Pub Date : 2025-01-15DOI: 10.1109/TLT.2024.3513373
Minjuan Wang;John Chi-Kin Lee
{"title":"Editorial: Journey to the Future: Extended Reality and Intelligence Augmentation","authors":"Minjuan Wang;John Chi-Kin Lee","doi":"10.1109/TLT.2024.3513373","DOIUrl":"https://doi.org/10.1109/TLT.2024.3513373","url":null,"abstract":"","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"53-55"},"PeriodicalIF":2.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10841808","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992955","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 : 2025-01-06DOI: 10.1109/TLT.2025.3525949
Malte Rolf Teichmann
Due to the rise of virtual reality and the—at least now—hypothetical construct of the Metaverse, learning processes are increasingly transferred to immersive virtual learning environments. While the literature provides few design guidelines, most papers miss an application and evaluation description of the design and development processes. As a result, few standardized design processes and related design frameworks exist that meaningfully integrate existing stand-alone design theories and resulting approaches for developing immersive virtual learning environments. The article tackles this challenge with a research procedure based on the design science research method to outline and communicate a Design process framework to create virtual learning environments based on real-world processes for the Edu-Metaverse. The simply applicable artifact represents a comprehensive five-step solution to a well-defined problem by combining interdisciplinary perspectives. It contributes to the concretization of the hypothetical term Metaverse in its intended domain. As a result, practitioners and researchers with different experience levels can use the low-threshold framework.
{"title":"How to Design Immersive Virtual Learning Environments Based on Real-World Processes for the Edu-Metaverse—A Design Process Framework","authors":"Malte Rolf Teichmann","doi":"10.1109/TLT.2025.3525949","DOIUrl":"https://doi.org/10.1109/TLT.2025.3525949","url":null,"abstract":"Due to the rise of virtual reality and the—at least now—hypothetical construct of the <italic>Metaverse</i>, learning processes are increasingly transferred to <italic>immersive virtual learning environments</i>. While the literature provides few design guidelines, most papers miss an application and evaluation description of the design and development processes. As a result, few standardized design processes and related design frameworks exist that meaningfully integrate existing stand-alone design theories and resulting approaches for developing <italic>immersive virtual learning environments</i>. The article tackles this challenge with a research procedure based on the design science research method to outline and communicate a <italic>Design process framework to create virtual learning environments based on real-world processes for the Edu-Metaverse</i>. The simply applicable artifact represents a comprehensive five-step solution to a well-defined problem by combining interdisciplinary perspectives. It contributes to the concretization of the hypothetical term <italic>Metaverse</i> in its intended domain. As a result, practitioners and researchers with different experience levels can use the low-threshold framework.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"100-118"},"PeriodicalIF":2.9,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10824930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106036","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}
This article introduces the development process of social presence-enabled augmented reality (SPEAR) tool, an innovative augmented reality (AR) based learning application tailored for online engineering education. SPEAR focuses on a learning module of structural beam-bending, empowering users to seamlessly integrate 3-D virtual beams into their real-world environment, using the AR Foundation framework within the Unity game engine. Learners can explore structural mechanics by manipulating loads and positions. SPEAR leverages a custom C# script based on the finite element method to offer a real-time simulation of beam deformation, accompanied by visualizations of the moment/shear diagrams and bending stresses. In addition, the integration of a cloud-based voice chat feature, photon unity networking 2, enhances social presence, fostering collaborative learning. Usability testing conducted with extended reality developers and structural engineers, utilizing the system usability scale, confirmed SPEAR's user-friendliness and intuitive interface. Results indicate high levels of participant satisfaction, validating its design and functionality. This study contributes to the field by highlighting SPEAR's pedagogical potential to enhance online engineering education through immersive AR experiences and social interaction. It offers a promising avenue for improving student engagement, comprehension, and performance. In addition, SPEAR facilitates future research into new learning theories and materials design strategies. Its versatility makes it a valuable tool for innovative online education approaches, potentially revolutionizing the learning experiences for students worldwide.
{"title":"Developing and Usability Testing of an Augmented Reality Tool for Online Engineering Education","authors":"Saurav Shrestha;Yongwei Shan;Robert Emerson;Zahrasadat Hosseini","doi":"10.1109/TLT.2024.3520413","DOIUrl":"https://doi.org/10.1109/TLT.2024.3520413","url":null,"abstract":"This article introduces the development process of social presence-enabled augmented reality (SPEAR) tool, an innovative augmented reality (AR) based learning application tailored for online engineering education. SPEAR focuses on a learning module of structural beam-bending, empowering users to seamlessly integrate 3-D virtual beams into their real-world environment, using the AR Foundation framework within the Unity game engine. Learners can explore structural mechanics by manipulating loads and positions. SPEAR leverages a custom C# script based on the finite element method to offer a real-time simulation of beam deformation, accompanied by visualizations of the moment/shear diagrams and bending stresses. In addition, the integration of a cloud-based voice chat feature, photon unity networking 2, enhances social presence, fostering collaborative learning. Usability testing conducted with extended reality developers and structural engineers, utilizing the system usability scale, confirmed SPEAR's user-friendliness and intuitive interface. Results indicate high levels of participant satisfaction, validating its design and functionality. This study contributes to the field by highlighting SPEAR's pedagogical potential to enhance online engineering education through immersive AR experiences and social interaction. It offers a promising avenue for improving student engagement, comprehension, and performance. In addition, SPEAR facilitates future research into new learning theories and materials design strategies. Its versatility makes it a valuable tool for innovative online education approaches, potentially revolutionizing the learning experiences for students worldwide.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"13-24"},"PeriodicalIF":2.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937842","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 : 2024-12-26DOI: 10.1109/TLT.2024.3523199
Weijiao Huang;Khe Foon Hew
In an online learning environment, both instruction and assessments take place virtually where students are primarily responsible for managing their own learning. This requires a high level of self-regulation from students. Many online students, however, lack self-regulation skills and are ill-prepared for autonomous learning, which can cause students to feel disengaged from online activities. In addition, students tend to feel isolated during online activities due to limited social interaction. To address these challenges, this study explores the use of chatbots to facilitate students’ self-regulated learning strategies and promote social presence to alleviate their feelings of isolation. Using a two-phase mixed-methods design, this study evaluates students’ behavioral engagement, perceived self-regulated learning strategies, and social presence in chatbot-supported online learning. In the first phase (Stage I Study), 39 students engaged in a goal-setting chatbot activity that employed the SMART framework and social presence indicators. The findings served as the basis for improving the chatbot design in the second phase (Stage II Study), in which 25 students interacted with the revised chatbot, focusing on goal-setting, help-seeking, self-evaluation, and social interaction with instructor's presence. The results show that the students in both studies had positive online learning experiences with the chatbots. Follow-up interviews with students and instructors provide valuable insights and suggestions for refining the chatbot design, such as chatbots for ongoing monitoring of self-regulation habits and personalized social interaction. Drawing from the evidence, we discuss a set of chatbot design principles that support students’ self-regulated learning and social presence in online settings.
{"title":"Facilitating Online Self-Regulated Learning and Social Presence Using Chatbots: Evidence-Based Design Principles","authors":"Weijiao Huang;Khe Foon Hew","doi":"10.1109/TLT.2024.3523199","DOIUrl":"https://doi.org/10.1109/TLT.2024.3523199","url":null,"abstract":"In an online learning environment, both instruction and assessments take place virtually where students are primarily responsible for managing their own learning. This requires a high level of self-regulation from students. Many online students, however, lack self-regulation skills and are ill-prepared for autonomous learning, which can cause students to feel disengaged from online activities. In addition, students tend to feel isolated during online activities due to limited social interaction. To address these challenges, this study explores the use of chatbots to facilitate students’ self-regulated learning strategies and promote social presence to alleviate their feelings of isolation. Using a two-phase mixed-methods design, this study evaluates students’ behavioral engagement, perceived self-regulated learning strategies, and social presence in chatbot-supported online learning. In the first phase (Stage I Study), 39 students engaged in a goal-setting chatbot activity that employed the SMART framework and social presence indicators. The findings served as the basis for improving the chatbot design in the second phase (Stage II Study), in which 25 students interacted with the revised chatbot, focusing on goal-setting, help-seeking, self-evaluation, and social interaction with instructor's presence. The results show that the students in both studies had positive online learning experiences with the chatbots. Follow-up interviews with students and instructors provide valuable insights and suggestions for refining the chatbot design, such as chatbots for ongoing monitoring of self-regulation habits and personalized social interaction. Drawing from the evidence, we discuss a set of chatbot design principles that support students’ self-regulated learning and social presence in online settings.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"56-71"},"PeriodicalIF":2.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992954","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}
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises has been proven effective for this task. One of the primary challenges in AR modeling for KT is effectively representing the anterior (preresponse) and posterior (postresponse) states of learners across exercises. Existing methods often employ complex model architectures to update learner states using question and response records. In this study, we propose a novel perspective on KT task by treating it as a generative process, consistent with the principles of AR models. We demonstrate that knowledge states can be directly represented through AR encodings on a question–response alternate sequence, where model generate the most probable representation in hidden state space by analyzing history interactions. This approach underpins our framework, termed alternate autoregressive KT (AAKT). In addition, we incorporate supplementary educational information, such as question-related skills, into our framework through an auxiliary task, and include extra exercise details, such as response time, as additional inputs. Our proposed framework is implemented using advanced AR technologies from Natural Language Generation for both training and prediction. Empirical evaluations on four real-world KT datasets indicate that AAKT consistently outperforms all baseline models in terms of area under the receiver operating characteristic curve, accuracy, and root mean square error. Furthermore, extensive ablation studies and visualized analysis validate the effectiveness of key components in AAKT.
{"title":"AAKT: Enhancing Knowledge Tracing With Alternate Autoregressive Modeling","authors":"Hao Zhou;Wenge Rong;Jianfei Zhang;Qing Sun;Yuanxin Ouyang;Zhang Xiong","doi":"10.1109/TLT.2024.3521898","DOIUrl":"https://doi.org/10.1109/TLT.2024.3521898","url":null,"abstract":"Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises has been proven effective for this task. One of the primary challenges in AR modeling for KT is effectively representing the anterior (preresponse) and posterior (postresponse) states of learners across exercises. Existing methods often employ complex model architectures to update learner states using question and response records. In this study, we propose a novel perspective on KT task by treating it as a generative process, consistent with the principles of AR models. We demonstrate that knowledge states can be directly represented through AR encodings on a question–response alternate sequence, where model generate the most probable representation in hidden state space by analyzing history interactions. This approach underpins our framework, termed alternate autoregressive KT (AAKT). In addition, we incorporate supplementary educational information, such as question-related skills, into our framework through an auxiliary task, and include extra exercise details, such as response time, as additional inputs. Our proposed framework is implemented using advanced AR technologies from Natural Language Generation for both training and prediction. Empirical evaluations on four real-world KT datasets indicate that AAKT consistently outperforms all baseline models in terms of area under the receiver operating characteristic curve, accuracy, and root mean square error. Furthermore, extensive ablation studies and visualized analysis validate the effectiveness of key components in AAKT.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"25-38"},"PeriodicalIF":2.9,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937840","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 : 2024-12-19DOI: 10.1109/TLT.2024.3520329
Rafael Herrero-Álvarez;Rafael Arnay;Eduardo Segredo;Gara Miranda;Coromoto León
RoblockLLy is an educational robotics simulator designed for primary and secondary school students, whose goal is to increase their interest in science, technology, engineering, and mathematics. In the particular case of computer science, it allows developing computational thinking skills. It has been designed with ease of use in mind. This free tool is available through a web browser and does not need a complex installation or specific hardware requirements, allowing educational robotics to be introduced to a wide range of users by working on practical projects that will help them understand key concepts of robotics and programming. The effectiveness of RoblockLLy has been validated based on motivation, usability, and user experience criteria. The tool was validated with 212 secondary school students (12–16 years old). Specifically, motivation was measured with the Intrinsic Motivation Inventory, usability with the System Usability Scale, and user experience with the User Experience Questionnaire. Generally speaking, the results demonstrate that students perceived RoblockLLy as a novel and interesting tool. The ratings for usability were predominantly positive, although a few students indicated a preference for expert assistance. The overall rating of the user experience was positive as well, yet notable differences in attitudes toward motivation and usability were observed between genders.
{"title":"Using RoblockLLy in the Classroom: Bridging the Gap in Computer Science Education Through Robotics Simulation","authors":"Rafael Herrero-Álvarez;Rafael Arnay;Eduardo Segredo;Gara Miranda;Coromoto León","doi":"10.1109/TLT.2024.3520329","DOIUrl":"https://doi.org/10.1109/TLT.2024.3520329","url":null,"abstract":"RoblockLLy is an educational robotics simulator designed for primary and secondary school students, whose goal is to increase their interest in science, technology, engineering, and mathematics. In the particular case of computer science, it allows developing computational thinking skills. It has been designed with ease of use in mind. This free tool is available through a web browser and does not need a complex installation or specific hardware requirements, allowing educational robotics to be introduced to a wide range of users by working on practical projects that will help them understand key concepts of robotics and programming. The effectiveness of RoblockLLy has been validated based on motivation, usability, and user experience criteria. The tool was validated with 212 secondary school students (12–16 years old). Specifically, motivation was measured with the Intrinsic Motivation Inventory, usability with the System Usability Scale, and user experience with the User Experience Questionnaire. Generally speaking, the results demonstrate that students perceived RoblockLLy as a novel and interesting tool. The ratings for usability were predominantly positive, although a few students indicated a preference for expert assistance. The overall rating of the user experience was positive as well, yet notable differences in attitudes toward motivation and usability were observed between genders.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"39-52"},"PeriodicalIF":2.9,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10807249","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142937841","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 : 2024-12-17DOI: 10.1109/TLT.2024.3514832
Philemon Yalamu;Abdullah Al Mahmud;Caslon Chua
Cultural differences can impact how learning is delivered in higher education. Past studies have overlooked the impacts of specific cultures on learning and technology integration, and therefore, this article explores aspects of culture and resource implications for technology-enabled learning. The study collected data from students, lecturers, and university administrators at the University of Papua New Guinea via questionnaire (n = 58), focus group (n = 15), and interview (n = 2). Descriptive statistics of questionnaire responses supplemented by conventional content analysis of interview and focus group responses revealed perceptions of higher education and teaching in Papua New Guinea (PNG). Often, learning technologies are adopted without careful consideration of the users’ requirements, especially their cultural influences, which sometimes hinder the effective implementation of chosen technologies for learning. This article discusses the key findings related to technology limitations, cultural influences, communication issues, and other challenges unique to PNG. It highlights that students and lecturers are keen to embrace technology in teaching and learning; however, institutional constraints hinder the successful implementation of technology that would allow them to advance learning. This, coupled with cultural influences such as traditional customs, practices, and belief systems, also affects how Western education is delivered. The article concludes with recommendations for implementing culturally sensitive learning management systems that can accommodate infrastructure and social challenges.
{"title":"Investigating Culture and Resource-Sensitive Technology-Enabled Learning","authors":"Philemon Yalamu;Abdullah Al Mahmud;Caslon Chua","doi":"10.1109/TLT.2024.3514832","DOIUrl":"https://doi.org/10.1109/TLT.2024.3514832","url":null,"abstract":"Cultural differences can impact how learning is delivered in higher education. Past studies have overlooked the impacts of specific cultures on learning and technology integration, and therefore, this article explores aspects of culture and resource implications for technology-enabled learning. The study collected data from students, lecturers, and university administrators at the University of Papua New Guinea via questionnaire (<italic>n</i> = 58), focus group (<italic>n</i> = 15), and interview (<italic>n</i> = 2). Descriptive statistics of questionnaire responses supplemented by conventional content analysis of interview and focus group responses revealed perceptions of higher education and teaching in Papua New Guinea (PNG). Often, learning technologies are adopted without careful consideration of the users’ requirements, especially their cultural influences, which sometimes hinder the effective implementation of chosen technologies for learning. This article discusses the key findings related to technology limitations, cultural influences, communication issues, and other challenges unique to PNG. It highlights that students and lecturers are keen to embrace technology in teaching and learning; however, institutional constraints hinder the successful implementation of technology that would allow them to advance learning. This, coupled with cultural influences such as traditional customs, practices, and belief systems, also affects how Western education is delivered. The article concludes with recommendations for implementing culturally sensitive learning management systems that can accommodate infrastructure and social challenges.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"85-99"},"PeriodicalIF":2.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106035","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 : 2024-11-15DOI: 10.1109/TLT.2024.3499751
Tian Song;Hang Zhang;Yijia Xiao
High-quality programming projects for education are critically required in teaching. However, it is hard to develop those projects efficiently and artificially constrained by the lecturers' experience and background. The recent popularity of large language models (LLMs) has led to a great number of applications in the field of education, but concerns persist that the output might be unreliable when dealing with intricate requirements. In this study, we design a customized role-based agent (CRBA), which can be configured for different roles specializing in specific areas of expertise, making the LLM yield content of higher specialization. An iterative architecture of multi-CRBAs is proposed to generate multistep projects, where CRBAs automatically criticize and optimize the LLM's intermediate outputs to enhance quality. We propose ten evaluation metrics across three aspects to assess project quality through expert grading. Further, we conduct an A/B test among 60 undergraduate students in a programming course and collect their feedback through a questionnaire. According to the students' rating results, the LLM-generated projects have comparable performance to man-made ones in terms of project description, learning step setting, assistance to students, and overall project quality. This study effectively integrates LLM into educational scenarios and enhances the efficiency of creating high-quality and practical programming exercises for lecturers.
{"title":"A High-Quality Generation Approach for Educational Programming Projects Using LLM","authors":"Tian Song;Hang Zhang;Yijia Xiao","doi":"10.1109/TLT.2024.3499751","DOIUrl":"https://doi.org/10.1109/TLT.2024.3499751","url":null,"abstract":"High-quality programming projects for education are critically required in teaching. However, it is hard to develop those projects efficiently and artificially constrained by the lecturers' experience and background. The recent popularity of large language models (LLMs) has led to a great number of applications in the field of education, but concerns persist that the output might be unreliable when dealing with intricate requirements. In this study, we design a customized role-based agent (CRBA), which can be configured for different roles specializing in specific areas of expertise, making the LLM yield content of higher specialization. An iterative architecture of multi-CRBAs is proposed to generate multistep projects, where CRBAs automatically criticize and optimize the LLM's intermediate outputs to enhance quality. We propose ten evaluation metrics across three aspects to assess project quality through expert grading. Further, we conduct an A/B test among 60 undergraduate students in a programming course and collect their feedback through a questionnaire. According to the students' rating results, the LLM-generated projects have comparable performance to man-made ones in terms of project description, learning step setting, assistance to students, and overall project quality. This study effectively integrates LLM into educational scenarios and enhances the efficiency of creating high-quality and practical programming exercises for lecturers.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"2296-2309"},"PeriodicalIF":2.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777578","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 : 2024-11-06DOI: 10.1109/TLT.2024.3492352
Peter H. F. Ng;Peter Q. Chen;Astin C. H. Wu;Ken S. K. Tai;Chen Li
This study examines a practical teaching and learning cycle tailored to integrate cutting-edge technologies (artificial intelligence (AI) and machine learning (ML) game development) and social entrepreneurship within a “STEM with meaning” approach. This cycle, rooted in service learning and the 5E constructivist teaching model (engage, explore, explain, elaborate, and evaluate), seeks to move beyond traditional lecture-based methods by promoting a deeper understanding of technology's societal impacts.Through a comparative analysis involving experimental and comparison groups, we evaluate the cycle's effectiveness in enhancing students' problem-solving skills, empathy, knowledge application, and sense of social responsibility—essential qualities for successful social entrepreneurs. This article contributes to the burgeoning field of entrepreneurship education by demonstrating the value of a pedagogical approach that combines AI, ML, and game development with a strong emphasis on social entrepreneurship. Our results advocate a shift toward educational models that prepare students with technical skills and the awareness and capabilities needed to address complex social issues. Through this research, we highlight the critical role of innovative teaching methods in cultivating the next generation of socially responsible entrepreneurs, thereby enriching both the educational landscape and society at large.
{"title":"Reimagining STEM Learning: A Comparative Analysis of Traditional and Service Learning Approaches for Social Entrepreneurship","authors":"Peter H. F. Ng;Peter Q. Chen;Astin C. H. Wu;Ken S. K. Tai;Chen Li","doi":"10.1109/TLT.2024.3492352","DOIUrl":"https://doi.org/10.1109/TLT.2024.3492352","url":null,"abstract":"This study examines a practical teaching and learning cycle tailored to integrate cutting-edge technologies (artificial intelligence (AI) and machine learning (ML) game development) and social entrepreneurship within a “STEM with meaning” approach. This cycle, rooted in service learning and the 5E constructivist teaching model (engage, explore, explain, elaborate, and evaluate), seeks to move beyond traditional lecture-based methods by promoting a deeper understanding of technology's societal impacts.Through a comparative analysis involving experimental and comparison groups, we evaluate the cycle's effectiveness in enhancing students' problem-solving skills, empathy, knowledge application, and sense of social responsibility—essential qualities for successful social entrepreneurs. This article contributes to the burgeoning field of entrepreneurship education by demonstrating the value of a pedagogical approach that combines AI, ML, and game development with a strong emphasis on social entrepreneurship. Our results advocate a shift toward educational models that prepare students with technical skills and the awareness and capabilities needed to address complex social issues. Through this research, we highlight the critical role of innovative teaching methods in cultivating the next generation of socially responsible entrepreneurs, thereby enriching both the educational landscape and society at large.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"2266-2280"},"PeriodicalIF":2.9,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10745650","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777849","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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