André Rocha, Lino Sousa, Mário Alves, Armando Sousa
The trend for an increasingly ubiquitous and cyber‐physical world has been leveraging the use and importance of microcontrollers (μC) to unprecedented levels. Therefore, microcontroller programming (μCP) becomes a paramount skill for electrical and computer engineering students. However, μCP poses significant challenges for undergraduate students, given the need to master low‐level programming languages and several algorithmic strategies that are not usual in “generic” programming. Moreover, μCP can be time‐consuming and complex even when using high‐level languages. This article samples the current state of μCP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of μCP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8‐bit μC and project‐based learning. While NLP tools emerge as strong candidates as students' μC companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for μCP education, building on both hands‐on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real‐world tasks that the μC is designed to handle, rather than on low‐level coding. We also analyzed the specific impact of chatGTP in the context of a μCP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in μCP courses.
{"title":"The underlying potential of NLP for microcontroller programming education","authors":"André Rocha, Lino Sousa, Mário Alves, Armando Sousa","doi":"10.1002/cae.22778","DOIUrl":"https://doi.org/10.1002/cae.22778","url":null,"abstract":"The trend for an increasingly ubiquitous and cyber‐physical world has been leveraging the use and importance of microcontrollers (<jats:italic>μ</jats:italic>C) to unprecedented levels. Therefore, microcontroller programming (<jats:italic>μ</jats:italic>CP) becomes a paramount skill for electrical and computer engineering students. However, <jats:italic>μ</jats:italic>CP poses significant challenges for undergraduate students, given the need to master low‐level programming languages and several algorithmic strategies that are not usual in “generic” programming. Moreover, <jats:italic>μ</jats:italic>CP can be time‐consuming and complex even when using high‐level languages. This article samples the current state of <jats:italic>μ</jats:italic>CP education in Portugal and unveils the potential support of natural language processing (NLP) tools (such as chatGPT). Our analysis of <jats:italic>μ</jats:italic>CP curricular units from seven representative Portuguese engineering schools highlights a predominant use of AVR 8‐bit <jats:italic>μ</jats:italic>C and project‐based learning. While NLP tools emerge as strong candidates as students' <jats:italic>μ</jats:italic>C companion, their application and impact on the learning process and outcomes deserve to be understood. This study compares the most prominent NLP tools, analyzing their benefits and drawbacks for <jats:italic>μ</jats:italic>CP education, building on both hands‐on tests and literature reviews. By providing automatic code generation and explanation of concepts, NLP tools can assist students in their learning process, allowing them to focus on software design and real‐world tasks that the <jats:italic>μ</jats:italic>C is designed to handle, rather than on low‐level coding. We also analyzed the specific impact of chatGTP in the context of a <jats:italic>μ</jats:italic>CP course at ISEP, confirming most of our expectations, but with a few curiosities. Overall, this work establishes the foundations for future research on the effective integration of NLP tools in <jats:italic>μ</jats:italic>CP courses.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the Decision Tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.
{"title":"Machine learning methods as auxiliary tool for effective mathematics teaching","authors":"Marina Milićević, Budimirka Marinović, Ljerka Jeftić","doi":"10.1002/cae.22787","DOIUrl":"https://doi.org/10.1002/cae.22787","url":null,"abstract":"Seeing mathematics teaching as a very demanding and responsible process while having in mind the importance of mathematical knowledge for students of technical faculties, this paper aims to present heuristics for student classification according to their predicted mathematical success. Over the last few decades, the process of informatization of universities has resulted in new challenges universities are faced with. Due to the widespread use of educational databases, which opens new possibilities for educational data mining and analyses, machine learning algorithms have become a very popular tool for predicting students' academic performance. The decision tree algorithm is used in this paper for the classification and prediction of students' mathematical performance and it is trained on the data collected from the educational information system. The experimental results show that the model accuracy is 72% with an error rate of 0.28. The implementation of the Decision Tree Model to predict whether a student will pass, fail or be conditional in mathematical courses is important for both teachers and students, as well as for universities. Students' performance is one of the major keys in evaluating the quality of the teaching process, but also for evaluating the overall success of the university itself. As mathematics is considered a basic and important discipline, it is clear why predicting students' mathematical achievement is crucial for all levels of university organization.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software Engineering is a discipline frequently reflected in training strategies. This discipline requires certain levels of abstraction to achieve the competencies and skills necessary for the professional development of future software developers. The software industry increasingly demands that professionals in this discipline have social and human skills to achieve highly productive teams. Therefore, these teams should respond to such demands in a world with increasing dependence on technology and the development of software products. Traditional pedagogical strategies often need help adapting to the new generations of software engineers and responding in a limited way to the demands of teaching processes related to this discipline. This article evaluates a gamification‐based strategy designed for the Software Engineering course at a Latin American higher education institution. This course addressed software project management as a training objective. Such a strategy was designed with a gamification‐based model to influence the productivity of software development teams. The results of using the model show its efficiency and usefulness as a guide for implementing new strategies based on gamification that considers social and human factors (SHFs) to intervene in the productivity of software development teams. The challenges designed in the proposal presented managed to promote SHFs in the participants, according to the analysis of the prepared case study. According to these results, the factors considered relate to skills and experience in managing software development projects, motivation, and communication. The activities executed by the participants in the context of the case study strengthened the human side of the team and allowed its growth to achieve its objectives.
{"title":"Gamification strategy to promote social and human factors in the training of software engineers: A case study","authors":"Gloria Piedad Gasca‐Hurtado, Liliana Machuca‐Villegas","doi":"10.1002/cae.22785","DOIUrl":"https://doi.org/10.1002/cae.22785","url":null,"abstract":"Software Engineering is a discipline frequently reflected in training strategies. This discipline requires certain levels of abstraction to achieve the competencies and skills necessary for the professional development of future software developers. The software industry increasingly demands that professionals in this discipline have social and human skills to achieve highly productive teams. Therefore, these teams should respond to such demands in a world with increasing dependence on technology and the development of software products. Traditional pedagogical strategies often need help adapting to the new generations of software engineers and responding in a limited way to the demands of teaching processes related to this discipline. This article evaluates a gamification‐based strategy designed for the Software Engineering course at a Latin American higher education institution. This course addressed software project management as a training objective. Such a strategy was designed with a gamification‐based model to influence the productivity of software development teams. The results of using the model show its efficiency and usefulness as a guide for implementing new strategies based on gamification that considers social and human factors (SHFs) to intervene in the productivity of software development teams. The challenges designed in the proposal presented managed to promote SHFs in the participants, according to the analysis of the prepared case study. According to these results, the factors considered relate to skills and experience in managing software development projects, motivation, and communication. The activities executed by the participants in the context of the case study strengthened the human side of the team and allowed its growth to achieve its objectives.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141778047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
José A. Abell, Patricio A. Moreno‐Casas, Matías Recabarren
In an era where technology continually reshapes the landscape of professional practice, it has become relevant to equip engineering students with advanced computational skills beyond programming. This article presents a novel discipline‐based framework designed to integrate advanced computational skills into engineering education. Responding to challenges such as the disconnection between computational abilities and domain‐specific knowledge, and student demotivation due to overwhelming technological challenges, this study aims to validate the impact of the framework on domain learning, computational skill acquisition, and perceived future utility. Implementing a case study approach, we explore the development of high‐performance computing skills within a project‐based learning context in Civil Engineering. Results indicate significant improvements in students' understanding of both computational concepts and the engineering domain, evidenced by enhanced self‐perception and positive Technology Acceptance Model outcomes. The framework facilitated a meaningful connection between computational skills and professional applications, as seen in students' project reflections. Despite the promising results, the necessity for instructors to possess and impart computational knowledge is highlighted as an important factor for successful integration. This study contributes to educational computing research by providing a scalable approach to embedding advanced computational skills in engineering curricula, addressing existing educational challenges, and suggesting directions for future research.
{"title":"Integrating advanced computational skills into engineering education: A discipline‐based approach","authors":"José A. Abell, Patricio A. Moreno‐Casas, Matías Recabarren","doi":"10.1002/cae.22784","DOIUrl":"https://doi.org/10.1002/cae.22784","url":null,"abstract":"In an era where technology continually reshapes the landscape of professional practice, it has become relevant to equip engineering students with advanced computational skills beyond programming. This article presents a novel discipline‐based framework designed to integrate advanced computational skills into engineering education. Responding to challenges such as the disconnection between computational abilities and domain‐specific knowledge, and student demotivation due to overwhelming technological challenges, this study aims to validate the impact of the framework on domain learning, computational skill acquisition, and perceived future utility. Implementing a case study approach, we explore the development of high‐performance computing skills within a project‐based learning context in Civil Engineering. Results indicate significant improvements in students' understanding of both computational concepts and the engineering domain, evidenced by enhanced self‐perception and positive Technology Acceptance Model outcomes. The framework facilitated a meaningful connection between computational skills and professional applications, as seen in students' project reflections. Despite the promising results, the necessity for instructors to possess and impart computational knowledge is highlighted as an important factor for successful integration. This study contributes to educational computing research by providing a scalable approach to embedding advanced computational skills in engineering curricula, addressing existing educational challenges, and suggesting directions for future research.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The launch of Generative Pretrained Transformer (ChatGPT) at the end of 2022 generated large interest in possible applications of artificial intelligence (AI) in science, technology, engineering, and mathematics (STEM) education and among STEM professions. As a result many questions surrounding the capabilities of generative AI tools inside and outside of the classroom have been raised and are starting to be explored. This study examines the capabilities of ChatGPT within the discipline of mechanical engineering. It aims to examine the use cases and pitfalls of such a technology in the classroom and professional settings. ChatGPT was presented with a set of questions from junior‐ and senior‐level mechanical engineering exams provided at a large private university, as well as a set of practice questions for the Fundamentals of Engineering (FE) exam in mechanical engineering. The responses of two ChatGPT models, one free to use and one paid subscription, were analyzed. The paper found that the subscription model (GPT‐4, May 12, 2023) greatly outperformed the free version (GPT‐3.5, May 12, 2023), achieving 76% correct versus 51% correct, but the limitation of text only input on both models makes neither likely to pass the FE exam. The results confirm findings in the literature with regard to types of errors and pitfalls made by ChatGPT. It was found that due to its inconsistency and a tendency to confidently produce incorrect answers, the tool is best suited for users with expert knowledge.
{"title":"ChatGPT‐3.5 and ‐4.0 and mechanical engineering: Examining performance on the FE mechanical engineering and undergraduate exams","authors":"Matthew Frenkel, Hebah Emara","doi":"10.1002/cae.22781","DOIUrl":"https://doi.org/10.1002/cae.22781","url":null,"abstract":"The launch of Generative Pretrained Transformer (ChatGPT) at the end of 2022 generated large interest in possible applications of artificial intelligence (AI) in science, technology, engineering, and mathematics (STEM) education and among STEM professions. As a result many questions surrounding the capabilities of generative AI tools inside and outside of the classroom have been raised and are starting to be explored. This study examines the capabilities of ChatGPT within the discipline of mechanical engineering. It aims to examine the use cases and pitfalls of such a technology in the classroom and professional settings. ChatGPT was presented with a set of questions from junior‐ and senior‐level mechanical engineering exams provided at a large private university, as well as a set of practice questions for the Fundamentals of Engineering (FE) exam in mechanical engineering. The responses of two ChatGPT models, one free to use and one paid subscription, were analyzed. The paper found that the subscription model (GPT‐4, May 12, 2023) greatly outperformed the free version (GPT‐3.5, May 12, 2023), achieving 76% correct versus 51% correct, but the limitation of text only input on both models makes neither likely to pass the FE exam. The results confirm findings in the literature with regard to types of errors and pitfalls made by ChatGPT. It was found that due to its inconsistency and a tendency to confidently produce incorrect answers, the tool is best suited for users with expert knowledge.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Daniel Mora‐Melia, Jimmy H. Gutiérrez‐Bahamondes, Pedro L. Iglesias‐Rey, Francisco Javier Martinez‐Solano
Recently, the growing demand for computational fluid dynamics (CFD) skills in industry has highlighted the importance of their incorporation into university academic programs at both the undergraduate and graduate levels. However, many academic programs treat CFD tools as a “black box” in which users simply enter data without fully understanding the inner workings of the software or its application in real‐world situations. Therefore, in the context of a civil engineering program in Chile, a novel approach combining problem‐based learning (PBL) with CFD was introduced into the curriculum of a fluid mechanics course to foster crucial competencies. This comprehensive methodology allows students to acquire fundamental theoretical knowledge that is directly related to specific problems in the classroom. Subsequently, students measure relevant variables in the laboratory, ultimately using these data to build computational models for comparing and contrasting reality with simulations. To gauge the effectiveness and impact of this PBL strategy, both quantitative analysis of student performance and qualitative analysis through surveys were conducted. The results reveal a significant improvement in student performance with the implementation of the PBL methodology, alongside a positive perception among students regarding its implementation. This underscores its benefits for learning, motivation, and academic performance. Additionally, the implementation of PBL was found to enhance both theoretical and practical understanding of concepts related to fluid dynamics and CFD simulation.
{"title":"Exploring the synergy of problem‐based learning and computational fluid dynamics in university fluid mechanics instruction","authors":"Daniel Mora‐Melia, Jimmy H. Gutiérrez‐Bahamondes, Pedro L. Iglesias‐Rey, Francisco Javier Martinez‐Solano","doi":"10.1002/cae.22782","DOIUrl":"https://doi.org/10.1002/cae.22782","url":null,"abstract":"Recently, the growing demand for computational fluid dynamics (CFD) skills in industry has highlighted the importance of their incorporation into university academic programs at both the undergraduate and graduate levels. However, many academic programs treat CFD tools as a “black box” in which users simply enter data without fully understanding the inner workings of the software or its application in real‐world situations. Therefore, in the context of a civil engineering program in Chile, a novel approach combining problem‐based learning (PBL) with CFD was introduced into the curriculum of a fluid mechanics course to foster crucial competencies. This comprehensive methodology allows students to acquire fundamental theoretical knowledge that is directly related to specific problems in the classroom. Subsequently, students measure relevant variables in the laboratory, ultimately using these data to build computational models for comparing and contrasting reality with simulations. To gauge the effectiveness and impact of this PBL strategy, both quantitative analysis of student performance and qualitative analysis through surveys were conducted. The results reveal a significant improvement in student performance with the implementation of the PBL methodology, alongside a positive perception among students regarding its implementation. This underscores its benefits for learning, motivation, and academic performance. Additionally, the implementation of PBL was found to enhance both theoretical and practical understanding of concepts related to fluid dynamics and CFD simulation.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arduino, a widely used tool for physical computing, is favored for its affordability and easy availability. However, a drawback for beginners is the requirement of prior knowledge of C programming language and circuit theory for effectively utilizing Arduino. In this research, we address this issue by developing a Graphical Arduino IDE system that allows users to control Arduino without the need for prior knowledge of C language and circuit theory. Users can create node graph‐based scripts in the Wiring Tab of the Graphical Arduino IDE and develop flowchart‐based scripts in the Algorithm Tab. The scripts created in the Wiring Tab serve as guidelines for wiring, thus preventing users from making wiring mistakes. Additionally, users without knowledge of C language can control Arduino by creating flowchart‐based scripts in the Algorithm Tab. The finalized scripts are converted into Arduino code and uploaded to the Arduino board using the built‐in Code Upload feature. Finally, a paired t test was conducted between the Graphical Arduino IDE and Scratch for Arduino, confirming that the Graphical Arduino IDE required fewer user inputs.
Arduino 是一种广泛使用的物理计算工具，因其价格低廉、易于获得而备受青睐。然而，对于初学者来说，有效使用 Arduino 的一个缺点是需要事先掌握 C 语言编程和电路理论知识。在这项研究中，我们通过开发一个图形化 Arduino IDE 系统来解决这个问题，该系统允许用户在不需要 C 语言和电路理论知识的情况下控制 Arduino。用户可以在图形化 Arduino IDE 的布线选项卡中创建基于节点图的脚本，并在算法选项卡中开发基于流程图的脚本。在布线选项卡中创建的脚本可作为布线指南，从而避免用户犯布线错误。此外，不懂 C 语言的用户也可以通过在算法选项卡中创建基于流程图的脚本来控制 Arduino。最终完成的脚本会被转换成 Arduino 代码，并通过内置的代码上传功能上传到 Arduino 板上。最后，在图形化 Arduino IDE 和 Scratch for Arduino 之间进行了配对 t 检验，证实图形化 Arduino IDE 需要的用户输入更少。
{"title":"Graphical Arduino IDE system with wiring layout and flowchart functions for physical computing education","authors":"Il‐Kyu Hwang, Tae‐Woong Kong, Jin‐Hyuk Park","doi":"10.1002/cae.22783","DOIUrl":"https://doi.org/10.1002/cae.22783","url":null,"abstract":"Arduino, a widely used tool for physical computing, is favored for its affordability and easy availability. However, a drawback for beginners is the requirement of prior knowledge of C programming language and circuit theory for effectively utilizing Arduino. In this research, we address this issue by developing a Graphical Arduino IDE system that allows users to control Arduino without the need for prior knowledge of C language and circuit theory. Users can create node graph‐based scripts in the Wiring Tab of the Graphical Arduino IDE and develop flowchart‐based scripts in the Algorithm Tab. The scripts created in the Wiring Tab serve as guidelines for wiring, thus preventing users from making wiring mistakes. Additionally, users without knowledge of C language can control Arduino by creating flowchart‐based scripts in the Algorithm Tab. The finalized scripts are converted into Arduino code and uploaded to the Arduino board using the built‐in Code Upload feature. Finally, a paired <jats:italic>t</jats:italic> test was conducted between the Graphical Arduino IDE and Scratch for Arduino, confirming that the Graphical Arduino IDE required fewer user inputs<jats:italic>.</jats:italic>","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141584700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Janez Podobnik, Ana Udir, Marko Munih, Matjaž Mihelj
This paper presents the development of a teaching approach for Reinforcement Learning (RL) for students at the Faculty of Electrical Engineering, University of Ljubljana. The approach is designed to introduce students to the basic concepts, approaches, and algorithms of RL through examples and experiments in both simulation environments and on a real robot. The approach includes practical programs written in Python and presents various RL algorithms. The Q‐learning algorithm is introduced and a deep Q network is implemented to introduce the use of neural networks in deep RL. The software is user‐friendly and allows easy modification of learning parameters, reward functions, and algorithms. The approach was tested successfully on a Franka Emika Panda robot, where the robot manipulator learned to move to a randomly generated target position, shoot a real ball into the goal, and push various objects into target position. The goal of the presented teaching approach is to serve as a study aid for future generations of students of robotics to help them better understand the basic concepts of RL and apply them to a wide variety of problems.
{"title":"Teaching approach for deep reinforcement learning of robotic strategies","authors":"Janez Podobnik, Ana Udir, Marko Munih, Matjaž Mihelj","doi":"10.1002/cae.22780","DOIUrl":"https://doi.org/10.1002/cae.22780","url":null,"abstract":"This paper presents the development of a teaching approach for Reinforcement Learning (RL) for students at the Faculty of Electrical Engineering, University of Ljubljana. The approach is designed to introduce students to the basic concepts, approaches, and algorithms of RL through examples and experiments in both simulation environments and on a real robot. The approach includes practical programs written in Python and presents various RL algorithms. The Q‐learning algorithm is introduced and a deep Q network is implemented to introduce the use of neural networks in deep RL. The software is user‐friendly and allows easy modification of learning parameters, reward functions, and algorithms. The approach was tested successfully on a Franka Emika Panda robot, where the robot manipulator learned to move to a randomly generated target position, shoot a real ball into the goal, and push various objects into target position. The goal of the presented teaching approach is to serve as a study aid for future generations of students of robotics to help them better understand the basic concepts of RL and apply them to a wide variety of problems.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141572390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developing one's entrepreneurial mindset is important for all students, regardless of discipline. Evidence‐based decision‐making (which has the potential to lower costs and improve quality of life) is one approach for applying entrepreneurially minded learning in the undergraduate classroom. This approach allows students to understand trends related to data, in general, and big data, specifically. Furthermore, it better prepares graduates to evaluate and identify effective data science‐based solutions. The purpose of this study is to report on one pedagogical approach to developing the entrepreneurial mindset through integrating evidence‐based decision‐making into the engineering and technology classroom using Microsoft Power BI Desktop (a freely available tool released by Microsoft in September 2013, where “BI” implies Business Intelligence). A mixed methods assessment was conducted including a rubric to measure students' effectiveness in applying the entrepreneurial mindset and a metacognitive reflection to better understand student motivation, awareness of learning, and engagement. First, the rubric was applied, and students were categorized by performance group (e.g., high, mid, low). Second, each performance group was analyzed to identify themes within the reflections. Our findings suggest that students in the high‐performing group communicated overall high levels of motivation, while students in the low‐performing group shared overall moderate levels of motivation. The relationship between performance and motivation among students in the mid‐performing group was inconclusive. Findings from our study suggest that there may be a relationship between students' performance and motivation. The key study implications relate to the use of new literacies, such as technological literacy, data literacy, and human literacy, as practices for promoting the development of an entrepreneurial mindset. Our findings suggest that our approach was effective in accomplishing this goal, but there is also room for improvement. Lessons learned and recommendations are provided.
培养自己的创业思维对所有学生都很重要，无论其学科如何。基于证据的决策（有可能降低成本并提高生活质量）是在本科课堂上应用创业思维学习的一种方法。这种方法可以让学生了解与数据相关的趋势，特别是大数据。此外，它还能帮助毕业生更好地评估和确定基于数据科学的有效解决方案。本研究旨在报告一种教学方法，通过使用微软 Power BI Desktop（微软于 2013 年 9 月发布的一款免费工具，其中 "BI "意指商业智能）将循证决策融入工程与技术课堂，从而培养学生的创业思维。我们采用了一种混合方法进行评估，其中包括用于衡量学生应用创业思维有效性的评分标准，以及用于更好地了解学生学习动机、学习意识和参与度的元认知反思。首先，应用评分标准，将学生按成绩组别（如高、中、低）进行分类。其次，对每个成绩组进行分析，以确定反思的主题。我们的研究结果表明，成绩优秀组的学生总体上表现出较高的学习动机，而成绩较差组的学生总体上表现出中等程度的学习动机。成绩中等组学生的成绩与学习动机之间的关系尚无定论。我们的研究结果表明，学生的成绩与学习动机之间可能存在一定的关系。研究的主要意义在于利用新素养，如技术素养、数据素养和人文素养，来促进创业思维的发展。研究结果表明，我们的方法能有效实现这一目标，但也有改进的余地。本文提供了经验教训和建议。
{"title":"Using evidence‐based decision‐making and cognitive apprenticeship approach to develop students' entrepreneurial mindset","authors":"Lisa Bosman, Alejandra Magana","doi":"10.1002/cae.22779","DOIUrl":"https://doi.org/10.1002/cae.22779","url":null,"abstract":"Developing one's entrepreneurial mindset is important for all students, regardless of discipline. Evidence‐based decision‐making (which has the potential to lower costs and improve quality of life) is one approach for applying entrepreneurially minded learning in the undergraduate classroom. This approach allows students to understand trends related to data, in general, and big data, specifically. Furthermore, it better prepares graduates to evaluate and identify effective data science‐based solutions. The purpose of this study is to report on one pedagogical approach to developing the entrepreneurial mindset through integrating evidence‐based decision‐making into the engineering and technology classroom using Microsoft Power BI Desktop (a freely available tool released by Microsoft in September 2013, where “BI” implies Business Intelligence). A mixed methods assessment was conducted including a rubric to measure students' effectiveness in applying the entrepreneurial mindset and a metacognitive reflection to better understand student motivation, awareness of learning, and engagement. First, the rubric was applied, and students were categorized by performance group (e.g., high, mid, low). Second, each performance group was analyzed to identify themes within the reflections. Our findings suggest that students in the high‐performing group communicated overall high levels of motivation, while students in the low‐performing group shared overall moderate levels of motivation. The relationship between performance and motivation among students in the mid‐performing group was inconclusive. Findings from our study suggest that there may be a relationship between students' performance and motivation. The key study implications relate to the use of new literacies, such as technological literacy, data literacy, and human literacy, as practices for promoting the development of an entrepreneurial mindset. Our findings suggest that our approach was effective in accomplishing this goal, but there is also room for improvement. Lessons learned and recommendations are provided.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Ministry of Education of the People's Republic of China (referred to as the Ministry of Education) has issued a series of training plans for outstanding engineers in new engineering disciplines, emphasizing accelerating the digital transformation of education and promoting the cultivation of interdisciplinary talents. The teaching purpose of medical–engineering integration professional courses is to cultivate new engineering talents with interdisciplinary backgrounds in medicine and engineering technology. This article aims to cultivate the comprehensive engineering practical ability of new medical information engineering talents to explore a new model based on the deep integration of Massive, Open, Online, and Course, and Conceive, Design, Implement, and Operate engineering talent training. This model integrates a variety of teaching methods, such as flipped classroom and project teaching, which is more conducive to achieving the talent training goals of cultivating innovative thinking, interdisciplinary thinking, analysis and problem‐solving abilities, and teamwork skills. This study uses the “Introduction to Digital Healthcare” course as an example to carry out the teaching practice of the new model, showing the practicability and effectiveness of this teaching model in cultivating the comprehensive practical literacy of new engineering talents. In summary, the new model proposed in this article can provide a reference for the teaching of medical information engineering professional courses and also provide a new model of thinking for the teaching of medical–engineering integration professional courses.
{"title":"Teaching exploration and practice of new engineering medical–engineering integration professional courses under the background of digital education","authors":"Yue Luo, Shuting Zhao, Chuanbiao Wen","doi":"10.1002/cae.22776","DOIUrl":"https://doi.org/10.1002/cae.22776","url":null,"abstract":"The Ministry of Education of the People's Republic of China (referred to as the Ministry of Education) has issued a series of training plans for outstanding engineers in new engineering disciplines, emphasizing accelerating the digital transformation of education and promoting the cultivation of interdisciplinary talents. The teaching purpose of medical–engineering integration professional courses is to cultivate new engineering talents with interdisciplinary backgrounds in medicine and engineering technology. This article aims to cultivate the comprehensive engineering practical ability of new medical information engineering talents to explore a new model based on the deep integration of Massive, Open, Online, and Course, and Conceive, Design, Implement, and Operate engineering talent training. This model integrates a variety of teaching methods, such as flipped classroom and project teaching, which is more conducive to achieving the talent training goals of cultivating innovative thinking, interdisciplinary thinking, analysis and problem‐solving abilities, and teamwork skills. This study uses the “Introduction to Digital Healthcare” course as an example to carry out the teaching practice of the new model, showing the practicability and effectiveness of this teaching model in cultivating the comprehensive practical literacy of new engineering talents. In summary, the new model proposed in this article can provide a reference for the teaching of medical information engineering professional courses and also provide a new model of thinking for the teaching of medical–engineering integration professional courses.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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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