IEEE Transactions on Education最新文献

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IEEE Transactions on Education Publication Information IEEE教育出版信息汇刊

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-12-02 DOI: 10.1109/TE.2025.3631811

引用次数: 0

IEEE Transactions on Education Information for Authors IEEE作者教育信息汇刊

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-12-02 DOI: 10.1109/TE.2025.3631813

引用次数: 0

Exploring the Influence of Visual Aids in Mathematical Problem Solving: An Eye-Tracking Study With Prospective Teachers 探讨视觉教具对数学解题的影响：对准教师的眼动追踪研究

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-11-07 DOI: 10.1109/TE.2025.3626625

Ismael García-Bayona;Adrián Pérez-Suay;Steven Van Vaerenbergh;Ana B. Pascual-Venteo

This study investigates the impact of images and visual schemes on students’ performance during mathematical problem solving (MPS). Forty primary education preservice teachers participated in this study and were divided into two groups. They were presented with a questionnaire consisting of 18 different mathematical problems, half of them accompanied by images or visual schemes, and the rest without any kind of visual aid. The group determined whether the participant would receive problems with visual aids before or after the ones without. Statistical tests were conducted to analyze the data, revealing that while overall performance did not significantly differ between the two groups, the presence of visual aids significantly improved performance in certain problem categories, such as percentages, reversal error problems, and fractions. Eye-tracking data were collected during problem solving, and the gaze patterns of 13 participants were analyzed, which shed light on students’ problem-solving strategies, including counting, multiplication, and the detection of the reversal error phenomenon. Additionally, this eye-tracking data were used to develop predictive models based on neural networks (NNs) to infer success or failure in MPS tasks.

本研究探讨了图像和视觉方案对学生数学解题成绩的影响。本研究以40名小学教育职前教师为研究对象，分为两组。研究人员向他们提交了一份由18个不同数学问题组成的问卷，其中一半附有图像或视觉方案，其余的没有任何视觉辅助。该小组决定参与者是否会在没有视觉辅助的之前或之后遇到问题。我们进行了统计测试来分析数据，结果显示，虽然两组学生的总体表现没有显著差异，但视觉辅助工具的存在显著提高了他们在某些问题类别（如百分比、反转错误问题和分数）上的表现。在解决问题过程中收集眼球追踪数据，分析13名参与者的注视模式，揭示学生解决问题的策略，包括计数、乘法和反转错误现象的检测。此外，这些眼球追踪数据被用于开发基于神经网络的预测模型，以推断MPS任务的成功或失败。

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引用次数: 0

Physical Computing and Computational Thinking Supported by Mobile Devices in an Introductory Electronics Course: An Active Learning Approach 电子导论课程中移动设备支持的物理计算和计算思维：一种主动学习方法

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-11-06 DOI: 10.1109/TE.2025.3626487

Jonathan Álvarez Ariza;Carola Hernández Hernández

Contribution: This study explores how physical computing (PhyC) activities supported by mobile devices can enhance learning, motivation, and computational thinking (CT) in engineering students. By adopting a learning-by-doing approach, tablets and smartphones were transformed into active learning devices for algorithm creation and experimentation through an educational app developed for block-based programming and hardware handling.

Background: Typically, learning through mobile learning (m-learning) devices have been adopted in e-learning settings, especially for content delivery. Conversely, this study utilizes mobile devices for active learning, enabling students to engage in programming and PhyC activities within an introductory engineering course.

Intended Outcomes: The methodology sought to enhance four areas for the students: academic performance, motivation, collaboration, and CT through mobile devices and PhyC activities. The 76 undergraduate engineering students participated in the methodology from 2022 to 2024.

Application Design: The methodology comprised active learning tasks developed by the students and aligned with the educational outcomes expected in the course. These tasks integrated handling of the app mentioned with hardware devices, i.e., sensors and basic robotics, along with the curriculum of an introductory electronics course. Data from 76 students were collected through academic grades, a questionnaire on a Likert scale, and semi-structured interviews. Data were analyzed utilizing a mixed research approach.

Findings: The educational outcomes suggest that the students improved their understanding of PhyC, programming, and electronics concepts in the course, with a large Wilcoxon effect size ( $r geq 0.5$ ) for most of the courses. At the qualitative level, five crucial components were identified in the m-learning intervention, namely, learning development, affective engagement, teacher presence, technology affordances, and PhyC interactivity and debugging, which influenced the students’ performance and engagement.

贡献：本研究探讨了移动设备支持的物理计算活动如何增强工程专业学生的学习、动机和计算思维。通过采用边做边学的方法，平板电脑和智能手机通过为基于块的编程和硬件处理开发的教育应用程序转变为主动学习设备，用于算法创建和实验。背景：通常，通过移动学习（m-learning）设备进行学习已被用于电子学习设置，特别是用于内容交付。相反，本研究利用移动设备进行主动学习，使学生能够在入门工程课程中参与编程和物理活动。预期结果：该方法旨在通过移动设备和体育活动提高学生的四个方面：学习成绩、动机、协作和CT。76名工科本科生从2022年到2024年参与了该方法。应用程序设计：该方法包括由学生开发的主动学习任务，并与课程预期的教育成果保持一致。这些任务整合了上述应用程序与硬件设备的处理，即传感器和基本机器人，以及入门电子课程的课程。通过学业成绩、李克特量表问卷和半结构化访谈收集了76名学生的数据。数据分析采用混合研究方法。研究结果：教育结果表明，学生在课程中提高了对物理、编程和电子学概念的理解，大多数课程具有较大的Wilcoxon效应量（$r geq 0.5$）。在定性层面上，我们确定了移动学习干预中的五个关键组成部分，即学习发展、情感参与、教师在场、技术支持、物理交互和调试，它们影响了学生的表现和参与。

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引用次数: 0

A Tale of Many IoTs: A Modular Constructivist Course Design for Internet of Things Education 多物联网的故事：面向物联网教育的模块化建构主义课程设计

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-11-06 DOI: 10.1109/TE.2025.3624281

Agustin Zuniga;Ngoc Thi Nguyen;Mika Tompuri;Henrik Nygren;Petteri Nurmi

Contribution: This study demonstrates that a modular constructivist approach to Internet of Things (IoT) education significantly enhances student engagement, knowledge retention, and practical application of concepts. The distinctive aspect of this approach lies in its adaptability to various teaching modalities, including traditional classrooms and massive open online courses (MOOCs), while effectively covering the comprehensive IoT ecosystem.

Background: The rapid proliferation of IoT technologies across various sectors has created a pressing demand for a skilled workforce adept in IoT principles. However, existing educational models often provide a limited perspective on IoT, underscoring the necessity for a holistic educational framework that can be applied across diverse educational programs.

Intended Outcomes: The primary outcomes of this approach include improved student knowledge and understanding of IoT concepts, increased engagement in the learning process, enhanced retention rates, and the ability to apply learned concepts in practical scenarios.

Application Design: The course employs a modular constructivist instructional approach, which allows for the integration of modern learning theories and constructivist principles. This design facilitates adaptability to various teaching modalities and encourages active learning through hands-on experiences in each module, covering critical aspects of the IoT ecosystem.

Findings: The findings show significant improvements in student knowledge, with self-assessment data showing increases between 41.5% and 89.6% across all topic areas. Performance metrics and qualitative feedback consistently indicate that the course effectively enhances understanding of IoT concepts, demonstrating its versatility and effectiveness in different learning environments.

贡献：本研究表明，物联网（IoT）教育的模块化建构主义方法显著提高了学生的参与度、知识保留和概念的实际应用。这种方法的独特之处在于它可以适应各种教学模式，包括传统课堂和大规模在线开放课程（MOOCs），同时有效覆盖全面的物联网生态系统。背景：物联网技术在各个领域的快速扩散，对熟练掌握物联网原理的劳动力产生了迫切的需求。然而，现有的教育模式往往对物联网提供了有限的视角，强调了一个可以应用于各种教育项目的整体教育框架的必要性。预期成果：该方法的主要成果包括提高学生对物联网概念的认识和理解，增加学习过程的参与度，提高保留率，以及将所学概念应用于实际场景的能力。应用设计：本课程采用模块化的建构主义教学方法，将现代学习理论与建构主义原则相结合。这种设计有利于适应各种教学模式，并通过每个模块的实践经验鼓励主动学习，涵盖物联网生态系统的关键方面。调查结果：调查结果显示，学生的知识有了显著的提高，自我评估数据显示，在所有主题领域，学生的知识增长了41.5%到89.6%。性能指标和定性反馈一致表明，该课程有效地提高了对物联网概念的理解，展示了其在不同学习环境中的多功能性和有效性。

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引用次数: 0

Understanding the Current Mentorship Capabilities of Teaching Assistants for Engineering Courses 对当前工科课程助教导师制能力的认识

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-10-13 DOI: 10.1109/TE.2025.3614155

Nathan G. Ewert;Javeed Kittur

Contribution: This article describes and interprets the quantitative results from a survey meant to evaluate the mentorship capabilities of engineering teaassistants. Background: teaching assistants (TAs) are a common facet at several higher learning institutions. In this position, they perform multiple duties to improve their students’ learning experiences, refining their own teaching abilities in the process. Given both their prevalence and impact, various researchers have been interested in studying these assistants’ capabilities and factors affecting their efficacy. Some of the most prominent lenses utilized for these investigations include self-efficacy, pedagogical behavior, and classroom structure. Research Questions: Building upon that previous literature, this article aims to answer the following questions: 1) How prepared is the current group of engineering TAs in the United States to facilitate improved student comprehension in class? 2) What potential avenues of growth can colleges in the United States follow to increase the overall efficacy of engineering TAs? Methodology: A survey with thirty-five 5-point Likert-based items was distributed to engineering program chairs at several universities across the United States. There was a total of 400 respondents for this survey once incomplete or insincere entries were excluded. Afterwards, exploratory factor analysis (EFA) was utilized to determine both the factorability of this data and its reliability. Regression analysis was also used to study the impact of certain factors across the survey’s scales. Findings: Three of the four initial questionnaire scales—self-efficacy, pedagogical practice, and content knowledge—emerged from EFA with high internal consistency reliability scores. Regression analysis found significance in student interaction, training, gender, and native language.

贡献：本文描述并解释了一项旨在评估工程助教指导能力的调查的定量结果。背景：助教（助教）在一些高等院校是一个常见的方面。在这个职位上，他们履行多种职责，以改善学生的学习体验，并在此过程中提高自己的教学能力。鉴于其普遍性和影响力，各种研究人员一直有兴趣研究这些助手的能力和影响其功效的因素。这些调查中使用的一些最突出的镜头包括自我效能感，教学行为和课堂结构。研究问题：基于先前的文献，本文旨在回答以下问题：1)美国目前的工程助教如何准备以促进学生在课堂上的理解？2)美国大学可以遵循哪些潜在的增长途径来提高工程助教的整体效率？方法：一份包含35个李克特5分制项目的调查被分发给了美国几所大学的工程项目主席。在排除不完整或不真实的条目后，本次调查共有400名受访者。然后，利用探索性因子分析（EFA）来确定该数据的因子性和可靠性。回归分析还用于研究某些因素在调查量表中的影响。结果：四个初始问卷量表中的三个——自我效能感、教学实践和内容知识——从EFA中获得了高内部一致性信度得分。回归分析发现学生互动、训练、性别和母语具有显著性。

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引用次数: 0

IEEE Transactions on Education Information for Authors IEEE作者教育信息汇刊

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-10-08 DOI: 10.1109/TE.2025.3615045

引用次数: 0

IEEE Transactions on Education Publication Information IEEE教育出版信息汇刊

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-10-08 DOI: 10.1109/TE.2025.3615043

引用次数: 0

An Experimental Study on the Association Between Affective States and Novice Programmers’ Performance 情感状态与新手程序员绩效关系的实验研究

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-10-01 DOI: 10.1109/TE.2025.3614598

Hemilis Joyse Barbosa Rocha;Evandro de Barros Costa;Bruno Almeida Pimentel

Problem-solving in programming requires not only cognitive but also affective engagement from students. Despite many novice programmers struggling with foundational programming concepts and problem-solving using these concepts, few studies explore how students’ emotional states impact their learning in introductory programming. This article investigates the association among affective states, programming concepts, and performance in novice programmers from a rural Brazilian school. Specifically, we explore how different affective states relate to success rates across core programming concepts. Unlike prior research often relying on IDE logs or broad academic indicators, this study employs a sensor-free, self-reported approach to assess students’ emotions in real time during programming tasks. Utilizing causal inference methods, we analyze both the direct and indirect effects of affective states on programming performance. Through two exploratory studies, our findings reveal that positive emotions such as enjoyment, motivation, and engagement are significantly associated with higher success rates, while negative states, like boredom, anxiety, and frustration, correlate with lower outcomes. These results enhance the understanding of the relationship among affective states, performance, and concepts in introductory programming tasks. Furthermore, this study offers valuable insights for improving educational practices and tools in programming education by emphasizing the critical role of addressing emotional dimensions in teaching and learning.

在编程中解决问题不仅需要学生的认知参与，还需要学生的情感参与。尽管许多新手程序员在基本的编程概念和使用这些概念解决问题方面苦苦挣扎，但很少有研究探讨学生的情绪状态如何影响他们在编程入门课程中的学习。本文调查了来自巴西农村学校的新手程序员的情感状态、编程概念和性能之间的关系。具体来说，我们探讨了不同的情感状态与核心编程概念的成功率之间的关系。与之前的研究通常依赖于IDE日志或广泛的学术指标不同，本研究采用了一种无传感器、自我报告的方法来评估学生在编程任务期间的实时情绪。利用因果推理方法，我们分析了情感状态对编程性能的直接和间接影响。通过两项探索性研究，我们的发现表明，积极的情绪，如享受、动力和投入，与更高的成功率显著相关，而消极的状态，如无聊、焦虑和沮丧，与较低的成功率相关。这些结果增强了对介绍性编程任务中情感状态、性能和概念之间关系的理解。此外，本研究通过强调在教学和学习中处理情感维度的关键作用，为改进编程教育中的教育实践和工具提供了有价值的见解。

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The Impact of Security Mindset on the Use of AI Assistants in Computing Education 安全思维对计算机教育中人工智能助手使用的影响

IF 2 2区工程技术 Q2 EDUCATION, SCIENTIFIC DISCIPLINES

IEEE Transactions on Education

Pub Date : 2025-09-26 DOI: 10.1109/TE.2025.3610340

Jiawei Yuan;Yanyan Li

The recent advances in large-language models (LLMs) have started shifting the way computing-related students write codes. LLM-based AI assistants, such as ChatGPT and Copilot, are now increasingly adopted to produce functional code by computing-related students. Although studies have shown that these AI assistants can improve coding efficiency, they also raise security challenges, especially when users lack a security mindset. Given the fact that AI assistants are increasingly integrated into computing education, this article performed an empirical study to explore the impact of a security mindset on the use of AI assistants for computing-related students in their coding and development. Our three-stage study showed that a significant portion of computing-related students currently lack security awareness toward the use of AI assistants. In addition, their usage of AI assistants has a high chance of producing insecure programs in programming tasks that frequently appear in computing curricula. Meanwhile, the results of our study indicate that a security mindset can greatly contribute to students’ usage of AI assistants in terms of code security. Our study further discussed and evaluated strategies to improve students’ secure usage of AI assistants in computing education by integrating a security mindset.

大语言模型（llm）的最新进展已经开始改变与计算机相关的学生编写代码的方式。基于法学硕士的人工智能助手，如ChatGPT和Copilot，现在越来越多地被计算机专业的学生用来编写功能代码。尽管有研究表明，这些人工智能助手可以提高编码效率，但它们也带来了安全挑战，尤其是在用户缺乏安全思维的情况下。鉴于人工智能助手越来越多地融入计算教育，本文进行了一项实证研究，探讨安全思维对计算机相关学生在编码和开发中使用人工智能助手的影响。我们的三个阶段的研究表明，相当一部分与计算机相关的学生目前对使用人工智能助手缺乏安全意识。此外，在计算机课程中经常出现的编程任务中，他们使用人工智能助手很有可能产生不安全的程序。同时，我们的研究结果表明，在代码安全方面，安全思维可以极大地促进学生使用人工智能助手。我们的研究进一步讨论和评估了通过整合安全思维来提高学生在计算教育中对人工智能助手的安全使用的策略。

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