Research Purpose and Contribution: The study aimed to construct an evaluation framework for assessing pupils’ computational thinking (CT) during classroom learning problem solving. As a self-report evaluation scale for pupils, this evaluation framework further enriched the CT assessment instruments for pupils and provided a specialized instrument for experts to evaluate pupils’ CT in problem-solving situations during classroom learning. Background: CT cultivation and assessment methods are hot topics in the field of education. CT assessment can effectively test the effect of CT cultivation. There are many CT assessment methods, of which evaluation frameworks are an effective self-reporting assessment method. Existing studies on self-reported CT evaluation frameworks are commonly applicable to students at different stages. However, few studies have focused on the specific context from the perspective of practice for pupils. Thus, the evaluation framework of pupils’ CT is worth exploring. Intended Outcomes: A CT evaluation framework for evaluating pupils’ CT in problem-solving situations in classroom learning was constructed to facilitate researchers’ understanding of pupils’ CT levels and problem-solving skills. Application Design: In this study, data from 897 pupils in the fifth and sixth grades were collected using an online questionnaire that included 27 items about CT. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item analysis were conducted to analyze the data in this study, with 17 items remaining in the final evaluation framework. Findings: The fitting validity, convergence validity, and discriminant validity all met the recommended criteria, which showed that the evaluation framework was effective. The total reliability of CT was 0.911, indicating that the consistency and reliability of the evaluation framework constructed in this study were satisfied.
{"title":"Constructing a Computational Thinking Evaluation Framework for Pupils","authors":"Yu-Sheng Su;Xiao Wang;Li Zhao","doi":"10.1109/TE.2024.3424423","DOIUrl":"10.1109/TE.2024.3424423","url":null,"abstract":"Research Purpose and Contribution: The study aimed to construct an evaluation framework for assessing pupils’ computational thinking (CT) during classroom learning problem solving. As a self-report evaluation scale for pupils, this evaluation framework further enriched the CT assessment instruments for pupils and provided a specialized instrument for experts to evaluate pupils’ CT in problem-solving situations during classroom learning. Background: CT cultivation and assessment methods are hot topics in the field of education. CT assessment can effectively test the effect of CT cultivation. There are many CT assessment methods, of which evaluation frameworks are an effective self-reporting assessment method. Existing studies on self-reported CT evaluation frameworks are commonly applicable to students at different stages. However, few studies have focused on the specific context from the perspective of practice for pupils. Thus, the evaluation framework of pupils’ CT is worth exploring. Intended Outcomes: A CT evaluation framework for evaluating pupils’ CT in problem-solving situations in classroom learning was constructed to facilitate researchers’ understanding of pupils’ CT levels and problem-solving skills. Application Design: In this study, data from 897 pupils in the fifth and sixth grades were collected using an online questionnaire that included 27 items about CT. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and item analysis were conducted to analyze the data in this study, with 17 items remaining in the final evaluation framework. Findings: The fitting validity, convergence validity, and discriminant validity all met the recommended criteria, which showed that the evaluation framework was effective. The total reliability of CT was 0.911, indicating that the consistency and reliability of the evaluation framework constructed in this study were satisfied.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 6","pages":"878-888"},"PeriodicalIF":2.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227402","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}
Contributions: This article presents the results from a teaching innovation project based on the creation of educational videos by students and their assessment through blind peer review in the context of an electric circuit course. This article also analyses the activity’s impact on learning outcomes by comparing the results of participating students with nonparticipants, as well as with results from the previous years. The study includes surveys completed by students. Background: Electric circuit courses involve a cumulative learning process that advances throughout the course. Students who do not adhere to a regular study-homework routine often struggle to maximize the benefits of their class time and are more prone to test failures. Research Questions (RQs): RQ1) Can peer assessment be relied upon as a grading method in an electrical engineering course? RQ2) Is it possible to enhance students’ study routines and improve their results by incorporating assessment activities different from partial exams, such as creating educational videos and peer review assessments? Methodology: Students create videos, which are then submitted to the designated task through the Moodle workshop tool. Subsequently, peer reviews are conducted using a rubric form. The reliability of peer review is analysed by comparing the grades assigned by students with those assigned by teachers who are introduced as incognito reviewers. Findings: The evaluation system, relying on peer assessments, demonstrated fair reliability. Participants have substantially improved their academic performance while dedicating less time to preparing for the different evaluation tests.
{"title":"Learning Through Explanation: Producing and Peer-Reviewing Videos on Electric Circuits Problem Solving","authors":"Francisco Arredondo;Belén García;Ruben Lijo","doi":"10.1109/TE.2024.3454008","DOIUrl":"10.1109/TE.2024.3454008","url":null,"abstract":"Contributions: This article presents the results from a teaching innovation project based on the creation of educational videos by students and their assessment through blind peer review in the context of an electric circuit course. This article also analyses the activity’s impact on learning outcomes by comparing the results of participating students with nonparticipants, as well as with results from the previous years. The study includes surveys completed by students. Background: Electric circuit courses involve a cumulative learning process that advances throughout the course. Students who do not adhere to a regular study-homework routine often struggle to maximize the benefits of their class time and are more prone to test failures. Research Questions (RQs): RQ1) Can peer assessment be relied upon as a grading method in an electrical engineering course? RQ2) Is it possible to enhance students’ study routines and improve their results by incorporating assessment activities different from partial exams, such as creating educational videos and peer review assessments? Methodology: Students create videos, which are then submitted to the designated task through the Moodle workshop tool. Subsequently, peer reviews are conducted using a rubric form. The reliability of peer review is analysed by comparing the grades assigned by students with those assigned by teachers who are introduced as incognito reviewers. Findings: The evaluation system, relying on peer assessments, demonstrated fair reliability. Participants have substantially improved their academic performance while dedicating less time to preparing for the different evaluation tests.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"67-78"},"PeriodicalIF":2.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10677388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218938","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}
Ján Guniš;L’ubomír Šnajder;L’ubomír Antoni;Peter Eliaš;Ondrej Krídlo;Stanislav Krajči
Contribution: We present a framework for teachers to investigate the relationships between attributes of students’ solutions in the process of problem solving or computational thinking. We provide visualization and evaluation techniques to find hidden patterns in the students’ solutions which allow teachers to predict the specific behavior of students or to prevent some student mistakes or misconceptions in advance or further pedagogical intervention. Background: Formal concept analysis is a method of unsupervised Machine Learning that applies mathematical lattice theory to organize data based on objects and their shared attributes. Several fuzzy extensions of formal concept analysis have a great potential to visualize and evaluate students’ solutions, to categorize the solutions into overlapping biclusters (formal concepts) or to generate the attribute implications between extracted attributes. Research Question: Does formal concept analysis describe the various solutions and the relationships between the extracted attributes of students’ solutions in the educational and computational thinking game Light-Bot? Methodology: Targeting the evaluation of 64 students’ solutions in the Light-Bot game, we construct the formal contexts of the extracted attributes. We apply formal concept analysis to construct the concept lattices from two binary formal contexts and to generate attribute implications and their fuzzy counterparts to find the dependencies between the extracted attributes. Findings: The results of our paper provide a description of various students’ solutions which are visualized in the concept lattices. 1) Regarding the concept lattice of binary formal contexts, we obtained the characterization of the largest biclusters which includes a description of the largest group of similar solutions. 2) The attribute implications mainly reveal the characterization of similar solutions, e.g., with a higher count of executed commands in solutions. 3) Using fuzzy attribute implications, we obtained the characterization of solutions with unnecessary commands, going out of the game area, or using indirect recursion.
{"title":"Formal Concept Analysis of Students’ Solutions on Computational Thinking Game","authors":"Ján Guniš;L’ubomír Šnajder;L’ubomír Antoni;Peter Eliaš;Ondrej Krídlo;Stanislav Krajči","doi":"10.1109/TE.2024.3442612","DOIUrl":"10.1109/TE.2024.3442612","url":null,"abstract":"Contribution: We present a framework for teachers to investigate the relationships between attributes of students’ solutions in the process of problem solving or computational thinking. We provide visualization and evaluation techniques to find hidden patterns in the students’ solutions which allow teachers to predict the specific behavior of students or to prevent some student mistakes or misconceptions in advance or further pedagogical intervention. Background: Formal concept analysis is a method of unsupervised Machine Learning that applies mathematical lattice theory to organize data based on objects and their shared attributes. Several fuzzy extensions of formal concept analysis have a great potential to visualize and evaluate students’ solutions, to categorize the solutions into overlapping biclusters (formal concepts) or to generate the attribute implications between extracted attributes. Research Question: Does formal concept analysis describe the various solutions and the relationships between the extracted attributes of students’ solutions in the educational and computational thinking game Light-Bot? Methodology: Targeting the evaluation of 64 students’ solutions in the Light-Bot game, we construct the formal contexts of the extracted attributes. We apply formal concept analysis to construct the concept lattices from two binary formal contexts and to generate attribute implications and their fuzzy counterparts to find the dependencies between the extracted attributes. Findings: The results of our paper provide a description of various students’ solutions which are visualized in the concept lattices. 1) Regarding the concept lattice of binary formal contexts, we obtained the characterization of the largest biclusters which includes a description of the largest group of similar solutions. 2) The attribute implications mainly reveal the characterization of similar solutions, e.g., with a higher count of executed commands in solutions. 3) Using fuzzy attribute implications, we obtained the characterization of solutions with unnecessary commands, going out of the game area, or using indirect recursion.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"20-32"},"PeriodicalIF":2.1,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218939","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}
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}
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