IEEE Transactions on Education最新文献

{"title":"Analyzing Pedagogic Practice and Assessments in a Cross-Campus Programming Course","authors":"Grethe Sandstrak;Bjørn Klefstad;Arne Styve;Kiran Raja","doi":"10.1109/TE.2024.3465870","DOIUrl":"https://doi.org/10.1109/TE.2024.3465870","url":null,"abstract":"Teaching programming efficiently to students in the first year of computer science education is challenging. It is especially cumbersome to retain the interest of both groups, when the student group consists of novice (i.e., those who have never programmed before) and expert programmers in the same crowd. Thus, individualized teaching cannot be achieved in a traditional lecture hall for a larger student population setting and poses a pedagogical challenge. This article presents various pedagogical approaches and explores different assessment forms to foster student active learning (SAL), drawing from years of experience teaching a programming course. A detailed analysis has been conducted to understand the effect on the students’ learning and perception of a course given different pedagogical approaches and varied assessment forms. The analysis is based on a course offered across three different campuses with the same course description. The key difference between the campuses however is the choice of pedagogical approach, where one of the campuses uses a bottom-up pedagogy. In contrast, the other two campuses use the top-down pedagogy to teach programming concepts to the students. Across the three campuses, the assessment form in the course has varied from regular practical school exams together with a theoretical multiple choice format, a practical home exam during the pandemic and most recently a portfolio. The findings show no significant differences in the learning outcomes based on an analysis of the grades across campuses and extensive student surveys.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 6","pages":"964-973"},"PeriodicalIF":2.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142859197","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":"Developing Teamwork Skills in Undergraduate Engineering Students: A Comparison Between Disciplinary and Cross-Disciplinary Projects","authors":"Francisco Cima;Pilar Pazos;Minjung Lee;Kristie Gutierrez;Jennifer Kidd;Orlando Ayala;Stacie Ringleb;Krishnanand Kaipa;Danielle Rhemer","doi":"10.1109/TE.2024.3456098","DOIUrl":"https://doi.org/10.1109/TE.2024.3456098","url":null,"abstract":"This study contributes to team science and competency development by comparing demonstrated teamwork skills by engineering students participating in disciplinary and cross-disciplinary team projects. Teamwork skills are key competencies necessary to solve complex technical challenges in the workplace. Despite prior efforts to enhance these competencies in engineering students, there is still a lack of empirical research examining the effectiveness of such efforts. This study compares teamwork skills demonstrated by undergraduate engineering students in two conditions: 1) engineering-only teams and 2) cross-disciplinary collaboration with education students. Projects in both settings had similar levels of complexity, scope, team size, and requirements. The differences in teamwork skills were investigated using a mixed methods approach with a main quantitative phase, followed by a secondary qualitative analysis to further examine the nature of the differences. Students in the cross-disciplinary model demonstrated higher levels of teamwork skills than those in the engineering-only condition. Results from this work illustrate how authentic and well-designed cross-disciplinary team projects can facilitate the development of students’ teamwork skills beyond what can be achieved in disciplinary teams, fulfilling an essential ABET requirement and enhancing student preparation to succeed in the workplace.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"86-94"},"PeriodicalIF":2.1,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360913","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":"Development and Evaluation of Remote Laboratory System for Simulated Induction Motor","authors":"Hisao Fukumoto;Tomoki Kamio;Toshihiro Matsuo;Takayuki Nitta;Hideki Shimada;Masashi Ohchi;Hideaki Itoh","doi":"10.1109/TE.2024.3454236","DOIUrl":"10.1109/TE.2024.3454236","url":null,"abstract":"Contribution: A remote laboratory system for induction motors was developed in this study. By creating an original simulated induction motor, the structure of the motor can be observed, and the current and magnetic flux can be measured safely.Background: Electrical machinery has little appeal to young engineers. Such machinery deals with invisible electromagnetic phenomena; thus, it is difficult for young engineers to understand the involved principles. The risks associated with high voltage and high-speed rotation are also considered reasons for this low interest.Intended Outcomes: The remote laboratory system enables remote learning even in educational institutions that do not have specialized simulated induction motors. In addition, it is possible to repeat experimental learning as required to ensure that the student has learned the content sufficiently.Application Design: This system is designed such that it can be used without teachers or teaching assistants support, and the number of controllable functions and operations increase gradually according to the learning content.Findings: The proposed remote laboratory system was evaluated experimentally with 46 student participants from Saga University and the Chiba Institute of Technology to confirm the usefulness of the system. Tests conducted before and after using the system confirmed that the participant’s understanding of induction motors improved. In addition, the results of a system usability scale evaluation confirmed that there were no problems with operation of the remote laboratory system.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"79-85"},"PeriodicalIF":2.1,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259719","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":"Developing and Validating the Contextual Technology Andragogy/Pedagogy Entrepreneurship Work Content Knowledge Model: A Framework for Vocational Education","authors":"Bilal Younis","doi":"10.1109/TE.2024.3449636","DOIUrl":"10.1109/TE.2024.3449636","url":null,"abstract":"Purpose: The present study aimed to integrate artificial intelligence technology (ChatGPT) with an in-depth literature review to explore knowledge dimensions of professional teaching and information and communication technology (ICT) integration models in TVET, and utilize the Delphi technique and ChatGPT to examine, enhance, and validate a newly proposed model for professional teaching and ICT integration in TVET.Methods: Three rounds of the Delphi technique were applied to develop and validate this new model. Artificial intelligence tool (ChatGPT) with an in-depth literature review were used to explore knowledge dimensions for TVET education. In Round 1, ChatGPT was used to transform the technological pedagogical content knowledge (CK) model for TVET instruction. A new suggested model was developed called the contextual technology andrology/pedagogy entrepreneurship work CK (CTA/PEWCK) model. Ten experts from the TVET sector participated in Round 2, and 39 participated in Round 3 to validate the new suggested model.Findings: The findings revealed that the fifteen knowledge dimensions extracted from this new suggested model presented essential knowledge for TVET education.Conclusion: Applying the CTA/PEWCK model offers professional development opportunities for TVET teachers that focus on hands-on experiences to develop competencies for sustainable development (ESD), enabling an integrated approach to knowledge dimensions, procedures, and attitude.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"68 1","pages":"57-66"},"PeriodicalIF":2.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218936","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":"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}

{"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}

{"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}

{"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}

{"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}

{"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}