Examining how students’ knowledge of the subject domain affects their process of modeling in a computer programming environment

The purpose of this study was to investigate whether learners with different science content knowledge backgrounds, namely physics and science education graduates, construct models differently in the same computer programming environment with graphically represented program language and for the same...

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Veröffentlicht in:Journal of computers in education (the official journal of the Global Chinese Society for Computers in Education) Jg. 2; H. 3; S. 251 - 282
Hauptverfasser: Papaevripidou, Marios, Zacharia, Zacharias C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2015
Springer Nature B.V
Schlagworte:
Animation
Collisions
Computer programming
Computer Uses in Education
Computers and Education
Constructivism (Learning)
Data analysis
Data collection
Education
Educational Technology
Graduate students
Graphical representations
Knowledge
Language
Metacognition
Modelling
Physics
Programming environments
Programming Languages
Qualitative analysis
Science education
Science Instruction
Scientific Enterprise
Scientists
Written Language
Modeling-based learning
Computer programming environment
Modeling competence
Science background knowledge
ISSN:2197-9987, 2197-9995
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Zusammenfassung:The purpose of this study was to investigate whether learners with different science content knowledge backgrounds, namely physics and science education graduates, construct models differently in the same computer programming environment with graphically represented program language and for the same subject matter (1D collisions). To do so, we selected 28 participants for each group and offered them the same modeling-based learning treatment. Data collection involved the administration of two paper-and-pencil tests, the participants’ created models, and screen-capture data (both video and sound). The first test examined the participants’ content knowledge on 1D collisions and the second one participants’ modeling competence. The data analysis involved both qualitative and quantitative methods. The findings revealed that variation in science background knowledge appears to affect the learners’ modeling competence, the types and nature of the models created, and the model creation progression followed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:2197-9987
2197-9995
DOI:10.1007/s40692-015-0034-1