基于在线学习评估工科学生的系统思维和建模能力

IF 2.1 2区 工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES
Roee Peretz;Natali Levi-Soskin;Dov Dori;Yehudit Judy Dori
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引用次数: 0

摘要

贡献:基于模型的学习能根据学生的已有知识和性别改善系统思维(ST)。发现文本、视觉和混合问题类型与学生成绩之间存在关系。背景:系统思维对明智决策和解决问题至关重要。可以教本科生更好地运用系统思维,而对他们的在线系统建模过程进行分析则可以提高他们的系统思维能力。研究问题1) 在线学习对工科学生的 ST 和概念建模技能水平有何影响?2) 学生的 ST、概念建模以及文本、视觉和混合问题类型的得分之间是否存在差异?研究方法:研究对象为参加二年级必修课程的 157 名工科本科生,在此期间收集数据并进行定量分析。研究结果具有不同先验知识的学生在ST总平均分和各种问题类型的平均分上存在显著差异。此外,还发现了 ST 及其相对提高方面的性别差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Engineering Students’ Systems Thinking and Modeling Based on Their Online Learning
Contribution: Model-based learning improves systems thinking (ST) based on students’ prior knowledge and gender. Relations were found between textual, visual, and mixed question types and student achievements. Background: ST is essential to judicious decision-making and problem-solving. Undergraduate students can be taught to apply better ST, and analysis of their online systems modeling processes can improve their ST. Research Questions: 1) What is the effect, if any, of online learning on the ST and conceptual modeling skill levels of undergraduate engineering students? 2) What differences are there, if any, between students’ ST, conceptual modeling, and scores in textual, visual, and mixed question types based on their prior knowledge levels? and 3) Are there any gender differences in student performance, and if so, what are they? Methodology: The research participants were 157 undergraduate engineering students who took part in a mandatory second-year course, during which data were collected and analyzed quantitatively. Findings: Students with disparate prior knowledge differed significantly from each other in their overall ST mean score and in the mean scores of the various question types. Gender differences in ST and its relative improvement were also found.
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来源期刊
IEEE Transactions on Education
IEEE Transactions on Education 工程技术-工程:电子与电气
CiteScore
5.80
自引率
7.70%
发文量
90
审稿时长
1 months
期刊介绍: The IEEE Transactions on Education (ToE) publishes significant and original scholarly contributions to education in electrical and electronics engineering, computer engineering, computer science, and other fields within the scope of interest of IEEE. Contributions must address discovery, integration, and/or application of knowledge in education in these fields. Articles must support contributions and assertions with compelling evidence and provide explicit, transparent descriptions of the processes through which the evidence is collected, analyzed, and interpreted. While characteristics of compelling evidence cannot be described to address every conceivable situation, generally assessment of the work being reported must go beyond student self-report and attitudinal data.
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