人工智能促进知识理解：以概念转变为目标的实证研究

doi:10.16382/j.cnki.1000-5560.2022.09.007

摘要/Abstract

摘要：

理解被广泛认为是教育的重要价值追求，“为理解而教，为理解而学”已然成为学界共识。知识理解是概念转变的基础，知识应用与创新的前提，是学习者高阶思维发展的关键，是深度学习的旨向。人工智能为学习者提供更多样的知识呈现方式与形态，提供更精准的学习分析，创设智能化的真实学习情境，为学习者的概念转变与知识理解创造了良好的条件。正是基于这样的背景，我们以概念转变为切入点，以上海方略教育研发的智能全息盒子为主要的智能仿真学习环境，开展了一项实证研究，旨在探究智能仿真学习环境对学习者概念转变的影响，由此窥察人工智能促进知识理解的诸多可能。研究结果表明，人工智能所建构的智能仿真学习环境，对于学习者概念转变具有积极的促进作用。

关键词: 人工智能, 知识理解, 概念转变

Abstract:

Understanding is widely regarded as an important value pursuit in education. “Teaching for understanding and learning for understanding” has become a consensus. Knowledge understanding is the basis of concept transformation, the premise of knowledge application and innovation, the key to the development of learners’ higher-order thinking, and the aim of deep learning. At present, when human education is transforming to intelligent education, artificial intelligence can enhance personalized learning, enrich the presentation form of knowledge, support man-machine collaborative learning, and create real learning situations, which brings many possibilities for promoting learners’ knowledge understanding. Based on this background, the authors conduct an empirical study which aims to explore the influence of artificial intelligence learning environment on learners’ conceptual change. The results show that the intelligent learning environment has a positive promoting effect on learners’ conceptual change.

Key words: aritficial intelligence, knowledge understanding, conceptual change

杜华, 顾小清. 人工智能促进知识理解：以概念转变为目标的实证研究[J]. 华东师范大学学报(教育科学版), 2022, 40(9): 67-77.

Hua Du, Xiaoqing Gu. Artificial Intelligence for Knowledge Understanding: An Empirical Study Aimed at Conceptual Change[J]. Journal of East China Normal University(Educational Sciences), 2022, 40(9): 67-77.

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绘图描述	编码	分值
综合使用线条、色块、文字注释等三种元素，正确显示原子结构立体图	1	10
使用线条、色块、文字注释等元素中的任意两种，部分正确显示原子结构立体图	2	8
仅正确使用了线条，用于显示原子结构立体图	3	6
仅正确使用了部分线条，用于显示原子结构立体图	4	4
仅正确使用了色块，用于显示原子结构立体图	5	2
仅正确使用了部分色块，用于显示原子结构立体图	6	1
绘图模棱两可、错误或根本没有作答	7	0

组别	测验类型	样本容量	均值	标准差	标准误
实验组	前测	20	65.93	6.243	0.53
实验组	后测	20	77.85	3.235	0.41
对照组	前测	20	64.93	8.119	0.42
对照组	后测	20	69.85	7.146	0.69

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