Studying the Interactions Between Science, Engineering, and Computational Thinking in a Learning-by-Modeling Environment

被引:11
|
作者
Zhang, Ningyu [1 ]
Biswas, Gautam [1 ]
McElhaney, Kevin W. [2 ]
Basu, Satabdi [3 ]
McBride, Elizabeth [3 ]
Chiu, Jennifer L. [4 ]
机构
[1] Vanderbilt Univ, Inst Software Integrated Syst, Dept EECS, 1025 16th Ave South, Nashville, TN 37212 USA
[2] Digital Promise, Learning Sci Res, San Mateo, CA USA
[3] SRI Int, Ctr Educ Res & Innovat, Menlo Pk, CA USA
[4] Univ Virginia, Dept Curriculum Instruct & Special Educ, Charlottesville, VA USA
来源
ARTIFICIAL INTELLIGENCE IN EDUCATION (AIED 2020), PT I | 2020年 / 12163卷
基金
美国国家科学基金会;
关键词
Science and engineering; Computational modeling; Log analysis; Regression methods; SCHOOL SCIENCE; PATH-ANALYSIS; CONSTRUCTION; MIDDLE; SPACE;
D O I
10.1007/978-3-030-52237-7_48
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Computational Thinking (CT) can play a central role in fostering students' integrated learning of science and engineering. We adopt this framework to design and develop the Water Runoff Challenge (WRC) curriculum for lower middle school students in the USA. This paper presents (1) the WRC curriculum implemented in an integrated computational modeling and engineering design environment and (2) formative and summative assessments used to evaluate learner's science, engineering, and CT skills as they progress through the curriculum. We derived a series of performance measures associated with student learning from system log data and the assessments. By applying Path Analysis we found significant relations between measures of science, engineering, and CT learning, indicating that they are mutually supportive of learning across these disciplines.
引用
收藏
页码:598 / 609
页数:12
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