Pub Date : 2024-04-05DOI: 10.1109/TLT.2024.3385505
Shujia Fan;Brian Yecies;Zeyang Ivy Zhou;Jun Shen
The metaverse, along with its various Web 3.0 subdomains, represents a ground-breaking extension of both the physical and digital worlds. In this emerging landscape, the real and virtual worlds are integrating in a way that allows for seamless interactions, creating an immersive experience. This integration has significant implications for diverse fields, particularly in reshaping both online and traditional education methods. By analyzing 417 comprehensive white papers released in 2022 and 2023 from leading consulting firms and think tanks, and incorporating insights from academic articles, we have extracted key information about how metaverse technologies are influencing education over time. Our investigation unveils the key impacts of metaverse technologies on the educational landscape, contributing to a more profound understanding of the transformative effects of the metaverse on the educational terrain in flux. Moreover, our study provides a holistic perspective on the advantages and disadvantages associated with metaverse education, offering in-depth insights into the challenges involved in seamlessly integrating the metaverse into educational practices. Furthermore, our research also highlights the challenges and opportunities presented by the metaverse and its impact on new educational paradigms.
元宇宙及其各种 Web 3.0 子域代表了物理世界和数字世界的突破性延伸。在这一新兴领域，现实世界和虚拟世界正在以一种无缝互动的方式融为一体，创造出一种身临其境的体验。这种融合对各个领域都有重大影响，尤其是在重塑在线教育和传统教育方法方面。通过分析领先咨询公司和智库在 2022 年和 2023 年发布的 417 份综合白皮书，并结合学术文章中的见解，我们提取了有关元虚拟技术如何随着时间的推移影响教育的关键信息。我们的调查揭示了元世界技术对教育领域的关键影响，有助于人们更深刻地理解元世界对不断变化的教育领域的变革性影响。此外，我们的研究还从整体角度探讨了与元数据教育相关的优势和劣势，深入揭示了将元数据无缝融入教育实践所面临的挑战。此外，我们的研究还强调了元海外带来的挑战和机遇及其对新教育范式的影响。
{"title":"Challenges and Opportunities for the Web 3.0 Metaverse Turn in Education","authors":"Shujia Fan;Brian Yecies;Zeyang Ivy Zhou;Jun Shen","doi":"10.1109/TLT.2024.3385505","DOIUrl":"10.1109/TLT.2024.3385505","url":null,"abstract":"The metaverse, along with its various Web 3.0 subdomains, represents a ground-breaking extension of both the physical and digital worlds. In this emerging landscape, the real and virtual worlds are integrating in a way that allows for seamless interactions, creating an immersive experience. This integration has significant implications for diverse fields, particularly in reshaping both online and traditional education methods. By analyzing 417 comprehensive white papers released in 2022 and 2023 from leading consulting firms and think tanks, and incorporating insights from academic articles, we have extracted key information about how metaverse technologies are influencing education over time. Our investigation unveils the key impacts of metaverse technologies on the educational landscape, contributing to a more profound understanding of the transformative effects of the metaverse on the educational terrain in flux. Moreover, our study provides a holistic perspective on the advantages and disadvantages associated with metaverse education, offering in-depth insights into the challenges involved in seamlessly integrating the metaverse into educational practices. Furthermore, our research also highlights the challenges and opportunities presented by the metaverse and its impact on new educational paradigms.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1989-2004"},"PeriodicalIF":2.9,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-04DOI: 10.1109/TLT.2024.3385009
Elizabeth Koh;Lishan Zhang;Alwyn Vwen Yen Lee;Hongye Wang
Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning applications. This article examines the word cloud, a toolkit often used to scaffold teaching and learning for reflection, critical thinking, and content learning. Addressing the issues in traditional word clouds, semantic word clouds have been developed but they are technically challenging to develop and still problematic. However, generative AI has the potential to develop efficient, accurate, creative, and accessible word clouds. Three different methods representing three major approaches of word cloud generation were developed, implemented, and user evaluated—traditional (baseline), semantic (natural language processing enhanced), and generative AI (generative pretrained transformer based)—in two different language contexts—Chinese (China case) and English (Singapore case). The findings of the study show the technical robustness of the methods, as well as provide key pedagogical insights from the user perspective of instructors of higher education courses in China and Singapore. Implications to the design of word clouds and their application in teaching and learning are discussed.
{"title":"Revolutionizing Word Clouds for Teaching and Learning With Generative Artificial Intelligence: Cases From China and Singapore","authors":"Elizabeth Koh;Lishan Zhang;Alwyn Vwen Yen Lee;Hongye Wang","doi":"10.1109/TLT.2024.3385009","DOIUrl":"10.1109/TLT.2024.3385009","url":null,"abstract":"Generative artificial intelligence (AI) has the potential to revolutionize teaching and learning applications. This article examines the word cloud, a toolkit often used to scaffold teaching and learning for reflection, critical thinking, and content learning. Addressing the issues in traditional word clouds, semantic word clouds have been developed but they are technically challenging to develop and still problematic. However, generative AI has the potential to develop efficient, accurate, creative, and accessible word clouds. Three different methods representing three major approaches of word cloud generation were developed, implemented, and user evaluated—traditional (baseline), semantic (natural language processing enhanced), and generative AI (generative pretrained transformer based)—in two different language contexts—Chinese (China case) and English (Singapore case). The findings of the study show the technical robustness of the methods, as well as provide key pedagogical insights from the user perspective of instructors of higher education courses in China and Singapore. Implications to the design of word clouds and their application in teaching and learning are discussed.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1416-1427"},"PeriodicalIF":3.7,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores and analyzes the specific performance of large language models (LLMs) in instructional design, aiming to unveil their potential strengths and possible weaknesses. Recently, the influence of LLMs has gradually increased in multiple fields, yet exploratory research on their application in education remains relatively scarce. In response to this situation, our research, grounded in pedagogical content knowledge theory, initially formulated an instructional design framework based on mathematical problem chains and corresponding prompt instructions. Subsequently, a comprehensive tool for assessing LLM's instructional design capabilities was developed. Utilizing Generative Pretrained Transformer 4, a high school mathematics teaching plan dataset was generated. Finally, the performance of LLMs in instructional design was evaluated. The evaluation results revealed that the teaching plans generated by LLMs excel in setting instructional objectives, identifying teaching priorities, organizing problem chains and teaching activities, articulating subject content, and selecting methods and strategies. Particularly commendable performance was noted in the modules of statistics and functions. However, there is room for improvement in aspects related to mathematical culture and interdisciplinary assessment, as well as in the geometry and algebra modules. Lastly, this study proposes initiatives, such as LLM prompt-based teacher training and the integration of mathematics-focused LLMs. These suggestions aim to advance personalized instructional design and professional development of teachers, offering educators new insights into the in-depth application of LLMs.
{"title":"Teaching Plan Generation and Evaluation With GPT-4: Unleashing the Potential of LLM in Instructional Design","authors":"Bihao Hu;Longwei Zheng;Jiayi Zhu;Lishan Ding;Yilei Wang;Xiaoqing Gu","doi":"10.1109/TLT.2024.3384765","DOIUrl":"10.1109/TLT.2024.3384765","url":null,"abstract":"This study explores and analyzes the specific performance of large language models (LLMs) in instructional design, aiming to unveil their potential strengths and possible weaknesses. Recently, the influence of LLMs has gradually increased in multiple fields, yet exploratory research on their application in education remains relatively scarce. In response to this situation, our research, grounded in pedagogical content knowledge theory, initially formulated an instructional design framework based on mathematical problem chains and corresponding prompt instructions. Subsequently, a comprehensive tool for assessing LLM's instructional design capabilities was developed. Utilizing Generative Pretrained Transformer 4, a high school mathematics teaching plan dataset was generated. Finally, the performance of LLMs in instructional design was evaluated. The evaluation results revealed that the teaching plans generated by LLMs excel in setting instructional objectives, identifying teaching priorities, organizing problem chains and teaching activities, articulating subject content, and selecting methods and strategies. Particularly commendable performance was noted in the modules of statistics and functions. However, there is room for improvement in aspects related to mathematical culture and interdisciplinary assessment, as well as in the geometry and algebra modules. Lastly, this study proposes initiatives, such as LLM prompt-based teacher training and the integration of mathematics-focused LLMs. These suggestions aim to advance personalized instructional design and professional development of teachers, offering educators new insights into the in-depth application of LLMs.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1471-1485"},"PeriodicalIF":3.7,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-02DOI: 10.1109/TLT.2024.3384290
Ka-Yan Fung;Kit-Yi Tang;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song
Prescreening children for specific learning disabilities, e.g., dyslexia, is essential for effective intervention. With a quick and reliable prescreening result, special education coordinators (SENCOs) can provide students with early intervention and relieve their learning pressure. Unfortunately, due to the limited resources, many students in Hong Kong receive dyslexia assessments beyond the golden period, i.e., under the age of six. To this end, information technology could establish automatic prescreening tools to address this issue. However, dyslexia prescreening for children learning Chinese is challenging due to the lack of sound–script correlation in Chinese. In this article, an automatic dyslexia prescreening system (ADPS) is developed to provide a quick test to identify at-risk children. Through a two-stage approach, we first develop a gamified tool based on linguistic characteristics and then evaluate the result by a comparison study. Results from a pilot test on 30 students with dyslexia and 32 students without dyslexia indicate that the ADPS can effectively distinguish between two groups of students. Furthermore, the interactive design elements can motivate students to conduct the prescreening independently.
{"title":"ADPS—A Prescreening Tool for Students With Dyslexia in Learning Traditional Chinese","authors":"Ka-Yan Fung;Kit-Yi Tang;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song","doi":"10.1109/TLT.2024.3384290","DOIUrl":"10.1109/TLT.2024.3384290","url":null,"abstract":"Prescreening children for specific learning disabilities, e.g., dyslexia, is essential for effective intervention. With a quick and reliable prescreening result, special education coordinators (SENCOs) can provide students with early intervention and relieve their learning pressure. Unfortunately, due to the limited resources, many students in Hong Kong receive dyslexia assessments beyond the golden period, i.e., under the age of six. To this end, information technology could establish automatic prescreening tools to address this issue. However, dyslexia prescreening for children learning Chinese is challenging due to the lack of sound–script correlation in Chinese. In this article, an automatic dyslexia prescreening system (ADPS) is developed to provide a quick test to identify at-risk children. Through a two-stage approach, we first develop a gamified tool based on linguistic characteristics and then evaluate the result by a comparison study. Results from a pilot test on 30 students with dyslexia and 32 students without dyslexia indicate that the ADPS can effectively distinguish between two groups of students. Furthermore, the interactive design elements can motivate students to conduct the prescreening independently.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1454-1470"},"PeriodicalIF":3.7,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.1109/TLT.2024.3383773
Thi Thuy An Ngo;Thanh Tu Tran;Gia Khuong An;Phuong Thy Nguyen
The growing prevalence of advanced generative artificial intelligence chatbots, such as ChatGPT, in the educational sector has raised considerable interest in understanding their impact on student knowledge and exploring effective and sustainable implementation strategies. This research investigates the influence of knowledge management factors on the continuous usage of ChatGPT for educational purposes while concurrently evaluating student satisfaction with its use in learning. Using a quantitative approach, a structured questionnaire was administered to 513 Vietnamese university students via Google Forms for data collection. The partial least squares structural equation modeling statistical technique was employed to examine the relationships between identified factors and evaluate the research model. The results provided strong support for several hypotheses, revealing significant positive effects of expectation confirmation on perceived usefulness and satisfaction, as well as perceived usefulness on user satisfaction and continuous usage of ChatGPT. These findings suggest that when students recognize the usefulness of ChatGPT for their learnings, they experience higher satisfaction and are more likely to continue using it. In addition, knowledge acquisition significantly impacts both satisfaction and continuous usage of ChatGPT, while knowledge sharing and application influence satisfaction exclusively. This indicates that students prioritize knowledge acquisition over sharing and applying knowledge through ChatGPT. The study has theoretical and practical implications for ChatGPT developers, educators, and future research. Theoretically, it contributes to understanding satisfaction and continuous usage in educational settings, utilizing the expectation confirmation model and integrating knowledge management factors. Practically, it provides insights into comprehension and suggestions for enhancing user satisfaction and continuous usage of ChatGPT in education.
{"title":"ChatGPT for Educational Purposes: Investigating the Impact of Knowledge Management Factors on Student Satisfaction and Continuous Usage","authors":"Thi Thuy An Ngo;Thanh Tu Tran;Gia Khuong An;Phuong Thy Nguyen","doi":"10.1109/TLT.2024.3383773","DOIUrl":"https://doi.org/10.1109/TLT.2024.3383773","url":null,"abstract":"The growing prevalence of advanced generative artificial intelligence chatbots, such as ChatGPT, in the educational sector has raised considerable interest in understanding their impact on student knowledge and exploring effective and sustainable implementation strategies. This research investigates the influence of knowledge management factors on the continuous usage of ChatGPT for educational purposes while concurrently evaluating student satisfaction with its use in learning. Using a quantitative approach, a structured questionnaire was administered to 513 Vietnamese university students via Google Forms for data collection. The partial least squares structural equation modeling statistical technique was employed to examine the relationships between identified factors and evaluate the research model. The results provided strong support for several hypotheses, revealing significant positive effects of expectation confirmation on perceived usefulness and satisfaction, as well as perceived usefulness on user satisfaction and continuous usage of ChatGPT. These findings suggest that when students recognize the usefulness of ChatGPT for their learnings, they experience higher satisfaction and are more likely to continue using it. In addition, knowledge acquisition significantly impacts both satisfaction and continuous usage of ChatGPT, while knowledge sharing and application influence satisfaction exclusively. This indicates that students prioritize knowledge acquisition over sharing and applying knowledge through ChatGPT. The study has theoretical and practical implications for ChatGPT developers, educators, and future research. Theoretically, it contributes to understanding satisfaction and continuous usage in educational settings, utilizing the expectation confirmation model and integrating knowledge management factors. Practically, it provides insights into comprehension and suggestions for enhancing user satisfaction and continuous usage of ChatGPT in education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1367-1378"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140550172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-31DOI: 10.1109/TLT.2024.3406964
Seok-Hyun Ga;Changmi Park;Hyun-Jung Cha;Chan-Jong Kim
Data collection is crucial in securing evidence to support students’ arguments during scientific inquiries. However, due to the high costs associated with equipping schools with various measurement devices, students are limited in the scope of their scientific inquiry. Arduino can be proposed as a solution to the lack of measurement devices in schools. With Arduino, students can create various measurement devices by connecting different sensors, customize these devices to suit their inquiries, and implement remote sensing using the Internet of Things. However, even when promising new technology serves as a beneficial tool for teaching and learning, its successful integration into the educational system can be challenging if teachers struggle to use it. Technical issues often discourage teachers from incorporating potentially valuable technologies into their classrooms. This article examined the adoption of Arduino in three different cases involving teachers from various educational institutions: a gifted education center, an autonomous club activity in a middle school, and a local community center. We identified four major difficulties: 1) selection of appropriate technologies; 2) credibility issues with information from the Internet; 3) technical complexity due to the intervention of multiple variables; and 4) compliance issues with related acts and regulations. We described each of the technical challenges that teachers faced, in detail, and how they dealt with them. Finally, we discussed suggestions for reducing the barriers to Arduino use for teachers and proposed areas for further research.
{"title":"Science Teachers’ Technical Difficulties in Using Physical Computing and the Internet of Things Into School Science Inquiry","authors":"Seok-Hyun Ga;Changmi Park;Hyun-Jung Cha;Chan-Jong Kim","doi":"10.1109/TLT.2024.3406964","DOIUrl":"10.1109/TLT.2024.3406964","url":null,"abstract":"Data collection is crucial in securing evidence to support students’ arguments during scientific inquiries. However, due to the high costs associated with equipping schools with various measurement devices, students are limited in the scope of their scientific inquiry. Arduino can be proposed as a solution to the lack of measurement devices in schools. With Arduino, students can create various measurement devices by connecting different sensors, customize these devices to suit their inquiries, and implement remote sensing using the Internet of Things. However, even when promising new technology serves as a beneficial tool for teaching and learning, its successful integration into the educational system can be challenging if teachers struggle to use it. Technical issues often discourage teachers from incorporating potentially valuable technologies into their classrooms. This article examined the adoption of Arduino in three different cases involving teachers from various educational institutions: a gifted education center, an autonomous club activity in a middle school, and a local community center. We identified four major difficulties: 1) selection of appropriate technologies; 2) credibility issues with information from the Internet; 3) technical complexity due to the intervention of multiple variables; and 4) compliance issues with related acts and regulations. We described each of the technical challenges that teachers faced, in detail, and how they dealt with them. Finally, we discussed suggestions for reducing the barriers to Arduino use for teachers and proposed areas for further research.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1849-1858"},"PeriodicalIF":2.9,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.1109/TLT.2024.3405966
Ye Zhang;Mo Wang;Jinlong He;Niantong Li;Yupeng Zhou;Haoxia Huang;Dunbo Cai;Minghao Yin
Diagnosing aesthetic perception plays a crucial role in deepening our understanding of student creativity, emotional expression, and the pursuit of lifelong learning within art education. This task encompasses the evaluation and analysis of students' sensitivity, preference, and capacity to perceive and appreciate beauty across different sensory domains. Currently, this assessment frequently relies on subjective evaluations of student artworks. There are two limitations: 1) the diagnosis is possibly limited by instructors' bias and 2) the heavy workload of instructors for conducting comprehensive assessments. These limitations motivate us to ask: Can we automatically and objectively conduct aesthetic perception diagnosis? To this end, we propose an innovative deep hybrid framework, AestheNet, to automatically evaluate aesthetic perception by analyzing numerous collected student paintings. More especially, we first utilize convolutional neural networks to extract the significant features from the student artworks. Then, we employ the transformer model to capture the intricate relationships among multiple aesthetic perception dimensions for objective diagnosis. Finally, we validate the effectiveness of the framework by creating a new dataset consisting of 2153 paintings drawn by 675 students. These paintings are annotated by human experts from 77 dimensions based on domain expertise. Extensive experiments have shown the effectiveness of AestheNet in aesthetic perception diagnosis. AestheNet is dedicated to overcoming the subjectivity inherent in traditional assessment methods, providing a new, quantifiable, and standardized way to evaluate aesthetic perception. This research not only opens up new perspectives in understanding students' aesthetic development during the art education process but also explores the innovation of using artificial intelligence technologies in the assessment of art education.
{"title":"AestheNet: Revolutionizing Aesthetic Perception Diagnosis in Education With Hybrid Deep Nets","authors":"Ye Zhang;Mo Wang;Jinlong He;Niantong Li;Yupeng Zhou;Haoxia Huang;Dunbo Cai;Minghao Yin","doi":"10.1109/TLT.2024.3405966","DOIUrl":"10.1109/TLT.2024.3405966","url":null,"abstract":"Diagnosing aesthetic perception plays a crucial role in deepening our understanding of student creativity, emotional expression, and the pursuit of lifelong learning within art education. This task encompasses the evaluation and analysis of students' sensitivity, preference, and capacity to perceive and appreciate beauty across different sensory domains. Currently, this assessment frequently relies on subjective evaluations of student artworks. There are two limitations: 1) the diagnosis is possibly limited by instructors' bias and 2) the heavy workload of instructors for conducting comprehensive assessments. These limitations motivate us to ask: \u0000<italic>Can we automatically and objectively conduct aesthetic perception diagnosis?</i>\u0000 To this end, we propose an innovative deep hybrid framework, AestheNet, to automatically evaluate aesthetic perception by analyzing numerous collected student paintings. More especially, we first utilize convolutional neural networks to extract the significant features from the student artworks. Then, we employ the transformer model to capture the intricate relationships among multiple aesthetic perception dimensions for objective diagnosis. Finally, we validate the effectiveness of the framework by creating a new dataset consisting of 2153 paintings drawn by 675 students. These paintings are annotated by human experts from 77 dimensions based on domain expertise. Extensive experiments have shown the effectiveness of AestheNet in aesthetic perception diagnosis. AestheNet is dedicated to overcoming the subjectivity inherent in traditional assessment methods, providing a new, quantifiable, and standardized way to evaluate aesthetic perception. This research not only opens up new perspectives in understanding students' aesthetic development during the art education process but also explores the innovation of using artificial intelligence technologies in the assessment of art education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"2117-2129"},"PeriodicalIF":2.9,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.1109/TLT.2024.3405565
Ricardo Conejo Muñoz;Beatriz Barros Blanco;José del Campo-Ávila;José L. Triviño Rodriguez
Automatic question generation and the assessment of procedural knowledge is still a challenging research topic. This article focuses on the case of it, the techniques of parsing grammars for compiler construction. There are two well-known techniques for parsing: top-down parsing with LL(1) and bottom-up with LR(1). Learning these techniques and learning to design grammars that can be parsed with these techniques requires practice. This article describes an application that covers all the tasks needed to automatize the learning and assessment process: 1) automatically generate context-free languages and grammars of different complexity; 2) pose different types of questions to the student with an appropriate response interface; 3) automatically correct the student answer, including grammar design for a given language; and 4) provide feedback on errors. The application has been implemented as a plug-in of the SIETTE assessment system that, in addition, can provide adaptive behavior for question selection. It has been successfully used by more than a thousand students for formative and summative assessment.
{"title":"Teaching Compilers: Automatic Question Generation and Intelligent Assessment of Grammars' Parsing","authors":"Ricardo Conejo Muñoz;Beatriz Barros Blanco;José del Campo-Ávila;José L. Triviño Rodriguez","doi":"10.1109/TLT.2024.3405565","DOIUrl":"10.1109/TLT.2024.3405565","url":null,"abstract":"Automatic question generation and the assessment of procedural knowledge is still a challenging research topic. This article focuses on the case of it, the techniques of parsing grammars for compiler construction. There are two well-known techniques for parsing: top-down parsing with LL(1) and bottom-up with LR(1). Learning these techniques and learning to design grammars that can be parsed with these techniques requires practice. This article describes an application that covers all the tasks needed to automatize the learning and assessment process: 1) automatically generate context-free languages and grammars of different complexity; 2) pose different types of questions to the student with an appropriate response interface; 3) automatically correct the student answer, including grammar design for a given language; and 4) provide feedback on errors. The application has been implemented as a plug-in of the SIETTE assessment system that, in addition, can provide adaptive behavior for question selection. It has been successfully used by more than a thousand students for formative and summative assessment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1734-1744"},"PeriodicalIF":3.7,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10540326","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-28DOI: 10.1109/TLT.2024.3406638
Ye Jia;Xiangzhi Eric Wang;Zackary P. T. Sin;Chen Li;Peter H. F. Ng;Xiao Huang;George Baciu;Jiannong Cao;Qing Li
One of the promises of edu-metaverse is its ability to provide a virtual environment that enables us to engage in learning activities that are similar to or on par with reality. The digital enhancements introduced in a virtual environment contribute to our increased expectations of novel learning experiences. However, despite its promising outcomes, there appears to be limited adoption of the edu-metaverse for practical learning at this time. We believe this can be attributed to the fact that there is a lack of investigation into learners' behavior given a social learning environment. This lack of investigation is critical, as without behavioral insight, it hinders the development of education material and the direction of an edu-metaverse. Upon completing our work with the pilot user studies, we provide the following insights: 1) compared to Zoom, a typical video conferencing and remote collaboration platform, learners in the edu-metaverse demonstrate heightened involvement in learning activities, particularly when drawing mind mapping aided by the embedded knowledge graph, and this copresence significantly boosts learner engagement and collaborative contribution to the learning tasks; and 2) the interaction and learning activity design within the edu-metaverse, especially concerning the use of MM.
教育虚拟世界（edu-metaverse）的承诺之一是，它能够提供一个虚拟环境，使我们能够参与与现实相似或相同的学习活动。虚拟环境中引入的数字增强功能有助于提高我们对新奇学习体验的期望。然而，尽管其成果令人期待，但目前在实际学习中采用教育虚拟世界的情况似乎很有限。我们认为，这是由于缺乏对学习者在社会学习环境中的行为的调查。这种调查的缺乏至关重要，因为没有对行为的深入了解，就会阻碍教育材料的开发和教育虚拟世界的发展方向。在完成试点用户研究工作后，我们提出了以下见解：1）与 Zoom（典型的视频会议和远程协作平台）相比，"edu-metaverse "中的学习者在学习活动中表现出更高的参与度，尤其是在绘制由嵌入式知识图谱辅助的思维导图时，这种共存性显著提高了学习者的参与度和对学习任务的协作贡献；以及2）"edu-metaverse "中的交互和学习活动设计，尤其是关于 MM.
{"title":"Knowledge-Graph-Driven Mind Mapping for Immersive Collaborative Learning: A Pilot Study in Edu-Metaverse","authors":"Ye Jia;Xiangzhi Eric Wang;Zackary P. T. Sin;Chen Li;Peter H. F. Ng;Xiao Huang;George Baciu;Jiannong Cao;Qing Li","doi":"10.1109/TLT.2024.3406638","DOIUrl":"10.1109/TLT.2024.3406638","url":null,"abstract":"One of the promises of edu-metaverse is its ability to provide a virtual environment that enables us to engage in learning activities that are similar to or on par with reality. The digital enhancements introduced in a virtual environment contribute to our increased expectations of novel learning experiences. However, despite its promising outcomes, there appears to be limited adoption of the edu-metaverse for practical learning at this time. We believe this can be attributed to the fact that there is a lack of investigation into learners' behavior given a social learning environment. This lack of investigation is critical, as without behavioral insight, it hinders the development of education material and the direction of an edu-metaverse. Upon completing our work with the pilot user studies, we provide the following insights: 1) compared to Zoom, a typical video conferencing and remote collaboration platform, learners in the edu-metaverse demonstrate heightened involvement in learning activities, particularly when drawing mind mapping aided by the embedded knowledge graph, and this copresence significantly boosts learner engagement and collaborative contribution to the learning tasks; and 2) the interaction and learning activity design within the edu-metaverse, especially concerning the use of MM.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1834-1848"},"PeriodicalIF":2.9,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10540301","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Teaching robotics with the robot operating system (ROS) is valuable for instating good programming practices but requires significant setup steps from the learner. Providing a ready-made ROS learning environment over the web can make robotics more accessible; however, most of the previous remote labs have abstracted the authentic ROS developer environment either for didactical or technological reasons, or do not give the possibility to program physical robots. In this article, we present a remote web lab that employs virtual network computing and Docker to serve in-browser desktop workstations, where learning tasks can be completed on both the physical and simulated robots. The learners can reserve access to the remote lab through a learning management interface, which also includes tools for administering the remote lab. The system allows anyone to experiment with ROS without configuring any software locally and was successfully trialed in an online ROS course.
{"title":"Open Remote Web Lab for Learning Robotics and ROS With Physical and Simulated Robots in an Authentic Developer Environment","authors":"Dāvis Krūmiņš;Sandra Schumann;Veiko Vunder;Rauno Põlluäär;Kristjan Laht;Renno Raudmäe;Alvo Aabloo;Karl Kruusamäe","doi":"10.1109/TLT.2024.3381858","DOIUrl":"10.1109/TLT.2024.3381858","url":null,"abstract":"Teaching robotics with the robot operating system (ROS) is valuable for instating good programming practices but requires significant setup steps from the learner. Providing a ready-made ROS learning environment over the web can make robotics more accessible; however, most of the previous remote labs have abstracted the authentic ROS developer environment either for didactical or technological reasons, or do not give the possibility to program physical robots. In this article, we present a remote web lab that employs virtual network computing and Docker to serve in-browser desktop workstations, where learning tasks can be completed on both the physical and simulated robots. The learners can reserve access to the remote lab through a learning management interface, which also includes tools for administering the remote lab. The system allows anyone to experiment with ROS without configuring any software locally and was successfully trialed in an online ROS course.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1325-1338"},"PeriodicalIF":3.7,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140316076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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