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}
Contributions: In this article, a generative artificial intelligence (AI)-based Q&A system has been developed by integrating information retrieval and natural language processing techniques, using course materials as a knowledge base and facilitating real-time student interaction through a chat interface. Background: The rise of advanced AI exemplified by ChatGPT developed by OpenAI, has sparked interest in its application within higher education. AI has the potential to reshape education delivery through chatbots and related tools, improving remote learning and mitigating challenges, such as student isolation and educator administrative burdens. Yet, ChatGPT’s practical applications in education remain uncertain, potentially due to its novel and enigmatic nature. Additionally, current e-learning chatbot systems often suffer from development complexity and a lack of input from key stakeholders, leading to developer-focused solutions rather than user-centered ones. Intended Outcomes: In this manuscript, we introduce a practical implementation of AI in education by creating a system called Q-Module-Bot that is accessible for both technical and nontechnical educators to harness e-learning benefits and demystify generative pretraining transformer (GPT). Application Design: The proposed Q-Module-Bot system has utilized pretrained large language models (LLMs) to build a Q&A system that helps students with their queries and supports education delivery using content extracted from a virtual learning environment (VLE). Findings: The prototype and system evaluation confirm the effectiveness of a scalable cross-departmental tool featuring source attribution and real-time responses. While successful in encouraging wider acceptance of GPT use cases in higher education, refinements are needed for full integration into the VLE and expansion to other modules/courses.
{"title":"Q-Module-Bot: A Generative AI-Based Question and Answer Bot for Module Teaching Support","authors":"Mia Allen;Usman Naeem;Sukhpal Singh Gill","doi":"10.1109/TE.2024.3435427","DOIUrl":"10.1109/TE.2024.3435427","url":null,"abstract":"Contributions: In this article, a generative artificial intelligence (AI)-based Q&A system has been developed by integrating information retrieval and natural language processing techniques, using course materials as a knowledge base and facilitating real-time student interaction through a chat interface. Background: The rise of advanced AI exemplified by ChatGPT developed by OpenAI, has sparked interest in its application within higher education. AI has the potential to reshape education delivery through chatbots and related tools, improving remote learning and mitigating challenges, such as student isolation and educator administrative burdens. Yet, ChatGPT’s practical applications in education remain uncertain, potentially due to its novel and enigmatic nature. Additionally, current e-learning chatbot systems often suffer from development complexity and a lack of input from key stakeholders, leading to developer-focused solutions rather than user-centered ones. Intended Outcomes: In this manuscript, we introduce a practical implementation of AI in education by creating a system called Q-Module-Bot that is accessible for both technical and nontechnical educators to harness e-learning benefits and demystify generative pretraining transformer (GPT). Application Design: The proposed Q-Module-Bot system has utilized pretrained large language models (LLMs) to build a Q&A system that helps students with their queries and supports education delivery using content extracted from a virtual learning environment (VLE). Findings: The prototype and system evaluation confirm the effectiveness of a scalable cross-departmental tool featuring source attribution and real-time responses. While successful in encouraging wider acceptance of GPT use cases in higher education, refinements are needed for full integration into the VLE and expansion to other modules/courses.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"793-802"},"PeriodicalIF":2.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937429","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}
José A. Ballesteros;Marcos D. Fernandez;José L. González-Geraldo
Contribution: A peer-mentoring plan designed to support engineering students during their transition from high school to university. This article addresses the adaptation challenges faced by first-year students in engineering programs. Background: The transition to university is a critical period for students, marked by significant lifestyle changes and the inherent difficulties of engineering degrees. This often results in high stress levels, with some students struggling to adapt and consequently dropping out. Previous efforts to support students have shown varying degrees of success, highlighting the need for effective peer support mechanisms. Intended Outcomes: A structured peer-mentoring environment aimed at reducing stress, improving first-year students’ adaptation to university life, and decreasing dropout rates. The program is designed to be well received by both mentors and mentees, thereby enhancing the academic experience for engineering students. Application Design: Drawing on existing teaching experiences and literature, the proposed peer-mentoring program involves senior students acting as mentors to first-year students. The program begins with a training session to equip mentors with necessary tools and to define their roles and boundaries. This is followed by an initial meeting during the welcome day, and continues with formal and informal interactions throughout the first semester, under the supervision of the degree coordinator. Findings: Surveys completed by both mentors and first-year students indicate a high level of acceptance and perceived usefulness of the peer-mentoring program. The results suggest that the program effectively supports first-year students in their transition to university life, with strong recommendations for its continuation in future academic years.
{"title":"Peer-Mentoring Program for the Individual Attention of Engineering Students","authors":"José A. Ballesteros;Marcos D. Fernandez;José L. González-Geraldo","doi":"10.1109/TE.2024.3432830","DOIUrl":"10.1109/TE.2024.3432830","url":null,"abstract":"Contribution: A peer-mentoring plan designed to support engineering students during their transition from high school to university. This article addresses the adaptation challenges faced by first-year students in engineering programs. Background: The transition to university is a critical period for students, marked by significant lifestyle changes and the inherent difficulties of engineering degrees. This often results in high stress levels, with some students struggling to adapt and consequently dropping out. Previous efforts to support students have shown varying degrees of success, highlighting the need for effective peer support mechanisms. Intended Outcomes: A structured peer-mentoring environment aimed at reducing stress, improving first-year students’ adaptation to university life, and decreasing dropout rates. The program is designed to be well received by both mentors and mentees, thereby enhancing the academic experience for engineering students. Application Design: Drawing on existing teaching experiences and literature, the proposed peer-mentoring program involves senior students acting as mentors to first-year students. The program begins with a training session to equip mentors with necessary tools and to define their roles and boundaries. This is followed by an initial meeting during the welcome day, and continues with formal and informal interactions throughout the first semester, under the supervision of the degree coordinator. Findings: Surveys completed by both mentors and first-year students indicate a high level of acceptance and perceived usefulness of the peer-mentoring program. The results suggest that the program effectively supports first-year students in their transition to university life, with strong recommendations for its continuation in future academic years.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"786-792"},"PeriodicalIF":2.1,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141884396","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 provides an implementation of a partially flipped classroom with gamification aspects that has shown a statistically significant increase in student performance relative to traditional lecture. Background: Electronic Circuits is a challenging required course for first-year students in the Electrical Engineering degree program at National Taiwan University. Students taking the English section have historically performed lower than other Chinese sections, likely due to their diverse backgrounds and less familiarity with Taiwanese-style exams. Intended Outcome: This study applied flipped-learning-with-gamification teaching methods to evaluate their effectiveness in improving students’ motivation to solve ungraded practice problems and increase student performance. One-third of the class was a condensed in-class lecture with supplemental online videos, while two-thirds was a problem-solving session with students in teams. A class gameboard (leaderboard) and weekly concept cards (badges) were used to motivate the students to complete weekly ungraded practice problems. Findings: The results showed that the flipped-learning-with-gamification approach increased the average of the English section’s normalized quiz and exam grades by 11.6% compared to the previous year, such that the section’s average performance matched that of the other sections (control groups). Results also found that higher grades were most strongly correlated with higher completion rates of in-class problems and were uncorrelated with lecture attendance. Survey results showed that students liked the gamification aspects of working in teams, receiving concept cards, and completing challenge problems more than the course gameboard.
{"title":"Using a Partially Flipped Classroom and Gamification to Improve Student Performance in a First-Year Electronic Circuits Course","authors":"Katherine A. Kim;F. Selin Bagci;Anwell Ho","doi":"10.1109/TE.2024.3422017","DOIUrl":"10.1109/TE.2024.3422017","url":null,"abstract":"Contribution: This study provides an implementation of a partially flipped classroom with gamification aspects that has shown a statistically significant increase in student performance relative to traditional lecture. Background: Electronic Circuits is a challenging required course for first-year students in the Electrical Engineering degree program at National Taiwan University. Students taking the English section have historically performed lower than other Chinese sections, likely due to their diverse backgrounds and less familiarity with Taiwanese-style exams. Intended Outcome: This study applied flipped-learning-with-gamification teaching methods to evaluate their effectiveness in improving students’ motivation to solve ungraded practice problems and increase student performance. One-third of the class was a condensed in-class lecture with supplemental online videos, while two-thirds was a problem-solving session with students in teams. A class gameboard (leaderboard) and weekly concept cards (badges) were used to motivate the students to complete weekly ungraded practice problems. Findings: The results showed that the flipped-learning-with-gamification approach increased the average of the English section’s normalized quiz and exam grades by 11.6% compared to the previous year, such that the section’s average performance matched that of the other sections (control groups). Results also found that higher grades were most strongly correlated with higher completion rates of in-class problems and were uncorrelated with lecture attendance. Survey results showed that students liked the gamification aspects of working in teams, receiving concept cards, and completing challenge problems more than the course gameboard.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"758-766"},"PeriodicalIF":2.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871481","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}
Daniel Escanez-Exposito;Javier Correa-Marichal;Pino Caballero-Gil
Quantum computing is an emerging and quickly expanding domain that captivates scientists and engineers. Recognizing the limitations of conventional educational approaches in adequately preparing individuals for their incursion in this area, this research introduces a novel board game called “Qubit: The Game,” whose objective is twofold: 1) to foster enthusiasm for quantum computing and 2) to enhance comprehension of fundamental notions within this discipline. This document provides explanations regarding the rationale behind selecting a board game format, the game’s design and mechanics, as well as the methodology followed during its development. Furthermore, it contains a first analysis conducted to assess the impact of the designed game, on the perception, interest and fundamental notions of quantum computing among K-16 students. The outcomes from this research unequivocally demonstrate that the devised game serves as a potent instrument in cultivating enjoyment and facilitating the understanding of essential knowledge in a topic as intricate as quantum computing. In fact, the effectiveness of this game also highlights its potential to introduce learners to different STEAM-related topics.
{"title":"Using Game-Based Learning and Quantum Computing to Enhance STEAM Competencies in K-16 Education","authors":"Daniel Escanez-Exposito;Javier Correa-Marichal;Pino Caballero-Gil","doi":"10.1109/TE.2024.3422315","DOIUrl":"10.1109/TE.2024.3422315","url":null,"abstract":"Quantum computing is an emerging and quickly expanding domain that captivates scientists and engineers. Recognizing the limitations of conventional educational approaches in adequately preparing individuals for their incursion in this area, this research introduces a novel board game called “Qubit: The Game,” whose objective is twofold: 1) to foster enthusiasm for quantum computing and 2) to enhance comprehension of fundamental notions within this discipline. This document provides explanations regarding the rationale behind selecting a board game format, the game’s design and mechanics, as well as the methodology followed during its development. Furthermore, it contains a first analysis conducted to assess the impact of the designed game, on the perception, interest and fundamental notions of quantum computing among K-16 students. The outcomes from this research unequivocally demonstrate that the devised game serves as a potent instrument in cultivating enjoyment and facilitating the understanding of essential knowledge in a topic as intricate as quantum computing. In fact, the effectiveness of this game also highlights its potential to introduce learners to different STEAM-related topics.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 6","pages":"807-816"},"PeriodicalIF":2.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10618884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871483","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}
This article discusses the latest developments of the Sucre4Stem tool, as part of the Sucre initiative, which aims to promote interest in computational thinking and programming skills in K-12 students. The tool follows the Internet of Things approach and consists of two prominent components: 1) SucreCore and 2) SucreCode. SucreCore incorporates an advanced microcontroller packaged in a more compact design and enables wireless connectivity. SucreCode, the block-based visual programming tool, supports two different sets of blocks depending on the education grade, and facilitates wireless communication with SucreCore. At the educational level, Sucre4Stem fosters new group dynamics and encourages students to experiment real-world projects by promoting the “programming to learn” approach to concepts from other disciplines as opposed to the strategy widely applied in schools of “learning to program” in isolation.
{"title":"Sucre4Stem: A K-12 Educational Tool for Integrating Computational Thinking and Programming Across Multidisciplinary Disciplines","authors":"Sergio Trilles;Aida Monfort-Muriach;Enrique Cueto-Rubio;Carmen López-Girona;Carlos Granell","doi":"10.1109/TE.2024.3422666","DOIUrl":"10.1109/TE.2024.3422666","url":null,"abstract":"This article discusses the latest developments of the Sucre4Stem tool, as part of the Sucre initiative, which aims to promote interest in computational thinking and programming skills in K-12 students. The tool follows the Internet of Things approach and consists of two prominent components: 1) SucreCore and 2) SucreCode. SucreCore incorporates an advanced microcontroller packaged in a more compact design and enables wireless connectivity. SucreCode, the block-based visual programming tool, supports two different sets of blocks depending on the education grade, and facilitates wireless communication with SucreCore. At the educational level, Sucre4Stem fosters new group dynamics and encourages students to experiment real-world projects by promoting the “programming to learn” approach to concepts from other disciplines as opposed to the strategy widely applied in schools of “learning to program” in isolation.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 6","pages":"868-877"},"PeriodicalIF":2.1,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10613367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141871482","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}
Wesley Beccaro;Elisabete Galeazzo;Denise Consonni;Henrique E. Maldonado Peres;Leopoldo R. Yoshioka
Contribution: The evaluation of analog-to-digital conversion methods constitutes a key component of an Instrumentation course. This study introduces an affordable educational platform based on Arduino UNO board designed for teaching analog-to-digital conversion concepts, supported by virtual instruments (VIs). Background: ADCs are electronic devices found in a wide range of consumer electronics, such as smartphones and Internet of Things (IoT) devices. In order to investigate the fundamental aspects of ADCs, a data acquisition system is required. However, high-quality ADC systems tend to be expensive. Alternatively, cost-effective microcontrollers can serve as an educational platform for conducting experimental procedures, including tests, characterization, and calibration. Intended Outcomes: The proposed experiment concentrates on elucidating the theoretical foundations of analog-to-digital conversion, along with providing in-depth insights into the technical details involved in characterizing and calibrating ADCs. Application Design: Four VIs have been developed and are employed to investigate concepts, such as resolution, nonlinearity, aliasing, and to determine offset and gain errors. Findings: The learning experience and the usability of the system were assessed through questionnaires distributed to a total of 105 students. In addition, the final exam was used to assess the performance of 29 students. The results indicate that the students significantly improved their ability to understand, apply, and analyze essential aspects of ADC after engaging in the experiments, demonstrating substantial learning gains.
{"title":"Practical Learning of Analog-to-Digital Conversion Concepts With a Low-Cost Didactic Platform","authors":"Wesley Beccaro;Elisabete Galeazzo;Denise Consonni;Henrique E. Maldonado Peres;Leopoldo R. Yoshioka","doi":"10.1109/TE.2024.3428414","DOIUrl":"10.1109/TE.2024.3428414","url":null,"abstract":"Contribution: The evaluation of analog-to-digital conversion methods constitutes a key component of an Instrumentation course. This study introduces an affordable educational platform based on Arduino UNO board designed for teaching analog-to-digital conversion concepts, supported by virtual instruments (VIs). Background: ADCs are electronic devices found in a wide range of consumer electronics, such as smartphones and Internet of Things (IoT) devices. In order to investigate the fundamental aspects of ADCs, a data acquisition system is required. However, high-quality ADC systems tend to be expensive. Alternatively, cost-effective microcontrollers can serve as an educational platform for conducting experimental procedures, including tests, characterization, and calibration. Intended Outcomes: The proposed experiment concentrates on elucidating the theoretical foundations of analog-to-digital conversion, along with providing in-depth insights into the technical details involved in characterizing and calibrating ADCs. Application Design: Four VIs have been developed and are employed to investigate concepts, such as resolution, nonlinearity, aliasing, and to determine offset and gain errors. Findings: The learning experience and the usability of the system were assessed through questionnaires distributed to a total of 105 students. In addition, the final exam was used to assess the performance of 29 students. The results indicate that the students significantly improved their ability to understand, apply, and analyze essential aspects of ADC after engaging in the experiments, demonstrating substantial learning gains.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 5","pages":"767-776"},"PeriodicalIF":2.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141782382","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: To address the interpretability issues in artificial intelligence (AI)-powered classroom discourse models, we employ explainable AI methods to unpack classroom discourse analysis from deep learning-based models and evaluate the effects of model explanations on STEM teachers. Background: Deep learning techniques have been used to automatically analyze classroom dialogue to provide feedback for teachers. However, these complex models operate as black boxes, lacking clear explanations of the analysis, which may lead teachers, particularly those lacking AI knowledge, to distrust the models and hinder their teaching practice. Therefore, it is crucial to address the interpretability issue in AI-powered classroom discourse models. Research Questions: How to explain deep learning-based classroom discourse models using explainable AI methods? What is the effect of these explanations on teachers’ trust in and technology acceptance of the models? How do teachers perceive the explanations of deep learning-based classroom discourse models? Method: Two explainable AI methods were employed to interpret deep learning-based models that analyzed teacher and student talk moves. A pilot study was conducted, involving seven STEM teachers interested in learning talk moves and receiving classroom discourse analysis. The study assessed changes in teachers’ trust and technology acceptance before and after receiving model explanations. Teachers’ perceptions of the model explanations were investigated. Findings: The AI-powered classroom discourse models were effectively explained using explainable AI methods. The model explanations enhanced teachers’ trust and technology acceptance of the classroom discourse models. The seven STEM teachers expressed satisfaction with the explanations and provided their perception of model explanations.
{"title":"Making AI Accessible for STEM Teachers: Using Explainable AI for Unpacking Classroom Discourse Analysis","authors":"Deliang Wang;Gaowei Chen","doi":"10.1109/TE.2024.3421606","DOIUrl":"10.1109/TE.2024.3421606","url":null,"abstract":"Contributions: To address the interpretability issues in artificial intelligence (AI)-powered classroom discourse models, we employ explainable AI methods to unpack classroom discourse analysis from deep learning-based models and evaluate the effects of model explanations on STEM teachers. Background: Deep learning techniques have been used to automatically analyze classroom dialogue to provide feedback for teachers. However, these complex models operate as black boxes, lacking clear explanations of the analysis, which may lead teachers, particularly those lacking AI knowledge, to distrust the models and hinder their teaching practice. Therefore, it is crucial to address the interpretability issue in AI-powered classroom discourse models. Research Questions: How to explain deep learning-based classroom discourse models using explainable AI methods? What is the effect of these explanations on teachers’ trust in and technology acceptance of the models? How do teachers perceive the explanations of deep learning-based classroom discourse models? Method: Two explainable AI methods were employed to interpret deep learning-based models that analyzed teacher and student talk moves. A pilot study was conducted, involving seven STEM teachers interested in learning talk moves and receiving classroom discourse analysis. The study assessed changes in teachers’ trust and technology acceptance before and after receiving model explanations. Teachers’ perceptions of the model explanations were investigated. Findings: The AI-powered classroom discourse models were effectively explained using explainable AI methods. The model explanations enhanced teachers’ trust and technology acceptance of the classroom discourse models. The seven STEM teachers expressed satisfaction with the explanations and provided their perception of model explanations.","PeriodicalId":55011,"journal":{"name":"IEEE Transactions on Education","volume":"67 6","pages":"907-918"},"PeriodicalIF":2.1,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141740687","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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