Much.Matter.in.Motion: learning by modeling systems in chemistry and physics with a universal programing platform

ABSTRACT

This paper presents the design and initial learning research with the MMM modeling platform, seeking to advance middle school students’ learning through constructing computational models of complex physical and chemical systems. A complexity-based structure of an MMM interface is introduced. It suggests that a complex system can be described and modeled by defining entities, their actions, interactions with each other, and interactions with their environment. MMM applies to a wide range of phenomena, targeting learning transfer and generalization. Design principles of MMM are presented and discussed based on a study with seventh-grade students. The study is a quasi-experimental, pretest-intervention-posttest control-comparison-group design. Findings from a quantitative analysis of the questionnaires show that engaging students with the construction of models using MMM significantly promoted students’ conceptual learning and enhanced their systems’ thinking compared with a comparison group who followed a normative curriculum. Students’ responses to the worksheets showed mutual effects between improving the practice of modeling and promoting conceptual understanding and systems thinking. A qualitative analysis of screen-capture movies of one pair of students and their log files revealed that, in a later construction activity, their constructed models grew in sophistication and they articulated their thinking and learning in depth, using more sophisticated relationships between concepts.

KEYWORDS:

• Complex Systems
• science education
• modeling tools
• computational models
• construction of models

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 CC1 is a learning environment constructed using NetLogo on the topic of gases in chemistry. The unit can be download here: http://ccl.northwestern.edu/curriculum/ConnectedChemistry/CC_GasLawsStudent.pdf

Additional information

Funding

This work was supported by the Israeli Science Foundation [grant number 1205\18]; and the Ministry of Science, Technology and Space, Israel [grant number 87166].

Notes on contributors

Janan Saba

Janan Saba is a PhD student at the Department of Learning, Teaching, and Instruction, in the Faculty of Education, University of Haifa. She has a BA in Mathematics and Computer Sciences and an MA in Mathematics Education.

Hagit Hel-Or

Prof Hagit Hel-Or is a faculty member in the Department of Computer Science at the University of Haifa, Israel. She has held visiting scholar positions in the Vision Group in the Department of Psychology and in the Department of Statistics both at Stanford University. Her research interests in the area of Image Processing and Computer Vision include Image Enhancement, Pattern Recognition, Color Vision, Imaging Technologies, and Computational and Human Vision. She is a member of the IEEE.

Sharona T. Levy

Dr Sharona T. Levy is a faculty member at the University of Haifa. She works with a wide span of age groups and abilities, and conducts research into people’s reasoning about systems they encounter in everyday life and about systems they construct and explore in the domain of science, technology, and health; and develop and study computer-based and physical learning environments, some of which are based upon embodied learning.