Artificial Intelligence for Knowledge Understanding: An Empirical Study Aimed at Conceptual Change

doi:10.16382/j.cnki.1000-5560.2022.09.007

Abstract

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

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.

Figures/Tables 5

References 50

1	陈家刚. (2013). 促进理解性学习的课程和教学设计原则. 全球教育展望, 42 (01), 53- 61.
2	陈明选, 刘径言. (2012). 教育信息化进程中教学设计的转型——基于理解的视角. 电化教育研究, 33 (08), 10- 16.
3	邓峰, 钱扬义. (2007). 国外几种科学概念转变教学模式简介与评析. 中学化学教学参考, (04), 61- 63.
4	杜华, 顾小清. (2020). 教育技术学理论五问——兼论教育技术学之于教育学理论建构的贡献. 教育研究, 41 (01), 148- 159.
5	顾小清. (2021). 当现实逼近想象: 人工智能时代预见未来教育研究. 开放教育研究, 27 (01), 4- 12.
6	郭炯, 郝建江. (2019). 人工智能环境下的学习发生机制. 现代远程教育研究, 31 (05), 32- 38. doi: 10.3969/j.issn.1009-5195.2019.05.004
7	胡卫平, 刘建伟. (2004). 概念转变模型: 理论基础、主要内容、发展与修正. 学科教育, (12), 34- 38.
8	牟智佳. (2017). “人工智能+”时代的个性化学习理论重思与开解. 远程教育杂志, 35 (03), 22- 30.
9	彭聪. (2015). 我国概念转变教学策略的研究综述. 教育导刊, (09), 54- 57. doi: 10.3969/j.issn.1005-3476.2015.09.013
10	乔纳森. (2008). 技术支持的思维建模: 用于概念转变的思维工具(顾小清译). 上海: 华东师范大学出版社.
11	舒杭. (2020). 学习分析技术支持的概念转变过程研究. 上海: 华东师范大学博士论文.
12	孙崇勇. (2012). 认知负荷的测量及其在多媒体学习中的应用. 苏州: 苏州大学博士论文.
13	孙艳超. (2016). 可视化工具支持的概念转变研究. 上海: 华东师范大学博士论文.
14	闫志明, 唐夏夏, 秦旋, 张飞, 段元美. (2017). 教育人工智能(EAI)的内涵、关键技术与应用趋势——美国《为人工智能的未来做好准备》和《国家人工智能研发战略规划》报告解析. 远程教育杂志, 35 (01), 26- 35.
15	袁维新. (2003). 概念转变学习的内在机制探析. 教育研究与实验, (02), 49- 54.
16	王燕. (2014). “理解性教学”的理念与实践. 上海教育科研, (02), 77- 79. doi: 10.3969/j.issn.1007-2020.2014.02.023
17	王珏, 解月光. (2017). 基于前概念体系的学习者认知诊断方法研究——以初中物理“力与运动”主题为例. 电化教育研究, 38 (9), 122- 128.
18	杨玉桓, 汪波, 雷颜萍, 李秀玉. (1988). 浅谈试卷的信度与效度. 天津教育, (11), 19- 20.
19	张良, 关素芳. (2021). 为理解而学: 人工智能时代的知识学习. 湖南师范大学教育科学学报, 20 (01), 55- 60.
20	张琼, 胡炳仙. (2016). 知识的情境性与情境化课程设计. 课程. 教材. 教法, 36 (06), 26- 32.
21	邹一娜, 周勇. (2011). 课堂探究学习活动中概念教学策略研究. 全球教育展望, 40 (02), 82- 87.
22	Brown, J. S., Collins, A., &Duguid, P. (1989). Situated Cognition and the Culture of Learning. Educational Researcher, 18 (1), 32- 42. doi: 10.3102/0013189X018001032
23	DiSessa A. A. (1982). Unlearning Aristotelian physics: A study of knowledge-based learning. Cognitive science, 6 (1), 37- 75. doi: 10.1207/s15516709cog0601_2
24	Foley, B. J. (1999). Visualization tool: Models, Representation and Knowledge Integration. Berkeley: University of California.
25	Gobert J. D, Clement J. J. (1999). Effects of student-generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36 (1), 39- 53. doi: 10.1002/(SICI)1098-2736(199901)36:1<39::AID-TEA4>3.0.CO;2-I
26	Halford G. S. (2014). Children's understanding: The development of mental models. Psychology Press.
27	Hart, S. G. , Staveland, L. E.. (1988). Development of NASA-TLX(Task Load Index): results of empirical and theoretical research. Advances in Psychology, 52 (6), 139- 183.
28	Hynd C. R. (2001). Refutational texts and the change process. International Journal of Educational Research, 35 (7-8), 699- 714. doi: 10.1016/S0883-0355(02)00010-1
29	Kaiser, M. K., Proffitt, D. R., Whelan, S. M., &Hecht, H. (1992). Influnce of animation on dynamical judgments. Journalof Experimental Psychology:Human Perception and Performance. 18(3), 669- 689.
30	Kendeou P., Walsh E. K., Smith E. R., et al. (2014). Knowledge revision processes in refutation texts. Discourse Processes, 51 (5-6), 374- 397. doi: 10.1080/0163853X.2014.913961
31	Larkin D. (2012). Using the conceptual change model of learning as an analytic tool in researching teacher preparation for student diversity. Teachers College Record, 114 (8), 1- 35.
32	Lewis, E. L. (1991). The process of scientific knowledge acquisition among middle school students learning thermdynamics. Berkeley: University of California.
33	Liu T. C. (2010). Developing Simulation-based Computer Assisted Learning to Correct Students' Statistical Misconceptions based on Cognitive Conflict Theory, using" Correlation" as an Example. Educational Technology & Society, 13 (2), 180- 192.
34	Michotte, A. (1963). The perception of causality. London: Methuen.
35	Mitchell I., Baird J. (1986). Teaching, learning and the curriculum 1: The influence of content in science. Research in Science Education, 16 (1), 141- 149. doi: 10.1007/BF02356828
36	Novak J. D., Gowin D. B., &Bob G. D. (1984). Learning how to learn. Cambridge: Cambridge University Press.
37	Paas, F. (1993). Instructional control of cognitive load in the training of complex cognitive tasks. Hague: University of Twente.
38	Potvin P., Sauriol É., &Riopel M. (2015). Experimental evidence of the superiority of the prevalence model of conceptual change over the classical models and repetition. Journal of Research in Science Teaching, 52 (8), 1082- 1108. doi: 10.1002/tea.21235
39	Prinz A., Golke S., &Wittwer J.,. (2018). Refutation texts compensate for detrimental effects of misconceptions on comprehension and metacomprehension accuracy and support transfer. Journal of Educational Psychology, 111 (6), 1- 87.
40	Scaife, M., Rogers, Y. (1996). External cognition: How do graphical representations work?. International Journal of Human-Compter Studies, 45 (2), 185- 213. doi: 10.1006/ijhc.1996.0048
41	Sellmann D., Liefländer A. K., &Bogner F. X. (2015). Concept maps in the classroom: A new approach to reveal students’ conceptual change. The Journal of Educational Research, 108 (3), 250- 257. doi: 10.1080/00220671.2014.896315
42	She H. C., Liao Y. W. (2010). Bridging scientific reasoning and conceptual change through adaptive web‐based learning. Journal of Research in Science Teaching, 47 (1), 91- 119.
43	Snir, J., Smith, C., &Grosslight, L. (1993). Conceptually ehanced simualtions: A computer tool for science teaching. Journal of Science and Technology, 2 (2), 373- 388.
44	Solomon S. H., Medaglia J. D., Thompson-Schill S. L.. (2019). Implementing a concept network model. Behavior research methods, 51 (4), 1717- 1736.
45	Ting C. Y., Sam Y. C., &Wong C. O. (2013). Model of conceptual change for INQPRO: A Bayesian Network approach. Computers & Education, 65, 77- 91.
46	Trundle K. C., Bell R. L. (2010). The use of a computer simulation to promote conceptual change: A quasi-experimental study. Computers & Education, 54 (4), 1078- 1088.
47	Tsang, P. S., Velazquez, V. L. (1996). Diagnosticity and multidimensional subjective workload ratings. Ergonomics, 39 (3), 358- 381. doi: 10.1080/00140139608964470
48	Tseng C. H., Tuan H. L., &Chin C. C. (2010). Investigating the influence of motivational factors on conceptual change in a digital learning context using the dual‐situated learning model. International Journal of Science Education, 32 (14), 1853- 1875. doi: 10.1080/09500690903219156
49	Wandersee J. H. (1990). Concept mapping and the cartography of cognition. Journal of research in science teaching, 27 (10), 923- 936. doi: 10.1002/tea.3660271002
50	Yin Y., Tomita M. K., &Shavelson R. J. (2014). Using formal embedded formative assessments aligned with a short-term learning progression to promote conceptual change and achievement in science. International Journal of Science Education, 36 (4), 531- 552. doi: 10.1080/09500693.2013.787556

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