Md. Yunus Naseri;Caitlin Snyder;Katherine X. Pérez-Rivera;Sambridhi Bhandari;Habtamu Alemu Workneh;Niroj Aryal;Gautam Biswas;Erin C. Henrick;Erin R. Hotchkiss;Manoj K. Jha;Steven Jiang;Emily C. Kern;Vinod K. Lohani;Landon T. Marston;Christopher P. Vanags;Kang Xia
Contribution: This article discusses a research-practice partnership (RPP) where instructors from six undergraduate courses in three universities developed data science modules tailored to the needs of their respective disciplines, academic levels, and pedagogies. Background: STEM disciplines at universities are incorporating data science topics to meet employer demands for data science-savvy graduates. Integrating these topics into regular course materials can benefit students and instructors. However, instructors encounter challenges in integrating data science instruction into their course schedules. Research Questions: How did instructors from multiple engineering and science disciplines working in an RPP integrate data science into their undergraduate courses? Methodology: A multiple case study approach, with each course as a unit of analysis, was used to identify data science topics and integration approaches. Findings: Instructors designed their modules to meet specific course needs, utilizing them as primary or supplementary learning tools based on their course structure and pedagogy. They selected a subset of discipline-agnostic data science topics, such as generating and interpreting visualizations and conducting basic statistical analyses. Although instructors faced challenges due to varying data science skills of their students, they valued the control they had in integrating data science content into their courses. They were uncertain about whether the modules could be adopted for use by other instructors, specifically by those outside of their discipline, but they all believed the approach for developing and integrating data science could be adapted to student needs in different situations.
{"title":"Integrating Data Science Into Undergraduate Science and Engineering Courses: Lessons Learned by Instructors in a Multiuniversity Research-Practice Partnership","authors":"Md. Yunus Naseri;Caitlin Snyder;Katherine X. Pérez-Rivera;Sambridhi Bhandari;Habtamu Alemu Workneh;Niroj Aryal;Gautam Biswas;Erin C. Henrick;Erin R. Hotchkiss;Manoj K. Jha;Steven Jiang;Emily C. Kern;Vinod K. Lohani;Landon T. Marston;Christopher P. Vanags;Kang Xia","doi":"10.1109/TE.2024.3436041","DOIUrl":"10.1109/TE.2024.3436041","url":null,"abstract":"Contribution: This article discusses a research-practice partnership (RPP) where instructors from six undergraduate courses in three universities developed data science modules tailored to the needs of their respective disciplines, academic levels, and pedagogies. Background: STEM disciplines at universities are incorporating data science topics to meet employer demands for data science-savvy graduates. Integrating these topics into regular course materials can benefit students and instructors. However, instructors encounter challenges in integrating data science instruction into their course schedules. Research Questions: How did instructors from multiple engineering and science disciplines working in an RPP integrate data science into their undergraduate courses? Methodology: A multiple case study approach, with each course as a unit of analysis, was used to identify data science topics and integration approaches. Findings: Instructors designed their modules to meet specific course needs, utilizing them as primary or supplementary learning tools based on their course structure and pedagogy. They selected a subset of discipline-agnostic data science topics, such as generating and interpreting visualizations and conducting basic statistical analyses. Although instructors faced challenges due to varying data science skills of their students, they valued the control they had in integrating data science content into their courses. They were uncertain about whether the modules could be adopted for use by other instructors, specifically by those outside of their discipline, but they all believed the approach for developing and integrating data science could be adapted to student needs in different situations.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"1-12"},"PeriodicalIF":2.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666964","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218940","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}
In recent years, knowledge tracing (KT) within intelligent tutoring systems (ITSs) has seen rapid development. KT aims to assess a student’s knowledge state based on past performance and predict the correctness of the next question. Traditional KT often treats questions with different difficulty levels of the same concept as identical representations, limiting the effectiveness of question embedding. Additionally, higher-order semantic relationships between questions are overlooked. Graph models have been employed in KT to enhance question embedding representation, but they rarely consider the directed relationships between learning interactions. Therefore, this article introduces a novel approach, KT through Enhanced Questions and Directed Learning Interaction Based on multigraph embeddings in ITSs (MGEKT), to address these limitations. One channel enhances question embedding representation by capturing relationships between students, concepts, and questions. This channel defines two meta paths, facilitating the learning of high-order semantic relationships between questions. The other channel constructs a directed graph of learning interactions, leveraging graph attention convolution to illustrate their intricate relationships. A new gating mechanism is proposed to capture long-term dependencies and emphasize critical information when tracing students’ knowledge states. Notably, MGEKT employs reverse knowledge distillation, transferring knowledge from two small models (student models) to a large model (teacher model). This knowledge distillation enhances the model’s generalization performance and improves the perception of crucial information. In comparative evaluations across four datasets, MGEKT outperformed baselines, demonstrating its effectiveness in KT.
{"title":"Knowledge Tracing Through Enhanced Questions and Directed Learning Interaction Based on Multigraph Embeddings in Intelligent Tutoring Systems","authors":"Liqing Qiu;Lulu Wang","doi":"10.1109/TE.2024.3448532","DOIUrl":"10.1109/TE.2024.3448532","url":null,"abstract":"In recent years, knowledge tracing (KT) within intelligent tutoring systems (ITSs) has seen rapid development. KT aims to assess a student’s knowledge state based on past performance and predict the correctness of the next question. Traditional KT often treats questions with different difficulty levels of the same concept as identical representations, limiting the effectiveness of question embedding. Additionally, higher-order semantic relationships between questions are overlooked. Graph models have been employed in KT to enhance question embedding representation, but they rarely consider the directed relationships between learning interactions. Therefore, this article introduces a novel approach, KT through Enhanced Questions and Directed Learning Interaction Based on multigraph embeddings in ITSs (MGEKT), to address these limitations. One channel enhances question embedding representation by capturing relationships between students, concepts, and questions. This channel defines two meta paths, facilitating the learning of high-order semantic relationships between questions. The other channel constructs a directed graph of learning interactions, leveraging graph attention convolution to illustrate their intricate relationships. A new gating mechanism is proposed to capture long-term dependencies and emphasize critical information when tracing students’ knowledge states. Notably, MGEKT employs reverse knowledge distillation, transferring knowledge from two small models (student models) to a large model (teacher model). This knowledge distillation enhances the model’s generalization performance and improves the perception of crucial information. In comparative evaluations across four datasets, MGEKT outperformed baselines, demonstrating its effectiveness in KT.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"43-56"},"PeriodicalIF":2.1,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Contribution: This article suggests a novel method for diagnosing a learner’s cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts she has already mastered. Therefore, it is desirable to appropriately diagnose her cognitive proficiency and select the questions that can help improve preparedness. Research Question: Can the cognitive proficiency of a learner be progressively predicted when she attempts a series of questions? Methodology: A novel approach using DNNs to diagnose the learner’s proficiency after she attempts a set of questions is proposed in this article. Subsequently, to realize the effectiveness of the proposed prediction model, an algorithm is introduced that can select questions of required difficulty based on the predicted proficiency level. An appropriate question sequence can facilitate a learner’s faster attainment of the necessary competency level. Findings: The experimental results indicate that the proposed approach can predict the ability of learners with an accuracy of 91.21%. Moreover, the proposed technique outperforms the existing techniques by 33.19% on an average.
{"title":"Diagnosing Cognitive Proficiency of Students Using Dense Neural Networks for Adaptive Assistance","authors":"Jyoti Prakash Meher;Rajib Mall","doi":"10.1109/TE.2024.3446316","DOIUrl":"10.1109/TE.2024.3446316","url":null,"abstract":"Contribution: This article suggests a novel method for diagnosing a learner’s cognitive proficiency using deep neural networks (DNNs) based on her answers to a series of questions. The outcome of the forecast can be used for adaptive assistance. Background: Often a learner spends considerable amounts of time in attempting questions on the concepts she has already mastered. Therefore, it is desirable to appropriately diagnose her cognitive proficiency and select the questions that can help improve preparedness. Research Question: Can the cognitive proficiency of a learner be progressively predicted when she attempts a series of questions? Methodology: A novel approach using DNNs to diagnose the learner’s proficiency after she attempts a set of questions is proposed in this article. Subsequently, to realize the effectiveness of the proposed prediction model, an algorithm is introduced that can select questions of required difficulty based on the predicted proficiency level. An appropriate question sequence can facilitate a learner’s faster attainment of the necessary competency level. Findings: The experimental results indicate that the proposed approach can predict the ability of learners with an accuracy of 91.21%. Moreover, the proposed technique outperforms the existing techniques by 33.19% on an average.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"33-42"},"PeriodicalIF":2.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227426","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}
Julie P. Martin;Isabel Miller;Karin J. Jensen;Deepthi E. Suresh
Contribution: Our work focuses on building research capacity in engineering education research (EER). We operationalize enculturation of novice researchers into the EER community by studying temporal changes in the social networks of engineering faculty participating in a mentorship-based training grant. Background: The U.S. National Science Foundation’s Research Initiation in Engineering Formation (RIEF) is a training grant for engineering faculty without prior EER experience who seek to conduct EER. Faculty mentees work with an experienced social science researcher during a funded two-year project. During this time, mentees must undergo a paradigm shift from engineering research to social science, which includes building research skills and becoming enculturated into the EER community. Research Questions: What are the characteristics of RIEF mentees’ professional networks for EER? How do RIEF mentees’ networks change over time, as operationalized by professional interactions, communication about the RIEF project, and collaborations? Methodology: We use social network analysis to investigate the development of EER professional networks of RIEF mentees and their interactions with other community members during the first year of their research initiation training. Findings: Overall, mentees’ professional networks for EER increased (i.e., reported more connections) after one year. However, when mentors had limited prior connections to the EER community, their mentees’ social networks for EER are isolated compared to mentees whose mentors have a higher number of connections to community members. Our findings have implications for mentored training programs, suggesting that well-connected mentors are best placed to enculturate mentees into a research community.
{"title":"A Social Network Analysis of Faculty Mentees Funded by the Research Initiation in Engineering Formation (RIEF) Program","authors":"Julie P. Martin;Isabel Miller;Karin J. Jensen;Deepthi E. Suresh","doi":"10.1109/TE.2024.3436560","DOIUrl":"10.1109/TE.2024.3436560","url":null,"abstract":"Contribution: Our work focuses on building research capacity in engineering education research (EER). We operationalize enculturation of novice researchers into the EER community by studying temporal changes in the social networks of engineering faculty participating in a mentorship-based training grant. Background: The U.S. National Science Foundation’s Research Initiation in Engineering Formation (RIEF) is a training grant for engineering faculty without prior EER experience who seek to conduct EER. Faculty mentees work with an experienced social science researcher during a funded two-year project. During this time, mentees must undergo a paradigm shift from engineering research to social science, which includes building research skills and becoming enculturated into the EER community. Research Questions: What are the characteristics of RIEF mentees’ professional networks for EER? How do RIEF mentees’ networks change over time, as operationalized by professional interactions, communication about the RIEF project, and collaborations? Methodology: We use social network analysis to investigate the development of EER professional networks of RIEF mentees and their interactions with other community members during the first year of their research initiation training. Findings: Overall, mentees’ professional networks for EER increased (i.e., reported more connections) after one year. However, when mentors had limited prior connections to the EER community, their mentees’ social networks for EER are isolated compared to mentees whose mentors have a higher number of connections to community members. Our findings have implications for mentored training programs, suggesting that well-connected mentors are best placed to enculturate mentees into a research community.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"13-19"},"PeriodicalIF":2.1,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643030","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218942","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: This study synthesizes insights into the thematic focuses and linguistic attributes that resonate most in engineering faculty collaborations aimed at fostering entrepreneurial mindsets (EMs). It provides a roadmap for educators and institutions to effectively communicate and encourage entrepreneurial thinking in engineering. Background: Amid the heightened emphasis on entrepreneurial thinking in engineering education, understanding the factors that resonate with faculty is pivotal for informing curriculum development, aligning with global trends, and optimizing the preparedness of engineering graduates. Research Questions: 1) What elements of the EM are most frequently emphasized by faculty in their shared educational content? 2) What aspects of the EM resonate most with academic faculty? and 3) How do these relations differ in the electrical or computer engineering disciplines compared to other engineering fields? Methodology: A comprehensive analysis of educational resources shared by faculty on EM was conducted. The study used text analytics to assess engagement metrics, such as views, shares, favorites, and downloads. The data were analyzed using Stata. Findings: Faculty engagement strongly resonates with the three core components of the EM: Curiosity, Connections, and Creating Value, often emphasized in their shared educational content. Specifically, the “Creating Value” component emerged as the most significant across most engagement measures, with nuanced variations in the electrical and computer engineering disciplines.
贡献：本研究综述了旨在培养创业思维（EMs）的工程学教师合作中最能引起共鸣的主题重点和语言属性。它为教育工作者和机构提供了一个路线图，以便在工程学领域有效交流和鼓励创业思维。背景：在工程学教育越来越重视创业思维的背景下，了解与教师产生共鸣的因素，对于指导课程开发、与全球趋势接轨以及优化工程学毕业生的培养至关重要。研究问题1) 在共同的教学内容中，教师们最常强调哪些创业元素？2) 教育管理的哪些方面最能引起学术教师的共鸣？ 3) 与其他工程领域相比，电子或计算机工程学科的这些关系有何不同？研究方法：对教师在 EM 上共享的教育资源进行了全面分析。该研究使用文本分析来评估参与度指标，如浏览量、分享量、收藏量和下载量。数据使用 Stata 进行分析。研究结果教师的参与与教育网络的三个核心要素产生了强烈共鸣：好奇心、联系和创造价值，这三个要素在他们分享的教育内容中经常得到强调。具体而言，"创造价值 "是大多数参与度测量中最重要的组成部分，在电气和计算机工程学科中存在细微差别。
{"title":"Words That Resonate: Synthesizing Insights From Engineering Faculty Collaboration on Entrepreneurial Mindset","authors":"Agnieszka Kwapisz;Brock J. LaMeres","doi":"10.1109/TE.2024.3416866","DOIUrl":"10.1109/TE.2024.3416866","url":null,"abstract":"Contribution: This study synthesizes insights into the thematic focuses and linguistic attributes that resonate most in engineering faculty collaborations aimed at fostering entrepreneurial mindsets (EMs). It provides a roadmap for educators and institutions to effectively communicate and encourage entrepreneurial thinking in engineering. Background: Amid the heightened emphasis on entrepreneurial thinking in engineering education, understanding the factors that resonate with faculty is pivotal for informing curriculum development, aligning with global trends, and optimizing the preparedness of engineering graduates. Research Questions: 1) What elements of the EM are most frequently emphasized by faculty in their shared educational content? 2) What aspects of the EM resonate most with academic faculty? and 3) How do these relations differ in the electrical or computer engineering disciplines compared to other engineering fields? Methodology: A comprehensive analysis of educational resources shared by faculty on EM was conducted. The study used text analytics to assess engagement metrics, such as views, shares, favorites, and downloads. The data were analyzed using Stata. Findings: Faculty engagement strongly resonates with the three core components of the EM: Curiosity, Connections, and Creating Value, often emphasized in their shared educational content. Specifically, the “Creating Value” component emerged as the most significant across most engagement measures, with nuanced variations in the electrical and computer engineering disciplines.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"735-745"},"PeriodicalIF":2.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Contribution: This article provides an examination of changes in first-year engineering students’ perceptions of the role of an engineer after completing the Engineers Without Borders Challenge. Background: Essential pre- and post-comparisons missing in existing studies on the Challenge are provided, as well as comparison to other first-year project types across two universities. Research Question: Do students who participate in service-learning versus traditional project-based learning gain different understandings of the role of an engineer? Methodology: This work implements the questionnaire variant of convergent mixed methods design. A survey containing a mix of Likert-scale, open-ended short answer, and closed card sorting questions was administered to students enrolled in first-year engineering (FYE) courses across two institutions. Limitations of this work include potential bias due to the pre/post survey design and participant course self-selection. Findings: Students’ perceptions of the roles of engineers did not significantly differ by project type. However, changes in their perceptions of technical skills as important to the role of engineers did indicate the beginning of a transition from discipline level thinking to process level thinking. Additionally, course learning objectives influenced students’ perceptions of the role of engineers—with an increase in awareness of the importance of problem solving, communication, design process, and teamwork and a decreasing sense of importance of items missing from course objectives, such as creativity and helping people. Engineers’ professional responsibility to diversity, equity, and inclusion were absent from both the course syllabi and student perceptions of the role of an engineer.
{"title":"First-Year Design Projects and Student Perceptions of the Role of an Engineer","authors":"Amanda Singer;Stacie Aguirre-Jaimes;Antonique White;Margot Vigeant;Michelle Jarvie-Eggart","doi":"10.1109/TE.2024.3406221","DOIUrl":"10.1109/TE.2024.3406221","url":null,"abstract":"Contribution: This article provides an examination of changes in first-year engineering students’ perceptions of the role of an engineer after completing the Engineers Without Borders Challenge. Background: Essential pre- and post-comparisons missing in existing studies on the Challenge are provided, as well as comparison to other first-year project types across two universities. Research Question: Do students who participate in service-learning versus traditional project-based learning gain different understandings of the role of an engineer? Methodology: This work implements the questionnaire variant of convergent mixed methods design. A survey containing a mix of Likert-scale, open-ended short answer, and closed card sorting questions was administered to students enrolled in first-year engineering (FYE) courses across two institutions. Limitations of this work include potential bias due to the pre/post survey design and participant course self-selection. Findings: Students’ perceptions of the roles of engineers did not significantly differ by project type. However, changes in their perceptions of technical skills as important to the role of engineers did indicate the beginning of a transition from discipline level thinking to process level thinking. Additionally, course learning objectives influenced students’ perceptions of the role of engineers—with an increase in awareness of the importance of problem solving, communication, design process, and teamwork and a decreasing sense of importance of items missing from course objectives, such as creativity and helping people. Engineers’ professional responsibility to diversity, equity, and inclusion were absent from both the course syllabi and student perceptions of the role of an engineer.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"669-680"},"PeriodicalIF":2.1,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10633790","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"IEEE Transactions on Education Information for Authors","authors":"","doi":"10.1109/TE.2024.3426182","DOIUrl":"10.1109/TE.2024.3426182","url":null,"abstract":"","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 4","pages":"C3-C3"},"PeriodicalIF":2.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10631815","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937425","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}
Shannon Chance;Farrah Fayyaz;Anita L. Campbell;Nicole P. Pitterson;Sadia Nawaz
Understanding mathematics is essential for learning many concepts in engineering. Conceptual learning of engineering requires students to successfully connect abstract and concrete concepts to achieve a cohesive understanding of the content, and doing so goes beyond memorizing facts and applying formulas. Educators can observe that conceptual learning “has happened” once a student is able to successfully explain the concept, use the concept, and create new knowledge from the learned concept [1] . Moreover, a student’s ability to understand, both qualitatively and quantitatively, the mathematical equations and computations that describe various engineering processes and phenomena is necessary for the conceptual learning of many courses in engineering.
{"title":"Guest Editorial Special Issue on Conceptual Learning of Mathematics-Intensive Concepts in Engineering","authors":"Shannon Chance;Farrah Fayyaz;Anita L. Campbell;Nicole P. Pitterson;Sadia Nawaz","doi":"10.1109/TE.2024.3416649","DOIUrl":"10.1109/TE.2024.3416649","url":null,"abstract":"Understanding mathematics is essential for learning many concepts in engineering. Conceptual learning of engineering requires students to successfully connect abstract and concrete concepts to achieve a cohesive understanding of the content, and doing so goes beyond memorizing facts and applying formulas. Educators can observe that conceptual learning “has happened” once a student is able to successfully explain the concept, use the concept, and create new knowledge from the learned concept \u0000<xref>[1]</xref>\u0000. Moreover, a student’s ability to understand, both qualitatively and quantitatively, the mathematical equations and computations that describe various engineering processes and phenomena is necessary for the conceptual learning of many courses in engineering.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 4","pages":"491-498"},"PeriodicalIF":2.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10631814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937427","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}
Contributions: An adoption framework to include generative artificial intelligence (GenAI) in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption. Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. GenAI has impacted our perspective on traditional methods of academic integrity and the scholarship of teaching, learning, and research. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved. Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students’ learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed 1) for effective redesign of the course curriculum; 2) for enabling staff and students; and 3) to define an evaluation matrix to measure the effectiveness and success of the adoption process.
{"title":"Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education","authors":"Samar Shailendra;Rajan Kadel;Aakanksha Sharma","doi":"10.1109/TE.2024.3432101","DOIUrl":"10.1109/TE.2024.3432101","url":null,"abstract":"Contributions: An adoption framework to include generative artificial intelligence (GenAI) in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption. Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. GenAI has impacted our perspective on traditional methods of academic integrity and the scholarship of teaching, learning, and research. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved. Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students’ learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed 1) for effective redesign of the course curriculum; 2) for enabling staff and students; and 3) to define an evaluation matrix to measure the effectiveness and success of the adoption process.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"777-785"},"PeriodicalIF":2.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937428","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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