Gabriel Castelblanco, Laura Cruz‐Castro, Zhenlin Yang
Grading is one of the most relevant hurdles for instructors, diverting instructor's focus on the development of engaging learning activities, class preparation, and attending to students' questions. Institutions and instructors are continuously looking for alternatives to reduce educators' time required on grading, frequently, resulting in hiring teaching assistants whose inexperience and frequent rotation can lead to inconsistent and subjective evaluations. Large Language Models (LLMs) like GPT‐4 may alleviate grading challenges; however, research in this field is limited when dealing with assignments requiring specialized knowledge, complex critical thinking, subjective, and creative. This research investigates whether GPT‐4's scores correlate with human grading in a construction capstone project and how the use of criteria and rubrics in GPT influences this correlation. Projects were graded by two human graders and three training configurations in GPT‐4: no detailed criteria, paraphrased criteria, and explicit rubrics. Each configuration was tested through 10 iterations to evaluate GPT consistency. Results challenge GPT‐4's potential to grade argumentative assignments. GPT‐4's score correlates slightly better (although poor overall) with human evaluations when no additional information is provided, underscoring the poor impact of the specificity of training materials for GPT scoring in this type of assignment. Despite the LLMs' promises, their limitations include variability in consistency and reliance on statistical pattern analysis, which can lead to misleading evaluations along with privacy concerns when handling sensitive student data. Educators must carefully design grading guidelines to harness the full potential of LLMs in academic assessments, balancing AI's efficiency with the need for nuanced human judgment.
{"title":"Performance of a Large‐Language Model in scoring construction management capstone design projects","authors":"Gabriel Castelblanco, Laura Cruz‐Castro, Zhenlin Yang","doi":"10.1002/cae.22796","DOIUrl":"https://doi.org/10.1002/cae.22796","url":null,"abstract":"Grading is one of the most relevant hurdles for instructors, diverting instructor's focus on the development of engaging learning activities, class preparation, and attending to students' questions. Institutions and instructors are continuously looking for alternatives to reduce educators' time required on grading, frequently, resulting in hiring teaching assistants whose inexperience and frequent rotation can lead to inconsistent and subjective evaluations. Large Language Models (LLMs) like GPT‐4 may alleviate grading challenges; however, research in this field is limited when dealing with assignments requiring specialized knowledge, complex critical thinking, subjective, and creative. This research investigates whether GPT‐4's scores correlate with human grading in a construction capstone project and how the use of criteria and rubrics in GPT influences this correlation. Projects were graded by two human graders and three training configurations in GPT‐4: no detailed criteria, paraphrased criteria, and explicit rubrics. Each configuration was tested through 10 iterations to evaluate GPT consistency. Results challenge GPT‐4's potential to grade argumentative assignments. GPT‐4's score correlates slightly better (although poor overall) with human evaluations when no additional information is provided, underscoring the poor impact of the specificity of training materials for GPT scoring in this type of assignment. Despite the LLMs' promises, their limitations include variability in consistency and reliance on statistical pattern analysis, which can lead to misleading evaluations along with privacy concerns when handling sensitive student data. Educators must carefully design grading guidelines to harness the full potential of LLMs in academic assessments, balancing AI's efficiency with the need for nuanced human judgment.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142248859","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}
Computer programming has emerged as an important field in K‐12 science, technology, engineering, and maths (STEM) education in the AI era. However, contemporary programming education is hindered by fragmented course content, high complexity, and difficulties in maintaining engagement, impeding smooth progress. More effective collaborative learning strategies need to be explored. This study constructed jigsaw‐integrated task‐driven learning (jigsaw‐TDL) in a high school Python programming course under a STEM curriculum and verified its teaching effectiveness on students’ learning motivation, computational thinking, collaborative skills, and programming performance both quantitatively and qualitatively. Nighty‐nine high school students were randomly assigned to a jigsaw‐TDL group and a general collaborative task‐driven learning group (collaborative‐TDL). During the experiment, a Python programming course was introduced over 7 weeks. Questionnaires, programming tasks, and semistructured interviews were comprehensively applied to examine students’ learning outcomes. Finally, the jigsaw‐TDL group showed significantly better performance than the collaborative‐TDL group in learning motivation, computational thinking, and collaborative skills. However, it only led to better programming performance in the less complex tasks. The majority of students held a positive attitude toward the jigsaw‐TDL model, acknowledging its benefits in group collaboration, programming knowledge acquisition, and application. This research provides empirical evidence and potential guidance for task organization and collaborative learning support in programming courses and STEM education.
{"title":"Effect of jigsaw‐integrated task‐driven learning on students' motivation, computational thinking, collaborative skills, and programming performance in a high‐school programming course","authors":"Zehui Zhan, Tingting Li, Yaner Ye","doi":"10.1002/cae.22793","DOIUrl":"https://doi.org/10.1002/cae.22793","url":null,"abstract":"Computer programming has emerged as an important field in K‐12 science, technology, engineering, and maths (STEM) education in the AI era. However, contemporary programming education is hindered by fragmented course content, high complexity, and difficulties in maintaining engagement, impeding smooth progress. More effective collaborative learning strategies need to be explored. This study constructed jigsaw‐integrated task‐driven learning (jigsaw‐TDL) in a high school Python programming course under a STEM curriculum and verified its teaching effectiveness on students’ learning motivation, computational thinking, collaborative skills, and programming performance both quantitatively and qualitatively. Nighty‐nine high school students were randomly assigned to a jigsaw‐TDL group and a general collaborative task‐driven learning group (collaborative‐TDL). During the experiment, a Python programming course was introduced over 7 weeks. Questionnaires, programming tasks, and semistructured interviews were comprehensively applied to examine students’ learning outcomes. Finally, the jigsaw‐TDL group showed significantly better performance than the collaborative‐TDL group in learning motivation, computational thinking, and collaborative skills. However, it only led to better programming performance in the less complex tasks. The majority of students held a positive attitude toward the jigsaw‐TDL model, acknowledging its benefits in group collaboration, programming knowledge acquisition, and application. This research provides empirical evidence and potential guidance for task organization and collaborative learning support in programming courses and STEM education.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194074","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}
This paper introduces an instructional framework for process identification, combining theoretical concepts with practical laboratory exercises, focusing particularly on the identification of first‐order‐plus‐time‐delay models. Our methodology emphasizes guiding students through the various stages involved in the system identification process, namely mastering techniques, such as data acquisition and preprocessing, identification and validation stages, and method comparison. The laboratory assignment is structured into three distinct stages: an initial prelab task working with simulated data, the hands‐on work with laboratory equipment, and the assignment report writing and oral presentation. The assessment of students' learning outcomes is conducted using a detailed rubric. Feedback from a focus group interview indicates that the majority of students appreciated the well‐balanced content, highlighting a strong link between theoretical concepts and practical application.
{"title":"Teaching experience for process identification using first‐order‐plus‐time‐delay models","authors":"Nourdine Aliane","doi":"10.1002/cae.22794","DOIUrl":"https://doi.org/10.1002/cae.22794","url":null,"abstract":"This paper introduces an instructional framework for process identification, combining theoretical concepts with practical laboratory exercises, focusing particularly on the identification of first‐order‐plus‐time‐delay models. Our methodology emphasizes guiding students through the various stages involved in the system identification process, namely mastering techniques, such as data acquisition and preprocessing, identification and validation stages, and method comparison. The laboratory assignment is structured into three distinct stages: an initial prelab task working with simulated data, the hands‐on work with laboratory equipment, and the assignment report writing and oral presentation. The assessment of students' learning outcomes is conducted using a detailed rubric. Feedback from a focus group interview indicates that the majority of students appreciated the well‐balanced content, highlighting a strong link between theoretical concepts and practical application.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194077","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}
Nutnicha Nigon, Julie D. Tucker, Thomas W. Ekstedt, Brandon C. Jeong, Dana C. Simionescu, Milo D. Koretsky
As the use of computers in education increases, adaptive learning platforms are becoming more common. However, these adaptive systems are typically designed to support acquisition of declarative knowledge and/or procedural fluency but rarely address conceptual learning. In this work, we developed the Crystallography Adaptive Learning Module (CALM) for materials science to provide students a tool for individualized conceptual learning. We used a randomized quasi‐experimental design comparing two instructional designs with different levels of computer‐provided direction and student agency. Undergraduate students were randomly assigned to one of two different instructional designs; one design had students complete an individualized, adaptive path using the CALM (N = 80), and the other gave students the freedom to explore CALM's learning resources but with limited guidance (N = 85). Within these two designs, we also investigated students among different cumulative grade point average (GPA) groups. While there was no statistically significant difference in the measure of conceptual understanding between instructional designs or among the groups with the same GPA, there is evidence to suggest the CALM improves conceptual understanding of students in the middle GPA group. Students using CALM also showed increased participation with the interactive learning videos compared to the other design. The number of videos watched in each instructional condition aligns with overall academic performance as the low GPA group received the most assigned supplements but watched the least videos by choice. This study provides insight for technology developers on how to develop educational adaptive technology systems that provide a proper level of student agency to promote conceptual understanding in challenging STEM topics.
{"title":"Adaptivity or agency? Educational technology design for conceptual learning of materials science","authors":"Nutnicha Nigon, Julie D. Tucker, Thomas W. Ekstedt, Brandon C. Jeong, Dana C. Simionescu, Milo D. Koretsky","doi":"10.1002/cae.22790","DOIUrl":"https://doi.org/10.1002/cae.22790","url":null,"abstract":"As the use of computers in education increases, adaptive learning platforms are becoming more common. However, these adaptive systems are typically designed to support acquisition of declarative knowledge and/or procedural fluency but rarely address conceptual learning. In this work, we developed the Crystallography Adaptive Learning Module (CALM) for materials science to provide students a tool for individualized conceptual learning. We used a randomized quasi‐experimental design comparing two instructional designs with different levels of computer‐provided direction and student agency. Undergraduate students were randomly assigned to one of two different instructional designs; one design had students complete an individualized, adaptive path using the CALM (<jats:italic>N</jats:italic> = 80), and the other gave students the freedom to explore CALM's learning resources but with limited guidance (<jats:italic>N</jats:italic> = 85). Within these two designs, we also investigated students among different cumulative grade point average (GPA) groups. While there was no statistically significant difference in the measure of conceptual understanding between instructional designs or among the groups with the same GPA, there is evidence to suggest the CALM improves conceptual understanding of students in the middle GPA group. Students using CALM also showed increased participation with the interactive learning videos compared to the other design. The number of videos watched in each instructional condition aligns with overall academic performance as the low GPA group received the most assigned supplements but watched the least videos by choice. This study provides insight for technology developers on how to develop educational adaptive technology systems that provide a proper level of student agency to promote conceptual understanding in challenging STEM topics.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194075","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}
In this conceptual study, we explore the incorporation of computational thinking (CT) within integrated Science, Technology, Engineering, and Mathematics (STEM) education, aiming to enhance the Technological Pedagogical Content Knowledge (TPACK) framework for teacher professional development. Despite the fundamental role of mathematics in K‐16 and engineering education, its theoretical and practical dimensions in a transdisciplinary STEM context and its interlinks with CT remain underexplored. This gap extends to the professional development of teachers in research‐oriented STEM environments, which presents significant challenges. The study aims to address these issues by repositioning cognitive‐adaptive competencies such as CT and design thinking (DT) as a crucial enabler for STEM teacher professional competency, advocating for a move beyond normative approaches. We comprehensively analyze the integration efforts of CT in STEM, which often rely on declarative definitions without substantive practical implications. The study poses questions on (1) how CT can be effectively integrated into STEM, (2) the characteristics of the normative‐adaptive model for teacher education, and (3) the development of a conceptual educational framework focused on mathematical modeling, simulation design, and student engagement in research. Drawing on innovative educational practices, we scrutinize the integration of CT and DT through examples from mathematics, emphasizing the importance of developing computational models and algorithms. Ultimately, we propose a competency‐centered normative‐adaptive‐context aware model of STEM integration (NACAMS)‐TPACK model that enhances the classical TPACK framework by interlinking computational, design, and general pedagogical competencies. This study is particularly relevant for educators, policymakers, and researchers involved in K‐16 STEM and engineering education.
{"title":"Competency‐based TPACK approaches to computational thinking and integrated STEM: A conceptual exploration","authors":"Vladimiras Dolgopolovas, Valentina Dagiene","doi":"10.1002/cae.22788","DOIUrl":"https://doi.org/10.1002/cae.22788","url":null,"abstract":"In this conceptual study, we explore the incorporation of computational thinking (CT) within integrated Science, Technology, Engineering, and Mathematics (STEM) education, aiming to enhance the Technological Pedagogical Content Knowledge (TPACK) framework for teacher professional development. Despite the fundamental role of mathematics in K‐16 and engineering education, its theoretical and practical dimensions in a transdisciplinary STEM context and its interlinks with CT remain underexplored. This gap extends to the professional development of teachers in research‐oriented STEM environments, which presents significant challenges. The study aims to address these issues by repositioning cognitive‐adaptive competencies such as CT and design thinking (DT) as a crucial enabler for STEM teacher professional competency, advocating for a move beyond normative approaches. We comprehensively analyze the integration efforts of CT in STEM, which often rely on declarative definitions without substantive practical implications. The study poses questions on (1) how CT can be effectively integrated into STEM, (2) the characteristics of the normative‐adaptive model for teacher education, and (3) the development of a conceptual educational framework focused on mathematical modeling, simulation design, and student engagement in research. Drawing on innovative educational practices, we scrutinize the integration of CT and DT through examples from mathematics, emphasizing the importance of developing computational models and algorithms. Ultimately, we propose a competency‐centered normative‐adaptive‐context aware model of STEM integration (NACAMS)‐TPACK model that enhances the classical TPACK framework by interlinking computational, design, and general pedagogical competencies. This study is particularly relevant for educators, policymakers, and researchers involved in K‐16 STEM and engineering education.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194076","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}
Serious games can play a crucial role in developing competencies for the job market, offering an innovative and engaging approach to learning. This study uses FLIGBY to develop employability skills among computer engineering graduates. FLIGBY is a serious game that simulates a virtual company environment where players make strategic decisions, emphasizing the principles of flow and positive psychology. Immersion in realistic scenarios provides practical experience, contributing to the development of market‐ready skills. A quantitative methodology based on descriptive statistics and hypothesis testing was adopted to measure the development of competencies for the job market using the Systems Engineering Competency Framework. The results show that competence development occurs mainly in the professional and managerial dimensions. In contrast, there is no development of core and technical competencies. Furthermore, the perception of the development of these competencies occurs mainly for students with more years of professional experience. The experiential nature of FLIGBY allows users to develop practical knowledge, promoting adaptability and resilience. This gamified approach accelerates the learning curve, preparing individuals for real‐world challenges in the computer engineering workplace.
{"title":"FLIGBY for graduates' employability enhancement in Computer Engineering","authors":"Fernando Almeida, Zoltan Buzady","doi":"10.1002/cae.22789","DOIUrl":"https://doi.org/10.1002/cae.22789","url":null,"abstract":"Serious games can play a crucial role in developing competencies for the job market, offering an innovative and engaging approach to learning. This study uses FLIGBY to develop employability skills among computer engineering graduates. FLIGBY is a serious game that simulates a virtual company environment where players make strategic decisions, emphasizing the principles of flow and positive psychology. Immersion in realistic scenarios provides practical experience, contributing to the development of market‐ready skills. A quantitative methodology based on descriptive statistics and hypothesis testing was adopted to measure the development of competencies for the job market using the Systems Engineering Competency Framework. The results show that competence development occurs mainly in the professional and managerial dimensions. In contrast, there is no development of core and technical competencies. Furthermore, the perception of the development of these competencies occurs mainly for students with more years of professional experience. The experiential nature of FLIGBY allows users to develop practical knowledge, promoting adaptability and resilience. This gamified approach accelerates the learning curve, preparing individuals for real‐world challenges in the computer engineering workplace.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194078","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}
There are several initiatives underway to improve the learning of software developers. These attempts include the integration of GitHub into software engineering classes, the creation of learning management systems, gamification approaches, and collaborative learning platforms. These initiatives have demonstrated promise in boosting students' collaborative growth and cooperation abilities, emphasizing their potential influence on improving learning experiences in practical areas. Books, on the other hand, remain basic in education, but their physical size limits their ability to explore all practical elements of a topic in depth. This limitation requires more research and application of theoretical information in real‐world circumstances. In this work, we address the issue of limited space in traditional books that frequently prevents complete presentation of practical elements of a topic. To address this issue, we propose an application that improves the reading experience and accelerates the learning process. To anticipate themes, we use a combination of latent Dirichlet allocation (LDA) algorithms and a generative pre‐trained transformer. First, utilizing LDA to find potential topic keywords inside the text and then leveraging generative pretrained transformer to predict topic names based on the LDA produced keywords. In addition, a query builder module produces and executes queries depending on the current page's topic, obtaining real‐world issues from Stack Overflow. The system classifies results by query‐title similarity, question‐answer ranking, and content quality before displaying them to users. This bridges the gap between theoretical knowledge and practical application. We illustrate the usefulness of suggested tool using simulations, comparison with existing tools and user studies. The majority of users provide favorable comments and find it interesting and helpful for improving the learning process.
{"title":"Learning beyond books: A hybrid model to learn real‐world problems","authors":"Zeeshan Anwar, Hammad Afzal, Naima Iltaf","doi":"10.1002/cae.22792","DOIUrl":"https://doi.org/10.1002/cae.22792","url":null,"abstract":"There are several initiatives underway to improve the learning of software developers. These attempts include the integration of GitHub into software engineering classes, the creation of learning management systems, gamification approaches, and collaborative learning platforms. These initiatives have demonstrated promise in boosting students' collaborative growth and cooperation abilities, emphasizing their potential influence on improving learning experiences in practical areas. Books, on the other hand, remain basic in education, but their physical size limits their ability to explore all practical elements of a topic in depth. This limitation requires more research and application of theoretical information in real‐world circumstances. In this work, we address the issue of limited space in traditional books that frequently prevents complete presentation of practical elements of a topic. To address this issue, we propose an application that improves the reading experience and accelerates the learning process. To anticipate themes, we use a combination of latent Dirichlet allocation (LDA) algorithms and a generative pre‐trained transformer. First, utilizing LDA to find potential topic keywords inside the text and then leveraging generative pretrained transformer to predict topic names based on the LDA produced keywords. In addition, a query builder module produces and executes queries depending on the current page's topic, obtaining real‐world issues from Stack Overflow. The system classifies results by query‐title similarity, question‐answer ranking, and content quality before displaying them to users. This bridges the gap between theoretical knowledge and practical application. We illustrate the usefulness of suggested tool using simulations, comparison with existing tools and user studies. The majority of users provide favorable comments and find it interesting and helpful for improving the learning process.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193875","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 world around us is evolving due to Industrial Revolution 4.0. This revolution is driving the present‐day education, that is, Education 3.0–creative education towards Education 4.0, that is, industry‐centric education. The present academic environment in India is in the process of developing an educational ecosystem, which will incorporate equal access to education for all stakeholders and promote critical thinking, digital literacy, global awareness and scientific reasoning to name a few. Accordingly, it has become prudent to develop and validate a mechanism for assessing the various steps being taken to realign students to Education 4.0. This study involved 300 undergraduate, postgraduate, and PhD students from eight different schools across diverse program and course outcomes, who were asked to respond to a questionnaire on their own accord. The Pearson product‐moment correlation test was used to statistically analyse the obtained responses. The test results showed that the questions were logical and valid, with a significance level of 0.0115. Furthermore, the reliability of the mechanism was also assessed using Cronbach's α score. This study indicated that three aspects—learning preferences, contribution of learning to knowledge, skills, and personal development, and time spent on learning and nonacademic activities—exhibited high reliability However, two aspects—the emphasis on mental functions and reading and writing—indicated to be of moderate reliability. Thus, it could be said that the questionnaire developed in this study was both reliable and valid for assessing the mechanism being undertaken to realign students to Education 4.0.
{"title":"New way to shaping India's academic environment: An assessment of the mechanism of integration of education 4.0 to industry‐centric learning","authors":"Shwetambara Verma, Somesh Sengupta","doi":"10.1002/cae.22791","DOIUrl":"https://doi.org/10.1002/cae.22791","url":null,"abstract":"The world around us is evolving due to Industrial Revolution 4.0. This revolution is driving the present‐day education, that is, Education 3.0–creative education towards Education 4.0, that is, industry‐centric education. The present academic environment in India is in the process of developing an educational ecosystem, which will incorporate equal access to education for all stakeholders and promote critical thinking, digital literacy, global awareness and scientific reasoning to name a few. Accordingly, it has become prudent to develop and validate a mechanism for assessing the various steps being taken to realign students to Education 4.0. This study involved 300 undergraduate, postgraduate, and PhD students from eight different schools across diverse program and course outcomes, who were asked to respond to a questionnaire on their own accord. The Pearson product‐moment correlation test was used to statistically analyse the obtained responses. The test results showed that the questions were logical and valid, with a significance level of 0.0115. Furthermore, the reliability of the mechanism was also assessed using Cronbach's <jats:italic>α</jats:italic> score. This study indicated that three aspects—learning preferences, contribution of learning to knowledge, skills, and personal development, and time spent on learning and nonacademic activities—exhibited high reliability However, two aspects—the emphasis on mental functions and reading and writing—indicated to be of moderate reliability. Thus, it could be said that the questionnaire developed in this study was both reliable and valid for assessing the mechanism being undertaken to realign students to Education 4.0.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194079","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}
Fausto A. Valenzuela‐Domínguez, Laura F. Munguía‐Martínez, Sofía Meléndez‐Tiznado, Zuneth Guzmán‐Limón, Victor H. Benitez, Luis C. Félix‐Herrán
This manuscript proposes a methodology for teaching undergraduate engineering students fundamental concepts of artificial intelligence and robotics applicable in Industry 4.0 using a project‐based learning strategy. A low‐cost prototype of a two‐degrees‐of‐freedom robotic arm was designed and implemented for monitoring applications, integrating the Internet of Things and artificial intelligence technology. Inverse and direct kinematics are applied in the robotic arm design, enabling the execution of desired trajectories by the robot's end effector. The robot, capable of following user‐programmed trajectories, uses two servo motors with a 180° mobility range, integrated into a 3D printed structure made from polylactic materials, whereas the programmable logic was accomplished using an ESP32 microcontroller. Furthermore, the robot is controlled through a MATLAB GUI (Graphical User Interface), designed to obtain detailed process information such as activity status, trajectory type, and quality. The results demonstrate the feasibility of integrating various cutting‐edge technologies to teach fundamental concepts of Industry 4.0. The proposed methodology could allow educators to design a robotics course where students are motivated by practical experience implementing impactful technologies beyond the academic realm.
{"title":"Design of a two‐degrees‐of‐freedom robotic arm for monitoring applications for teaching robotics and artificial intelligence technology","authors":"Fausto A. Valenzuela‐Domínguez, Laura F. Munguía‐Martínez, Sofía Meléndez‐Tiznado, Zuneth Guzmán‐Limón, Victor H. Benitez, Luis C. Félix‐Herrán","doi":"10.1002/cae.22786","DOIUrl":"https://doi.org/10.1002/cae.22786","url":null,"abstract":"This manuscript proposes a methodology for teaching undergraduate engineering students fundamental concepts of artificial intelligence and robotics applicable in Industry 4.0 using a project‐based learning strategy. A low‐cost prototype of a two‐degrees‐of‐freedom robotic arm was designed and implemented for monitoring applications, integrating the Internet of Things and artificial intelligence technology. Inverse and direct kinematics are applied in the robotic arm design, enabling the execution of desired trajectories by the robot's end effector. The robot, capable of following user‐programmed trajectories, uses two servo motors with a 180° mobility range, integrated into a 3D printed structure made from polylactic materials, whereas the programmable logic was accomplished using an ESP32 microcontroller. Furthermore, the robot is controlled through a MATLAB GUI (Graphical User Interface), designed to obtain detailed process information such as activity status, trajectory type, and quality. The results demonstrate the feasibility of integrating various cutting‐edge technologies to teach fundamental concepts of Industry 4.0. The proposed methodology could allow educators to design a robotics course where students are motivated by practical experience implementing impactful technologies beyond the academic realm.","PeriodicalId":50643,"journal":{"name":"Computer Applications in Engineering Education","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193877","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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