业负担的状况分为"低投高负型""低投低负型""高投高负型""高投低负型"等四种类型。(2)不同类型的学生无论是在学业成就、人际关系还是在影响学生终生发展的学习品质上的表现均存在显著差异。(3)减轻负担的核心在于提升学生的学习品质等非学业方面的素养,无论学习投入程度如何,学习品质表现好的学生的负担感受均较低。因此,人工智能时代下建立对学生的负担状况进行科学分类的精确诊断系统是必要的,不同类型的学生应采取个性化的减负方案,利用智能化的自适应学习系统提升学生的学习品质是减负的核心路径。
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| 关 键 词： | 人工智能时代 学习投入 精准减负 负担类型 |
Reducing Learning Burden Accurately: the Key to Improve the Effectiveness of the Policy——Based on the Classification and Characteristic Analysis of Primary School Students’ Learning Engagement and Subjective Schoolwork Burden |
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| Zhang Sheng,Zhang Ping,Cao Rong,Cheng Shu,Fang Dan.Reducing Learning Burden Accurately: the Key to Improve the Effectiveness of the Policy——Based on the Classification and Characteristic Analysis of Primary School Students’ Learning Engagement and Subjective Schoolwork Burden[J].China Educational Technology,2020(1):114-121. |
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| Authors: | Zhang Sheng Zhang Ping Cao Rong Cheng Shu Fang Dan |
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| Institution: | (Beijing normal university,Collaborative Inn ovation Center of Assessment for Basic Education Quality,Beijing 100875;Haidian Institute of Education Sciences,Beijing 100080) |
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| Abstract: | The conflict between the individualized learning needs of different types of students and the unified policy is one of the obstacles to the implementation of the policy of burden reduction.The present study explored the potential classification of students’learning input and subjective schoolwork load inside and outside the school,and the development characteristics of various types of students were described in detail from the perspectives of individual academic achievements,learning quality and interpersonal relationship through clustering analysis and other methods.The research found that:(1)The situation of students can not be generalized,someone need to reduce the learning burden,someone need to increase time investment,some need to improve the quality of learning,they were divided into four categories by learning burden:"low engagement high burden""high engagement high burden""high engagement low burden""high engagement low burden"and"low engagement low burden".(2)Four clusters differed significantly in academic achievements,interpersonal relationship and learning quality.(3)The findings accurately classified students with different types of primary school students in order to provide effective strategies of diagnosing and reducing learning burden for different types of students to enhance the overall efficiency of the education.The core of learning burden reduction lies in improving students’learning quality.When the level of investment is different,students with good learning quality feel lower burden.Therefore,it is necessary to establish an accurate diagnosis system for scientific classification of students’burden in the era of artificial intelligence.Students of different types should adopt personalized programs to reduce students’learning burden,and using intelligent adaptive learning system to improve students’learning quality is the core path to reduce students’learning burden. |
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| Keywords: | The Age of Artificial Intelligence Learning Engagement Reducing Learning Burden Accurately Types of Learning Burden |
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