Studying Synergistic Learning of Physics and Computational Thinking in a Learning by Modeling Environment

被引:0
|
作者
Hutchins, Nicole [1 ]
Biswas, Gautam [1 ]
Conlin, Luke [2 ]
Emara, Mona [3 ]
Grover, Shuchi [4 ]
Basu, Satabdi [5 ]
McElhaney, Kevin [5 ]
机构
[1] Vanderbilt Univ, 221 Kirkland Hall, Nashville, TN 37235 USA
[2] Salem State Univ, Salem, MA USA
[3] Damanhour Univ, Damanhur, Egypt
[4] Stanford Univ, Stanford, CA 94305 USA
[5] SRI Int, 333 Ravenswood Ave, Menlo Pk, CA 94025 USA
基金
美国国家科学基金会;
关键词
Learning-by-modeling; synergistic learning; DSML; collaborative learning; SCIENCE; METACOGNITION;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Synergistic learning of computational thinking (CT) and STEM has proven to effective in helping students develop better understanding of STEM topics, while simultaneously acquiring CT concepts and practices. With the ubiquity of computational devices and tools, advances in technology, and the globalization of product development, it is important for our students to not only develop multi-disciplinary skills acquired through such synergistic learning opportunities, but to also acquire key collaborative learning and problem- solving skills. In this paper, we describe the design and implementation of a collaborative learning-by-modeling environment developed for high school physics classrooms. We develop systematic rubrics and discuss the results of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices.
引用
下载
收藏
页码:153 / 162
页数:10
相关论文
共 50 条
  • [41] Problem Based Learning: A Facilitator of Computational Thinking
    Jonasen, Tanja Svarre
    Gram-Hansen, Sandra Burri
    PROCEEDINGS OF THE 18TH EUROPEAN CONFERENCE ON E-LEARNING (ECEL 2019), 2019, : 260 - 267
  • [42] Survey: Artificial Intelligence, Computational Thinking and Learning
    Nina Bonderup Dohn
    Yasmin Kafai
    Anders Mørch
    Marco Ragni
    KI - Künstliche Intelligenz, 2022, 36 : 5 - 16
  • [43] Analysis of Collaborative Learning in a Computational Thinking Class
    Chowdhury, Bushra
    Bart, Austin Cory
    Kafura, Dennis
    SIGCSE'18: PROCEEDINGS OF THE 49TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, 2018, : 143 - 148
  • [44] Learning Performance of Different Genders' Computational Thinking
    Hsu, Ting-Chia
    Chang, Ching
    Wong, Lung-Hsiang
    Aw, Guat Poh
    SUSTAINABILITY, 2022, 14 (24)
  • [45] Computational Thinking as an Emergent Learning Trajectory of Mathematics
    Niemela, Pia
    Partanen, Tiina
    Harsu, Maarit
    Leppanen, Leo
    Ihantola, Petri
    17TH KOLI CALLING INTERNATIONAL CONFERENCE ON COMPUTING EDUCATION RESEARCH (KOLI CALLING 2017), 2017, : 70 - 79
  • [46] Computational Thinking Trough Programming: A Learning Paradigm
    Basogain Olabe, Xabier
    Olabe Basogain, Miguel Angel
    Olabe Basogain, Juan Carlos
    RED-REVISTA DE EDUCACION A DISTANCIA, 2015, (46):
  • [47] Survey: Artificial Intelligence, Computational Thinking and Learning
    Dohn, Nina Bonderup
    Kafai, Yasmin
    Morch, Anders
    Ragni, Marco
    KUNSTLICHE INTELLIGENZ, 2022, 36 (01): : 5 - 16
  • [48] Computational Thinking for Self-Regulated Learning
    Pasterk, Stefan
    Benke, Gertraud
    PROCEEDINGS OF THE 2024 CONFERENCE INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, VOL 1, ITICSE 2024, 2024, : 640 - 645
  • [49] Learning environment and learning activities with chemistry and physics laboratory on the Web
    Jong, BS
    Lin, TW
    Wu, YL
    INTELLIGENT SYSTEMS, 2002, : 114 - 119
  • [50] Computational Thinking in the Danish High School: Learning Coding, Modeling, and Content Knowledge with NetLogo
    Musaeus, Line Have
    Musaeus, Peter
    SIGCSE '19: PROCEEDINGS OF THE 50TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, 2019, : 913 - 918