Contribution: An innovative approach utilizing interactive software paired with stereoscopic projection hardware is introduced to enhance the teaching and learning of solid-state physics. This method is distinctive for its integration of complex 3-D visualizations directly into classroom instruction, facilitating a deeper understanding of abstract concepts through interactive engagement.Background: This study is motivated by the challenges faced in teaching abstract concepts in a solid-state physics course, such as the energy band theory and Bravais lattices, to undergraduate students. Traditional teaching methods, such as blackboard teaching, or PowerPoint presentations often fail to adequately address these complexities, leading to a significant learning gap. This gap underlines the necessity for innovative educational tools that can bridge theoretical knowledge with practical understanding, applicable globally across educational programs.Intended Outcomes: The primary outcomes targeted by this approach include improved student engagement and learning efficacy, enhanced comprehension and retention of complex physics concepts, and better transfer of theoretical knowledge to practical applications.Application and Evaluation Experiment Design: The teaching tool integrates MATLAB-based interactive software with hardware utilizing the “Pepper Ghost” technique for 3-D stereoscopic visualization. This approach aims to foster an interactive and engaging learning environment, allowing complex physics concepts to be visualized intuitively. Assessment of learning effectiveness is carried out through the design of targeted questions, participant recruitment, and statistical analysis of questionnaire responses.Findings: Noticeable improvements in performance on both retention questions and transfer questions are observed, indicating that students exposed to this new teaching approach benefit in knowledge retention and application compared to those who experienced traditional teaching methods. These findings highlight the effectiveness of the integrated teaching tool in enhancing teaching outcomes in physics, suggesting its potential broad applicability in other fields.
{"title":"Elevating Learning Effectiveness in Solid-State Physics Through Interactive Software and Stereoscopic Projection","authors":"Xuhan Luo;Boxuan Li;Jinmei Liu;Shihong Ma;Xinyuan Wei;Yan Cen","doi":"10.1109/TE.2025.3545696","DOIUrl":"https://doi.org/10.1109/TE.2025.3545696","url":null,"abstract":"Contribution: An innovative approach utilizing interactive software paired with stereoscopic projection hardware is introduced to enhance the teaching and learning of solid-state physics. This method is distinctive for its integration of complex 3-D visualizations directly into classroom instruction, facilitating a deeper understanding of abstract concepts through interactive engagement.Background: This study is motivated by the challenges faced in teaching abstract concepts in a solid-state physics course, such as the energy band theory and Bravais lattices, to undergraduate students. Traditional teaching methods, such as blackboard teaching, or PowerPoint presentations often fail to adequately address these complexities, leading to a significant learning gap. This gap underlines the necessity for innovative educational tools that can bridge theoretical knowledge with practical understanding, applicable globally across educational programs.Intended Outcomes: The primary outcomes targeted by this approach include improved student engagement and learning efficacy, enhanced comprehension and retention of complex physics concepts, and better transfer of theoretical knowledge to practical applications.Application and Evaluation Experiment Design: The teaching tool integrates MATLAB-based interactive software with hardware utilizing the “Pepper Ghost” technique for 3-D stereoscopic visualization. This approach aims to foster an interactive and engaging learning environment, allowing complex physics concepts to be visualized intuitively. Assessment of learning effectiveness is carried out through the design of targeted questions, participant recruitment, and statistical analysis of questionnaire responses.Findings: Noticeable improvements in performance on both retention questions and transfer questions are observed, indicating that students exposed to this new teaching approach benefit in knowledge retention and application compared to those who experienced traditional teaching methods. These findings highlight the effectiveness of the integrated teaching tool in enhancing teaching outcomes in physics, suggesting its potential broad applicability in other fields.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"224-233"},"PeriodicalIF":2.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856284","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: The integration of artificial intelligence (AI) in engineering higher education is becoming increasingly important nowadays. This article contributes to the Scholarship of Integration by providing a comprehensive review of current research on AI integration in engineering higher education and presenting a pilot AI introductory module designed to teach engineering students AI fundamentals. Background: With the rapid development of AI, it is crucial to integrate AI into engineering curricula to prepare students for the workforce. However, there is a lack of comprehensive research on the strategies to integrate AI into engineering higher education. Research Questions (RQs): This article addresses the following RQs: What is the current state of AI integration in engineering higher education? What are the key considerations for integrating AI education into undergraduate engineering programs? What are the challenges and lessons learned when delivering an AI module to undergraduate students majoring in electronics? Methodology: A comprehensive review was conducted to identify current research on pedagogical methods for integrating AI in engineering curricula. A pilot AI introductory module was also developed and implemented based on this comprehensive review. To customize module design for U.K. students, data was collected from a program review of 29 universities in the U.K. to understand the platforms used to deliver these programs. Finally, surveys were used to evaluate the impact of this module and to identify any challenges and lessons learned. Findings: Our comprehensive review revealed a lack of comprehensive research on AI integration in engineering higher education. The program review results showed that 29 universities in the U.K. offer AI and engineering-related knowledge in the same curriculum, among which London leads the trend. Following the review, an AI module was developed and delivered to 150 U.K. first-year electronics and electrical engineering students. The module was evaluated via entry and exit surveys that were completed by 114 and 104 students, respectively. The results suggested that the pilot AI module aids in teaching AI fundamentals to undergraduate engineering students, with 97% of students agreeing that the module can increase their future job competencies. The review and developed module can serve as valuable references for introducing AI into existing engineering programs at the undergraduate level.
{"title":"Integrating AI in Engineering Education: A Comprehensive Review and Student-Informed Module Design for U.K. Students","authors":"Yijia Hao;Yushi Liu;Bo Liu;George Amarantidis;Rami Ghannam","doi":"10.1109/TE.2025.3536105","DOIUrl":"https://doi.org/10.1109/TE.2025.3536105","url":null,"abstract":"Contribution: The integration of artificial intelligence (AI) in engineering higher education is becoming increasingly important nowadays. This article contributes to the Scholarship of Integration by providing a comprehensive review of current research on AI integration in engineering higher education and presenting a pilot AI introductory module designed to teach engineering students AI fundamentals. Background: With the rapid development of AI, it is crucial to integrate AI into engineering curricula to prepare students for the workforce. However, there is a lack of comprehensive research on the strategies to integrate AI into engineering higher education. Research Questions (RQs): This article addresses the following RQs: What is the current state of AI integration in engineering higher education? What are the key considerations for integrating AI education into undergraduate engineering programs? What are the challenges and lessons learned when delivering an AI module to undergraduate students majoring in electronics? Methodology: A comprehensive review was conducted to identify current research on pedagogical methods for integrating AI in engineering curricula. A pilot AI introductory module was also developed and implemented based on this comprehensive review. To customize module design for U.K. students, data was collected from a program review of 29 universities in the U.K. to understand the platforms used to deliver these programs. Finally, surveys were used to evaluate the impact of this module and to identify any challenges and lessons learned. Findings: Our comprehensive review revealed a lack of comprehensive research on AI integration in engineering higher education. The program review results showed that 29 universities in the U.K. offer AI and engineering-related knowledge in the same curriculum, among which London leads the trend. Following the review, an AI module was developed and delivered to 150 U.K. first-year electronics and electrical engineering students. The module was evaluated via entry and exit surveys that were completed by 114 and 104 students, respectively. The results suggested that the pilot AI module aids in teaching AI fundamentals to undergraduate engineering students, with 97% of students agreeing that the module can increase their future job competencies. The review and developed module can serve as valuable references for introducing AI into existing engineering programs at the undergraduate level.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"173-185"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856215","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}
Henrique Mohallem Paiva;Flávia Maria Santoro;Victor Takashi Hayashi;Bianca Cassemiro Lima
Contribution: This article analyzes student assessment within a computing faculty employing a full project-based learning (PBL) approach. Examining 2078 final grades across 60 classes and periods, the study reveals a significant correlation between graded self-studies, exams, and projects. This result contributes to understanding the reliability and independence of diverse evaluation methods, emphasizing their effectiveness in measuring students’ learning within a PBL framework. Background: This study is motivated by the need to investigate diverse learning assessment methods within a PBL setting, aiming to establish context and underline the broad applicability of a comprehensive evaluation approach in computing education. Intended Outcomes: The study seeks to provide information about the interrelationships between different assessment methods within a PBL framework. The desired outcome is a comprehensive understanding applicable across various classes and periods, emphasizing the robustness of the assessment system. Application Design: Implementing a full PBL model in a computing faculty during a two-year period, the chosen approach integrates graded self-studies, exams, and projects to ensure a well-rounded evaluation system, capturing student proficiency across diverse computing concepts. This study involved 210 first- and second-year students, 162 men and 48 women, with a mean age of 21.7 ± 2.7 years. Findings: Correlation analysis of 2078 final grades indicates consistent reliability among graded self-studies, exams, and projects. This result underscores the effectiveness of each assessment method in measuring learning within a PBL framework, highlighting the system’s robust applicability in a computing educational environment.
{"title":"PBL Student Assessment: Consistency of Different Evaluation Methods in a Computing Faculty","authors":"Henrique Mohallem Paiva;Flávia Maria Santoro;Victor Takashi Hayashi;Bianca Cassemiro Lima","doi":"10.1109/TE.2025.3545314","DOIUrl":"https://doi.org/10.1109/TE.2025.3545314","url":null,"abstract":"Contribution: This article analyzes student assessment within a computing faculty employing a full project-based learning (PBL) approach. Examining 2078 final grades across 60 classes and periods, the study reveals a significant correlation between graded self-studies, exams, and projects. This result contributes to understanding the reliability and independence of diverse evaluation methods, emphasizing their effectiveness in measuring students’ learning within a PBL framework. Background: This study is motivated by the need to investigate diverse learning assessment methods within a PBL setting, aiming to establish context and underline the broad applicability of a comprehensive evaluation approach in computing education. Intended Outcomes: The study seeks to provide information about the interrelationships between different assessment methods within a PBL framework. The desired outcome is a comprehensive understanding applicable across various classes and periods, emphasizing the robustness of the assessment system. Application Design: Implementing a full PBL model in a computing faculty during a two-year period, the chosen approach integrates graded self-studies, exams, and projects to ensure a well-rounded evaluation system, capturing student proficiency across diverse computing concepts. This study involved 210 first- and second-year students, 162 men and 48 women, with a mean age of 21.7 ± 2.7 years. Findings: Correlation analysis of 2078 final grades indicates consistent reliability among graded self-studies, exams, and projects. This result underscores the effectiveness of each assessment method in measuring learning within a PBL framework, highlighting the system’s robust applicability in a computing educational environment.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"215-223"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10924470","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856325","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}
Contribution: Expand the scope of factors influencing self-efficacy and highlight the importance of teaching quality, peer support, perceived course value, the moderating effects of self-regulation, and adversity quotient (AQ). Background: Self-efficacy has been regarded as an important factor in students’ learning performance. However, little research has explored the antecedents of self-efficacy in the context of students’ learning computer programming. Research Question: What are the factors affecting students’ self-efficacy in the context of learning computer programming? And how do these factors influence students’ self-efficacy in learning computer programming?Methodology: Five hundred and twenty-three validated questionnaires were collected from four universities in Taiwan. Findings: Three antecedents (the quality of lectures, reciprocal peer tutoring, and perceived course value) positively affected self-efficacy. Two moderators (self-regulation and AQ) positively moderated the relationships between the quality of lectures, reciprocal peer tutoring, and self-efficacy but not the relationship between perceived course value and self-efficacy.
{"title":"Exploring the Antecedents and Moderators of Impacting Self-Efficacy in Students’ Learning Computer Programming","authors":"Ying-Chieh Liu;Hung-Yi Chen","doi":"10.1109/TE.2025.3540493","DOIUrl":"https://doi.org/10.1109/TE.2025.3540493","url":null,"abstract":"Contribution: Expand the scope of factors influencing self-efficacy and highlight the importance of teaching quality, peer support, perceived course value, the moderating effects of self-regulation, and adversity quotient (AQ). Background: Self-efficacy has been regarded as an important factor in students’ learning performance. However, little research has explored the antecedents of self-efficacy in the context of students’ learning computer programming. Research Question: What are the factors affecting students’ self-efficacy in the context of learning computer programming? And how do these factors influence students’ self-efficacy in learning computer programming?Methodology: Five hundred and twenty-three validated questionnaires were collected from four universities in Taiwan. Findings: Three antecedents (the quality of lectures, reciprocal peer tutoring, and perceived course value) positively affected self-efficacy. Two moderators (self-regulation and AQ) positively moderated the relationships between the quality of lectures, reciprocal peer tutoring, and self-efficacy but not the relationship between perceived course value and self-efficacy.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"203-214"},"PeriodicalIF":2.1,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856180","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 enhances the understanding of the factors that impact academic performance and self-efficacy among computer science (CS) students, specifically focusing on gender differences. Background: The motivation behind this study stems from the gender disparity observed within undergraduate CS programs. This gender gap undermines diversity within the tech industry and hampers its potential for innovation. Research Questions: Do female CS students exhibit lower levels of school belonging and academic self-efficacy? Are there gender differences in academic achievement? What is the predictive power of prior academic performance, academic self-efficacy, and sense of belonging on first-semester grades? Methodology: In this study, 113 undergraduate students (82 males) were surveyed. The questionnaire was administered midway through the semester, in early November. High school math exam scores were assessed along with self-reported measures of academic self-efficacy and sense of belonging. Findings: The findings revealed several noteworthy observations: Female students exhibited a statistically significant higher grade point average (GPA) at the end of the first semester despite reporting lower levels of academic self-efficacy. Regression analysis identified gender, academic self-efficacy, and high school math exam scores as significant predictors of first-semester GPA. Implications: The implications of the study underscore the importance of fostering a supportive learning environment within CS education. Specifically, this study advocates for implementing teaching practices that prioritize social aspects and help enhance field-specific self-efficacy for all students.
{"title":"Women in Computer Science: High School Math Exam, School Belonging, Academic Self-Efficacy, and Their Relationship to First-Semester Grades","authors":"Mirjam Paales;Karin Täht","doi":"10.1109/TE.2025.3538949","DOIUrl":"https://doi.org/10.1109/TE.2025.3538949","url":null,"abstract":"Contribution: This study enhances the understanding of the factors that impact academic performance and self-efficacy among computer science (CS) students, specifically focusing on gender differences. Background: The motivation behind this study stems from the gender disparity observed within undergraduate CS programs. This gender gap undermines diversity within the tech industry and hampers its potential for innovation. Research Questions: Do female CS students exhibit lower levels of school belonging and academic self-efficacy? Are there gender differences in academic achievement? What is the predictive power of prior academic performance, academic self-efficacy, and sense of belonging on first-semester grades? Methodology: In this study, 113 undergraduate students (82 males) were surveyed. The questionnaire was administered midway through the semester, in early November. High school math exam scores were assessed along with self-reported measures of academic self-efficacy and sense of belonging. Findings: The findings revealed several noteworthy observations: Female students exhibited a statistically significant higher grade point average (GPA) at the end of the first semester despite reporting lower levels of academic self-efficacy. Regression analysis identified gender, academic self-efficacy, and high school math exam scores as significant predictors of first-semester GPA. Implications: The implications of the study underscore the importance of fostering a supportive learning environment within CS education. Specifically, this study advocates for implementing teaching practices that prioritize social aspects and help enhance field-specific self-efficacy for all students.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"195-202"},"PeriodicalIF":2.1,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856324","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 examines the impact of student data and behaviors on student evaluations of teaching. It leverages campus data and employs statistical methods to explore the relationships among these indicators. A regression model is developed that integrates teaching evaluation, expected grades, and course participation, aiming to mitigate instructors’ influence on student evaluations.Background: In higher education, the assessment of teaching quality commonly includes student evaluations of teaching. However, subjective factors, such as students’ expected grades, can distort evaluation outcomes. The ample student behavior data on campus enable an analysis of the validity of student evaluations on teaching.Research Questions: How do student evaluations of teaching correlate with student grades, library borrowing, and dormitory living? How can campus data analysis be utilized to mitigate the influence of instructors on student evaluations of teaching?Methodology: Data collected from campus are utilized, and statistical methods, including the Shapiro-Wilk test and linear regression models, are applied to analyze the relationships between student data and teaching evaluations.Findings: The study finds a strong correlation between students’ expected grades and teaching evaluation scores, suggesting the potential for instructor influence. The proposed regression model highlights the interrelationships among teaching evaluations, expected grades, and course participation, offering insights into mitigating instructor influence on student evaluations.
{"title":"Enhancing Teaching Evaluations Through Campus Data","authors":"Ruizhi Liao;Zhizhen Chen;Ao Zhang","doi":"10.1109/TE.2025.3536301","DOIUrl":"https://doi.org/10.1109/TE.2025.3536301","url":null,"abstract":"Contribution: This study examines the impact of student data and behaviors on student evaluations of teaching. It leverages campus data and employs statistical methods to explore the relationships among these indicators. A regression model is developed that integrates teaching evaluation, expected grades, and course participation, aiming to mitigate instructors’ influence on student evaluations.Background: In higher education, the assessment of teaching quality commonly includes student evaluations of teaching. However, subjective factors, such as students’ expected grades, can distort evaluation outcomes. The ample student behavior data on campus enable an analysis of the validity of student evaluations on teaching.Research Questions: How do student evaluations of teaching correlate with student grades, library borrowing, and dormitory living? How can campus data analysis be utilized to mitigate the influence of instructors on student evaluations of teaching?Methodology: Data collected from campus are utilized, and statistical methods, including the Shapiro-Wilk test and linear regression models, are applied to analyze the relationships between student data and teaching evaluations.Findings: The study finds a strong correlation between students’ expected grades and teaching evaluation scores, suggesting the potential for instructor influence. The proposed regression model highlights the interrelationships among teaching evaluations, expected grades, and course participation, offering insights into mitigating instructor influence on student evaluations.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 2","pages":"186-194"},"PeriodicalIF":2.1,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143856292","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}
{"title":"IEEE Transactions on Education Information for Authors","authors":"","doi":"10.1109/TE.2025.3530662","DOIUrl":"https://doi.org/10.1109/TE.2025.3530662","url":null,"abstract":"","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"C3-C3"},"PeriodicalIF":2.1,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10876440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360877","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}
Imre Kocsis;Sándor Hajdu;Róbert Mikuska;Péter Korondi
We have introduced a novel approach to competency-based education in mechatronics from the undergraduate to the postgraduate level. What distinguishes this approach is the integration of modeling and control of sampled systems right from the beginning of the undergraduate education. It is achieved by changing the structure of the first-semester Calculus course to focus on discrete-time systems: emphasizing numerical differentiation and integration and difference equations. The curriculum is enriched by interdisciplinary homework project assignments that, given in subsequent courses throughout the education, are tied to the same quarter-vehicle model but vary in theoretical complexity. It demonstrates multiple dimensions of the same engineering problem, leading to a deeper understanding. Based on the discrete-time modeling studied in Calculus, students can solve the problem at a basic level and verify the results with measurements. Later, they can compare these solutions with those obtained using more advanced tools. This approach creates a synergy between different subjects ranging from the basics to the advanced control theory. This article focuses primarily on the mathematical toolkit that facilitates the achievement of our didactic goals.
{"title":"Introduction to the Mathematics of Control Education in Calculus for Engineering Students","authors":"Imre Kocsis;Sándor Hajdu;Róbert Mikuska;Péter Korondi","doi":"10.1109/TE.2024.3520590","DOIUrl":"https://doi.org/10.1109/TE.2024.3520590","url":null,"abstract":"We have introduced a novel approach to competency-based education in mechatronics from the undergraduate to the postgraduate level. What distinguishes this approach is the integration of modeling and control of sampled systems right from the beginning of the undergraduate education. It is achieved by changing the structure of the first-semester Calculus course to focus on discrete-time systems: emphasizing numerical differentiation and integration and difference equations. The curriculum is enriched by interdisciplinary homework project assignments that, given in subsequent courses throughout the education, are tied to the same quarter-vehicle model but vary in theoretical complexity. It demonstrates multiple dimensions of the same engineering problem, leading to a deeper understanding. Based on the discrete-time modeling studied in Calculus, students can solve the problem at a basic level and verify the results with measurements. Later, they can compare these solutions with those obtained using more advanced tools. This approach creates a synergy between different subjects ranging from the basics to the advanced control theory. This article focuses primarily on the mathematical toolkit that facilitates the achievement of our didactic goals.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"163-172"},"PeriodicalIF":2.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360879","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}
Michal Balberg;Hen Friman;Heftsi Ragones;Ifaa Baner;Revital Shechter;Gila Kurtz
Contribution: This study demonstrates the effectiveness of a dedicated soft skills (SSs) course in an electrical engineering (EE) undergraduate program, showing improvements in students’ appreciation and satisfaction of expressing most of these skills.Background: SSs, encompassing interpersonal and social competencies, are important for career success in engineering. However, these skills are often overlooked or only indirectly addressed in EE curricula. This study addresses the need for intentional SSs development in EE education, with potential implications for engineering programs worldwide.Research Questions: How does a dedicated SSs course affect EE students’ perception of the importance of these skills?Does such a course improve students’ satisfaction with their ability to express these skills?Methodology: A dedicated SSs course was designed and implemented for undergraduate EE students. The course explicitly focused on developing teamwork, time management, written and oral communication, and implicitly addressed several other skills. Students’ perceptions of the importance of SSs and their satisfaction with expressing these skills were assessed at the beginning and end of the course using a questionnaire.Findings: While students recognized the importance of SSs before the course, their appreciation for these skills’ contribution to job-seeking and career success increased after completing the course. More significantly, students reported higher levels of satisfaction in expressing several of the targeted skills by the end of the course. These results, though limited to a single course at one institution, suggest the value of incorporating dedicated SSs development into EE curricula.
{"title":"Soft Skills Education is Valuable—Perception of Engineering Students","authors":"Michal Balberg;Hen Friman;Heftsi Ragones;Ifaa Baner;Revital Shechter;Gila Kurtz","doi":"10.1109/TE.2024.3510569","DOIUrl":"https://doi.org/10.1109/TE.2024.3510569","url":null,"abstract":"Contribution: This study demonstrates the effectiveness of a dedicated soft skills (SSs) course in an electrical engineering (EE) undergraduate program, showing improvements in students’ appreciation and satisfaction of expressing most of these skills.Background: SSs, encompassing interpersonal and social competencies, are important for career success in engineering. However, these skills are often overlooked or only indirectly addressed in EE curricula. This study addresses the need for intentional SSs development in EE education, with potential implications for engineering programs worldwide.Research Questions: How does a dedicated SSs course affect EE students’ perception of the importance of these skills?Does such a course improve students’ satisfaction with their ability to express these skills?Methodology: A dedicated SSs course was designed and implemented for undergraduate EE students. The course explicitly focused on developing teamwork, time management, written and oral communication, and implicitly addressed several other skills. Students’ perceptions of the importance of SSs and their satisfaction with expressing these skills were assessed at the beginning and end of the course using a questionnaire.Findings: While students recognized the importance of SSs before the course, their appreciation for these skills’ contribution to job-seeking and career success increased after completing the course. More significantly, students reported higher levels of satisfaction in expressing several of the targeted skills by the end of the course. These results, though limited to a single course at one institution, suggest the value of incorporating dedicated SSs development into EE curricula.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"152-162"},"PeriodicalIF":2.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360878","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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