學生應用視覺化建模與程式工具進行計算建模之分析;An Analysis of Computational Modeling in Science with Visual Modeling and Programming Tool

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Network Learning Technology > Electronic Thesis & Dissertation > Item 987654321/85078

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/85078

Title:	學生應用視覺化建模與程式工具進行計算建模之分析;An Analysis of Computational Modeling in Science with Visual Modeling and Programming Tool
Authors:	賴柏翰;Lai, Po-Han
Contributors:	網路學習科技研究所
Keywords:	計算思維;建造理論;綜效學習;電腦模擬;計算建模;滯後序列分析;computational thinking;constructionism;synergistic learning;computer simulation;computational modeling;lag sequential analysis
Date:	2021-01-18
Issue Date:	2021-03-18 17:35:42 (UTC+8)
Publisher:	國立中央大學
Abstract:	計算思維為現代所有人都應具備的基礎能力，而計算建模能夠幫助學生學習科學與計算思維相關的概念知識。因此，本研究基於建造理論，設計一個具備低門檻與高天花板且使用視覺化程式設計介面的科學計算建模系統與計算思維引導之課程活動，蒐集了28名國三學生於建模活動中所建立的科學計算模型與建模行為，使用質性與量化分析，並加入行為序列分析方式，探討科學計算建模對於學生科學與計算思維的影響，以及分析建模行為和學習成效與建模表現的相關性。研究結果顯示，學生是否能夠建立出計算模型的關鍵步驟為發展問題相關的表徵內容，這一步驟亦影響建模過程中出錯的頻率。此外，學生能否建立較為完整的計算模型則與對於隨時間變化之動態系統的描述、數學與工程概念的轉換、與對於事件識別能力有關。最後，本研究亦發現解構能力較差的學生會傾向依賴參考案例來完成建模。另外，本研究根據研究結果提出未來研究中能改進的部分，像是科學與計算思維試題都應重新設計、教師於課程中可以先帶領學生系統性地分析問題並依照學生程度給予鷹架輔助以及系統上可以加入更多防止錯誤機制與錯誤回復機制。;Computational thinking is one of the contemporary skills that all modern people should possess, and computational modeling can help students learn scientific and computational thinking concepts. One aim of this study was to understand the effect of scientific computational modeling activities on students’ scientific and computational thinking concepts. The other aim of this study was to clarify the relations between modeling behaviors, learning outcomes and modeling performance. In this study, we designed a scientific computational modeling system with the feature of low-threshold and high-ceiling effect based on constructionism. The system provided a visual-based programming interface along with learning activities guided by computational thinking. A number of twenty-eight 9th graders participated in the learning activity. Students’ scientific computational models and their modeling behaviors during the learning activities were collected. The quantitative analysis, qualitative analysis, and behavior sequence analysis were conducted to answer research questions. The result showed that developing abstract features for the questions is the key step to successfully build models. This step also affects the frequency of errors in the modeling process. Furthermore, students’ abilities to develop more comprehensive computational models were correlated with the abilities to describe a time-varying dynamic system, the conversion between mathematics and engineering concepts, and the abilities to recognize event patterns. Finally, the result also showed that students with poor decomposition abilities tended to rely on references to create the model. Suggestions were provided for future studies. First, both science and computational thinking assessments should be redesigned. Second, teachers can lead students to analyze problems systematically and provide scaffoldings for students with different levels in class. Third, more error prevention and correction mechanisms should be integrated into the modeling system.
Appears in Collections:	[Graduate Institute of Network Learning Technology] Electronic Thesis & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	112	View/Open

社群 sharing

Loading...