This study investigated acquisition and transfer of the modeling ability of fifth graders in various domains. Teaching interventions concentrated on the topic of marine ecosystems either through a modeling-based approach or a worksheet-based approach. A quasi-experimental (pre-post comparison study) design was used. The control group (n = 17) received a traditional worksheet-based instruction about ecosystems, whereas the experimental group (n = 16) received an instruction which was based on Stagecast Creator, an object-oriented programming tool, and a set of modeling-based curriculum materials. Paper-and-pencil tests were used both before and after the study to evaluate students' development of specific modeling skills. The data analysis followed both qualitative and quantitative methods. The findings of the present study indicate that (i) the development of modeling ability was effectively enhanced through the modeling-based approach, since, after instruction, students were able to transfer those aspects to unfamiliar contexts; in contrast, the more traditional worksheet-based approach did not promote the development of the same aspects of the modeling skill; and (ii) Stagecast Creator enabled students to construct, test, revise and validate dynamic computer-based models of a marine ecosystem through building, testing and debugging complex rules, routines and programs for simulating multiple behaviours and processes of marine species.
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.
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