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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of computers in education (the official journal of the Global Chinese Society for Computers in Education) Ročník 2; číslo 3; s. 251 - 282
Hlavní autoři: Papaevripidou, Marios, Zacharia, Zacharias C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2015
Springer Nature B.V
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2197-9987
2197-9995
DOI:10.1007/s40692-015-0034-1