Contribution: This study proposes a student dropout prediction model, named image convolutional and bi-directional temporal convolutional network (IC-BTCN), which makes dropout prediction for learners based on the learning clickstream data of students in massive open online courses (MOOCs) courses. Background: The MOOCs learning platform attracts hundreds of millions of users with in-depth teaching content and low-threshold learning methods. However, the high-dropout rate has always been its weakness compared with offline teaching. Intended Outcomes: The effectiveness of IC-BTCN model is evaluated on the KDD CUP 2015 dataset, including a large amount of clickstream data from the online learning platforms. The experimental results show that IC-BTCN model achieves an accuracy rate of 89.3%. Application Design: First, learning record data of students are converted into 3-D learning behavior matrix. Then, local features of the behavior matrix are extracted through convolutional techniques. These extracted learning features are then input into a temporal convolutional network to further refine the data. The temporal learning features of students are extracted through dilated causal convolution. Finally, a multilayer perceptron is used to derive the dropout prediction for students. Findings: Compared with three typical deep learning models, IC-BTCN model is advanced in accuracy and other evaluation indicators. On the premise of complying with the provisions of MOOCs platforms, the IC-BTCN model has good portability and practicability.
贡献：本研究提出了一种学生辍学预测模型，命名为图像卷积和双向时间卷积网络（IC-BTCN），该模型基于大规模在线开放课程（mooc）中学生的学习点击流数据对学习者进行辍学预测。背景：mooc学习平台以深度的教学内容和低门槛的学习方式吸引了上亿用户。然而，与线下教学相比，高辍学率一直是其弱点。预期结果：IC-BTCN模型的有效性在KDD CUP 2015数据集上进行了评估，其中包括来自在线学习平台的大量点击流数据。实验结果表明，IC-BTCN模型的准确率达到89.3%。应用设计：首先，将学生的学习记录数据转换成三维学习行为矩阵。然后，通过卷积技术提取行为矩阵的局部特征；然后将这些提取的学习特征输入到时间卷积网络中以进一步优化数据。通过扩展因果卷积提取学生的时间学习特征。最后，使用多层感知器对学生进行退学预测。结果：与三种典型的深度学习模型相比，IC-BTCN模型在准确率等评价指标上具有优势。在符合mooc平台规定的前提下，IC-BTCN模型具有良好的可移植性和实用性。
{"title":"IC-BTCN: A Deep Learning Model for Dropout Prediction of MOOCs Students","authors":"Xinhong Zhang;Xiangyu Wang;Jiayin Zhao;Boyan Zhang;Fan Zhang","doi":"10.1109/TE.2024.3398771","DOIUrl":"10.1109/TE.2024.3398771","url":null,"abstract":"Contribution: This study proposes a student dropout prediction model, named image convolutional and bi-directional temporal convolutional network (IC-BTCN), which makes dropout prediction for learners based on the learning clickstream data of students in massive open online courses (MOOCs) courses. Background: The MOOCs learning platform attracts hundreds of millions of users with in-depth teaching content and low-threshold learning methods. However, the high-dropout rate has always been its weakness compared with offline teaching. Intended Outcomes: The effectiveness of IC-BTCN model is evaluated on the KDD CUP 2015 dataset, including a large amount of clickstream data from the online learning platforms. The experimental results show that IC-BTCN model achieves an accuracy rate of 89.3%. Application Design: First, learning record data of students are converted into 3-D learning behavior matrix. Then, local features of the behavior matrix are extracted through convolutional techniques. These extracted learning features are then input into a temporal convolutional network to further refine the data. The temporal learning features of students are extracted through dilated causal convolution. Finally, a multilayer perceptron is used to derive the dropout prediction for students. Findings: Compared with three typical deep learning models, IC-BTCN model is advanced in accuracy and other evaluation indicators. On the premise of complying with the provisions of MOOCs platforms, the IC-BTCN model has good portability and practicability.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 6","pages":"974-982"},"PeriodicalIF":2.1,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Campbell R. Bego;Joshua C. Nwokeji;Deborah A. Trytten
The promise of engineering and computing education is to prepare and fully equip students to solve societal problems that are complex, open-ended, and/or poorly defined [1] . So far, engineering and computing educators have made notable progress toward fulfilling this promise. However, established practices are constantly tested by new challenges like those caused by the coronavirus pandemic and breakthroughs in generative artificial intelligence. For engineering and computing education to achieve its full potential and deliver its promise, these new challenges have to be elicited, analyzed, and addressed.
{"title":"FIE-TOE Guest Editorial Grand Challenges in Engineering and Computing Education: Beyond the Pandemic","authors":"Campbell R. Bego;Joshua C. Nwokeji;Deborah A. Trytten","doi":"10.1109/TE.2024.3382848","DOIUrl":"https://doi.org/10.1109/TE.2024.3382848","url":null,"abstract":"The promise of engineering and computing education is to prepare and fully equip students to solve societal problems that are complex, open-ended, and/or poorly defined \u0000<xref>[1]</xref>\u0000. So far, engineering and computing educators have made notable progress toward fulfilling this promise. However, established practices are constantly tested by new challenges like those caused by the coronavirus pandemic and breakthroughs in generative artificial intelligence. For engineering and computing education to achieve its full potential and deliver its promise, these new challenges have to be elicited, analyzed, and addressed.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 3","pages":"333-335"},"PeriodicalIF":2.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547114","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141245060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE Transactions on Education Information for Authors","authors":"","doi":"10.1109/TE.2024.3401881","DOIUrl":"https://doi.org/10.1109/TE.2024.3401881","url":null,"abstract":"","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 3","pages":"C3-C3"},"PeriodicalIF":2.6,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141245046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Isaac Lozano-Osorio;Sonia Ruiz-Olmedilla;Diana Pérez-Marín;Maximiliano Paredes-Velasco
Contribution: A methodology based on the learning by teaching approach facilitated by a mobile learning tool for creating tutorials. A study of the impact this methodology has had on the emotional well-being of students and its correlation with academic performance has also been carried out. Background: Insufficient resources and the absence of targeted teaching methods for technical content in vocational training may impact academic outcomes and lead to student demotivation. Previous studies indicate that employing active group methodologies contributes to the improvement of educational quality and positively influences the emotional well-being of students. Intended Outcomes: Improved academic performance, motivation levels, and collaborative work among peers, particularly within vocational training cycles. Application Design: A quasi-experimental design with a pre–post assessment of knowledge and emotional states. The study was carried out with 131 vocational training students, with one group following a conventional master class methodology that used practical activities, and another group following the learning by teaching methodology, where students created explanatory tutorials for their peers using a mobile application. Findings: Students who followed the proposed methodology significantly improved learning results compared to those who followed the traditional methodology. This improvement was evident both at the end of the experience and in the final evaluation of the course. Moreover, results revealed that the emotions of enjoyment and pride at the end of the learning process positively correlated with the acquisition of knowledge and that this correlation was more pronounced within the cohort that followed the learning by teaching methodology.
{"title":"Learning by Teaching: Creation of Tutorials in the Field of Vocational Training","authors":"Isaac Lozano-Osorio;Sonia Ruiz-Olmedilla;Diana Pérez-Marín;Maximiliano Paredes-Velasco","doi":"10.1109/TE.2024.3387336","DOIUrl":"10.1109/TE.2024.3387336","url":null,"abstract":"Contribution: A methodology based on the learning by teaching approach facilitated by a mobile learning tool for creating tutorials. A study of the impact this methodology has had on the emotional well-being of students and its correlation with academic performance has also been carried out. Background: Insufficient resources and the absence of targeted teaching methods for technical content in vocational training may impact academic outcomes and lead to student demotivation. Previous studies indicate that employing active group methodologies contributes to the improvement of educational quality and positively influences the emotional well-being of students. Intended Outcomes: Improved academic performance, motivation levels, and collaborative work among peers, particularly within vocational training cycles. Application Design: A quasi-experimental design with a pre–post assessment of knowledge and emotional states. The study was carried out with 131 vocational training students, with one group following a conventional master class methodology that used practical activities, and another group following the learning by teaching methodology, where students created explanatory tutorials for their peers using a mobile application. Findings: Students who followed the proposed methodology significantly improved learning results compared to those who followed the traditional methodology. This improvement was evident both at the end of the experience and in the final evaluation of the course. Moreover, results revealed that the emotions of enjoyment and pride at the end of the learning process positively correlated with the acquisition of knowledge and that this correlation was more pronounced within the cohort that followed the learning by teaching methodology.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 4","pages":"602-609"},"PeriodicalIF":2.1,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140837750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Contribution: This article explored problem scoping in an industry sponsored engineering design capstone course before and during the COVID-19 pandemic. It employs reflection-in-action and quantitative action research, to demonstrate how the pandemic offered an opportunity for 1) students to develop their problem scoping skills and 2) for the instruction team to design novel reflection-based interventions to support students’ problem scoping process. Background: Despite the importance of problem scoping in engineering design, its development in engineering undergraduates, especially during crises, such as the COVID-19 pandemic, remains understudied. While the pandemic’s impact on higher education is generally perceived to have been negative, this article presents a different perspective, suggesting it may have improved students’ reflective practices. This is examined by comparing three cohorts of students who participated in the capstone prepandemic (2019), at the onset of the pandemic (2020), and a year after the pandemic began (2021). Research Question: How did students’ problem scoping skills change due to pandemic-motivated interventions in 2020 and 2021? Methodology: A quantitative action research approach was utilized due to the iterative nature of the interventions designed in response to the pandemic. Scoping metrics were derived from final project reports and compared across cohorts using nonparametric statistical tests. Nonparametric tests were selected due to small sample sizes and violation of the assumption of normality and homogeneity of variance. Findings: Increasing opportunities for reflection-in-action lead to improved design performance (with nuances) underscoring the importance of teaching reflective design practices to engineering students to better equip for complex design problem-solving.
{"title":"The Impact of Scaffolding and Reflection During Problem Scoping in Capstone Design","authors":"S. Misra;D. Wilson","doi":"10.1109/TE.2024.3380413","DOIUrl":"10.1109/TE.2024.3380413","url":null,"abstract":"Contribution: This article explored problem scoping in an industry sponsored engineering design capstone course before and during the COVID-19 pandemic. It employs reflection-in-action and quantitative action research, to demonstrate how the pandemic offered an opportunity for 1) students to develop their problem scoping skills and 2) for the instruction team to design novel reflection-based interventions to support students’ problem scoping process. Background: Despite the importance of problem scoping in engineering design, its development in engineering undergraduates, especially during crises, such as the COVID-19 pandemic, remains understudied. While the pandemic’s impact on higher education is generally perceived to have been negative, this article presents a different perspective, suggesting it may have improved students’ reflective practices. This is examined by comparing three cohorts of students who participated in the capstone prepandemic (2019), at the onset of the pandemic (2020), and a year after the pandemic began (2021). Research Question: How did students’ problem scoping skills change due to pandemic-motivated interventions in 2020 and 2021? Methodology: A quantitative action research approach was utilized due to the iterative nature of the interventions designed in response to the pandemic. Scoping metrics were derived from final project reports and compared across cohorts using nonparametric statistical tests. Nonparametric tests were selected due to small sample sizes and violation of the assumption of normality and homogeneity of variance. Findings: Increasing opportunities for reflection-in-action lead to improved design performance (with nuances) underscoring the importance of teaching reflective design practices to engineering students to better equip for complex design problem-solving.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 4","pages":"582-591"},"PeriodicalIF":2.1,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Contribution: This study qualitatively uncovered meaning for why and what was motivating to undergraduates participating in an educational human—robot interaction (HRI) experience. A data corpus of four documents (groups) was evaluated from a quasi-experimental, nonequivalent control ( ${n},,=$ 23) and treatment ( ${n},,=$ 61) research design revealing three themes attributing meaning for why and what was motivating to students. Background: Engineering education literature has indicated a positive impact on student motivation and academic success from HRIs. However, rigorous research that attributes meaning for why and what are motivating to students in these HRI experiences is not readily available. Intended Outcomes: Results of this study represent substantial findings that answer questions of why and what are motivating in HRI experiences. However, future research is needed to further understand the nuanced dynamics and multidimensional aspects of motivation in HRIs. Application Design: To analyze the qualitative open-ended survey responses from students, structured term frequency (tf), tf – inverse document frequency (tf-idf), latent Dirichlet allocation (LDA) modeling, and content analysis were used, as these approaches are typical in computational text mining. Findings: When describing motivation toward the HRI, students were found to form a common lexicon to attribute why and what was motivating (e.g., “…[being] abl[e]” [to] see [the] robot follow [the line]…”). This suggests that the tangible, visual, immediate feedback experienced by students was why and what motivated them.
{"title":"Toward Uncovering Meaning in Human-Robot Interactions","authors":"John R. Haughery","doi":"10.1109/TE.2024.3384255","DOIUrl":"10.1109/TE.2024.3384255","url":null,"abstract":"Contribution: This study qualitatively uncovered meaning for why and what was motivating to undergraduates participating in an educational human—robot interaction (HRI) experience. A data corpus of four documents (groups) was evaluated from a quasi-experimental, nonequivalent control (\u0000<inline-formula> <tex-math>${n},,=$ </tex-math></inline-formula>\u0000 23) and treatment (\u0000<inline-formula> <tex-math>${n},,=$ </tex-math></inline-formula>\u0000 61) research design revealing three themes attributing meaning for why and what was motivating to students. Background: Engineering education literature has indicated a positive impact on student motivation and academic success from HRIs. However, rigorous research that attributes meaning for why and what are motivating to students in these HRI experiences is not readily available. Intended Outcomes: Results of this study represent substantial findings that answer questions of why and what are motivating in HRI experiences. However, future research is needed to further understand the nuanced dynamics and multidimensional aspects of motivation in HRIs. Application Design: To analyze the qualitative open-ended survey responses from students, structured term frequency (tf), tf – inverse document frequency (tf-idf), latent Dirichlet allocation (LDA) modeling, and content analysis were used, as these approaches are typical in computational text mining. Findings: When describing motivation toward the HRI, students were found to form a common lexicon to attribute why and what was motivating (e.g., “…[being] abl[e]” [to] see [the] robot follow [the line]…”). This suggests that the tangible, visual, immediate feedback experienced by students was why and what motivated them.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 4","pages":"592-601"},"PeriodicalIF":2.1,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marilena Bratu;Cristina Hava Muntean;Cristian Buică-Belciu;Sabina Stan;Gabriel-Miro Muntean
Contributions: This article presents the results of a study carried out as part of two large-scale pilots and analyzes the impact of the use of modern technologies in education in term of knowledge acquisition in case of students with hearing disabilities. Background: Teaching topics from STEM area is a challenging task for all teachers who have students with hearing disabilities in their class. On the one hand, because of the high degree of difficulty of the information in this field and on the other hand, because of the difficulties that students with hearing disabilities face in understanding this information. In this context, the use of modern technologies in education, but especially the way they are used together with traditional ones, can provide great support for teachers of students with special needs. Intended Outcomes: A set of modern technologies, i.e., virtual lab, virtual reality (VR), in the game-based learning context of the EU-funded NEWTON project, were employed in education as part of the Earth Course. The goal was to increase the accessibility of STEM information in the area of natural sciences for the benefit of students, especially those with hearing disabilities. Application Design: The study targeted secondary school students with hearing disabilities. Content related to natural sciences (i.e., wildlife and sealife) was presented as part of the Earth Course via innovative applications that included virtual laboratories and VR. Avatars were used to support the students with special needs (sign-language translation). Findings: The results of this study demonstrate that the use of modern technologies, combined with game-based learning in science lessons for students with hearing disabilities, contribute to increasing their knowledge level. The best results were obtained when these technologies were used in mixed lessons, alongside the traditional teaching methods.
贡献：本文介绍了作为两个大型试点项目的一部分而开展的一项研究的结果，并分析了在教育中使用现代技术对听力残疾学生获取知识的影响。研究背景对于班上有听力残疾学生的所有教师来说，STEM 领域的教学课题都是一项具有挑战性的任务。一方面是因为该领域的信息难度很大，另一方面是因为听力残疾学生在理解这些信息时面临困难。在这种情况下，在教育中使用现代技术，特别是将现代技术与传统技术结合起来使用的方式，可以为有特殊需要的学生的教师提供极大的支持。预期成果：在欧盟资助的 NEWTON 项目的游戏式学习背景下，一套现代技术，即虚拟实验室、虚拟现实 （VR），被应用于教育中，作为地球课程的一部分。其目的是提高自然科学领域 STEM 信息的可及性，使学生，尤其是听力有障碍的学生受益。应用设计：研究对象是有听力障碍的中学生。与自然科学（即野生动物和海豹）相关的内容作为地球课程的一部分，通过包括虚拟实验室和 VR 在内的创新应用进行展示。使用头像为有特殊需要的学生提供支持（手语翻译）。研究结果本研究的结果表明，在为听力残疾学生开设的科学课中使用现代技术，结合游戏式学习，有助于提高他们的知识水平。当这些技术与传统教学方法一起在混合课程中使用时，效果最好。
{"title":"Impact of NEWTON Technology-Enhanced Learning Solutions on Knowledge Acquisition in Pilots Involving Students With Hearing Impairments","authors":"Marilena Bratu;Cristina Hava Muntean;Cristian Buică-Belciu;Sabina Stan;Gabriel-Miro Muntean","doi":"10.1109/TE.2024.3378103","DOIUrl":"10.1109/TE.2024.3378103","url":null,"abstract":"Contributions: This article presents the results of a study carried out as part of two large-scale pilots and analyzes the impact of the use of modern technologies in education in term of knowledge acquisition in case of students with hearing disabilities. Background: Teaching topics from STEM area is a challenging task for all teachers who have students with hearing disabilities in their class. On the one hand, because of the high degree of difficulty of the information in this field and on the other hand, because of the difficulties that students with hearing disabilities face in understanding this information. In this context, the use of modern technologies in education, but especially the way they are used together with traditional ones, can provide great support for teachers of students with special needs. Intended Outcomes: A set of modern technologies, i.e., virtual lab, virtual reality (VR), in the game-based learning context of the EU-funded NEWTON project, were employed in education as part of the Earth Course. The goal was to increase the accessibility of STEM information in the area of natural sciences for the benefit of students, especially those with hearing disabilities. Application Design: The study targeted secondary school students with hearing disabilities. Content related to natural sciences (i.e., wildlife and sealife) was presented as part of the Earth Course via innovative applications that included virtual laboratories and VR. Avatars were used to support the students with special needs (sign-language translation). Findings: The results of this study demonstrate that the use of modern technologies, combined with game-based learning in science lessons for students with hearing disabilities, contribute to increasing their knowledge level. The best results were obtained when these technologies were used in mixed lessons, alongside the traditional teaching methods.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 3","pages":"472-482"},"PeriodicalIF":2.6,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140629200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabiano Fruett;Fernanda Pereira Barbosa;Samuel Cardoso Zampolli Fraga;Pedro Ivo Aragão Guimarães
Open-source hardware and software platforms have played an important role in democratizing access to technology and education in computer science and engineering. Recent advancements in tools, such as KiCad, MicroPython, and Thonny, integrated development environment potentially accelerate low-budget educational applications, providing a smooth and consistent learning curve for users. The BitDogLab, utilizing these platforms, is designed and developed for Science, Technology, Engineering, Arts, and Mathematics activities based on the Raspberry Pi Pico Board, featuring artificial intelligence (AI)-supported programming via the GPT model. BitDogLab is an open-source hardware solution with a flexible design for continuous expansion in both software and hardware areas. Its functionality, based on open-source code, can be enhanced by integrating various software resources. Additionally, BitDogLab’s adaptability to sophisticated hardware like robotic systems or advanced sensors expands its educational applications. An evaluation of BitDogLab, conducted through a hands-on workshop at the Latin America Open Technology Conference—Latinoware 2023, revealed unanimous interest among 36 respondents, mainly for teaching programming (58%), and valuing its low cost, diverse components, and ease of AI-assisted programming. 75% of respondents expressed a strong desire to use BitDogLab, despite 19% preferring familiar boards like Arduino or Micro:bit. The project’s open distribution encourages community contributions, promoting collaborative learning and innovation from children to preuniversity students.
{"title":"Empowering STEAM Activities With Artificial Intelligence and Open Hardware: The BitDogLab","authors":"Fabiano Fruett;Fernanda Pereira Barbosa;Samuel Cardoso Zampolli Fraga;Pedro Ivo Aragão Guimarães","doi":"10.1109/TE.2024.3377555","DOIUrl":"10.1109/TE.2024.3377555","url":null,"abstract":"Open-source hardware and software platforms have played an important role in democratizing access to technology and education in computer science and engineering. Recent advancements in tools, such as KiCad, MicroPython, and Thonny, integrated development environment potentially accelerate low-budget educational applications, providing a smooth and consistent learning curve for users. The BitDogLab, utilizing these platforms, is designed and developed for Science, Technology, Engineering, Arts, and Mathematics activities based on the Raspberry Pi Pico Board, featuring artificial intelligence (AI)-supported programming via the GPT model. BitDogLab is an open-source hardware solution with a flexible design for continuous expansion in both software and hardware areas. Its functionality, based on open-source code, can be enhanced by integrating various software resources. Additionally, BitDogLab’s adaptability to sophisticated hardware like robotic systems or advanced sensors expands its educational applications. An evaluation of BitDogLab, conducted through a hands-on workshop at the Latin America Open Technology Conference—Latinoware 2023, revealed unanimous interest among 36 respondents, mainly for teaching programming (58%), and valuing its low cost, diverse components, and ease of AI-assisted programming. 75% of respondents expressed a strong desire to use BitDogLab, despite 19% preferring familiar boards like Arduino or Micro:bit. The project’s open distribution encourages community contributions, promoting collaborative learning and innovation from children to preuniversity students.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 3","pages":"462-471"},"PeriodicalIF":2.6,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roee Peretz;Natali Levi-Soskin;Dov Dori;Yehudit Judy Dori
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.
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。
{"title":"Assessing Engineering Students’ Systems Thinking and Modeling Based on Their Online Learning","authors":"Roee Peretz;Natali Levi-Soskin;Dov Dori;Yehudit Judy Dori","doi":"10.1109/TE.2024.3379218","DOIUrl":"10.1109/TE.2024.3379218","url":null,"abstract":"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.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 3","pages":"483-490"},"PeriodicalIF":2.6,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The sample mean in statistics is a concept of great importance, with its properties being extensively utilized in other areas, such as computer science. This research centers on the concept of the sample mean and its characteristics in a cohort of computer engineering students undertaking a required course in statistics at a university in the Netherlands. The objective of this study was to analyze students’ comprehension of the fundamental structure, properties, and applications of the sample mean. A digital mini-course on sample mean was developed and employed with 97 students, who had the option to apply the concepts at their own pace. The action-process-object-schema (APOS) theoretical framework was utilized to analyze the interview responses of seven participants. The majority of participants were able to demonstrate an understanding of the Process conception of the sample mean, with only a minority demonstrating an understanding of the Object conception. The lack of comprehension of the sample mean hindered students’ capacity to effectively utilize the associated applications and properties. These findings imply that educators should strive to ensure that students have a sound comprehension of the sample mean so that they are better equipped to work with related applications. Suggestions of this study and further research ideas are indicated.
{"title":"Conceptualizing the Sample Mean: Insights for Computer Engineering Students in the Learning Process","authors":"Fulya Kula;Nelly Litvak;Tracy S. Craig","doi":"10.1109/TE.2024.3376795","DOIUrl":"10.1109/TE.2024.3376795","url":null,"abstract":"The sample mean in statistics is a concept of great importance, with its properties being extensively utilized in other areas, such as computer science. This research centers on the concept of the sample mean and its characteristics in a cohort of computer engineering students undertaking a required course in statistics at a university in the Netherlands. The objective of this study was to analyze students’ comprehension of the fundamental structure, properties, and applications of the sample mean. A digital mini-course on sample mean was developed and employed with 97 students, who had the option to apply the concepts at their own pace. The action-process-object-schema (APOS) theoretical framework was utilized to analyze the interview responses of seven participants. The majority of participants were able to demonstrate an understanding of the Process conception of the sample mean, with only a minority demonstrating an understanding of the Object conception. The lack of comprehension of the sample mean hindered students’ capacity to effectively utilize the associated applications and properties. These findings imply that educators should strive to ensure that students have a sound comprehension of the sample mean so that they are better equipped to work with related applications. Suggestions of this study and further research ideas are indicated.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 4","pages":"574-581"},"PeriodicalIF":2.1,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140563340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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