首页 > 最新文献

IEEE Transactions on Learning Technologies最新文献

英文 中文
Automated Multimode Teaching Behavior Analysis: A Pipeline-Based Event Segmentation and Description 自动多模式教学行为分析:基于管道的事件分割和描述
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-02 DOI: 10.1109/TLT.2024.3396159
Qiuyu Zheng;Zengzhao Chen;Mengke Wang;Yawen Shi;Shaohui Chen;Zhi Liu
The rationality and the effectiveness of classroom teaching behavior directly influence the quality of classroom instruction. Analyzing teaching behavior intelligently can provide robust data support for teacher development and teaching supervision. By observing verbal and nonverbal behaviors of teachers in the classroom, valuable data on teacher–student interaction, classroom atmosphere, and teacher–student rapport can be obtained. However, traditional approaches of teaching behavior analysis primarily focus on student groups in the classroom, neglecting intelligent analysis and intervention of teacher behavior. Moreover, these traditional methods often rely on manual annotation and decision making, which are time consuming and labor intensive, and cannot efficiently facilitate analysis. To address these limitations, this article proposes an innovative automated multimode teaching behavior analysis framework, known as AMTBA. First, a model for segmenting classroom events is introduced, which separates teacher behavior sequences logically. Next, this article utilizes deep learning strategies with optimal performance to conduct multimode analysis and identification of split classroom events, enabling the fine-grained measurement of teacher's behavior in terms of verbal interaction, emotion, gaze, and position. Overall, we establish a uniform description framework. The AMTBA framework is utilized to analyze eight classrooms, and the obtained teacher behavior data are used to analyze differences. The empirical results reveal the differences of teacher behavior in different types of teachers, different teaching modes, and different classes. These findings provide an efficient solution for large-scale and multidisciplinary educational analysis and demonstrate the practical value of AMTBA in educational analytics.
课堂教学行为的合理性和有效性直接影响课堂教学质量。对教学行为进行智能分析,可以为教师发展和教学督导提供有力的数据支持。通过观察教师在课堂上的言语和非言语行为,可以获得师生互动、课堂气氛、师生默契等方面的宝贵数据。然而,传统的教学行为分析方法主要关注课堂上的学生群体,忽视了对教师行为的智能分析和干预。此外,这些传统方法往往依赖人工标注和决策,耗时耗力,无法有效促进分析工作。针对这些局限性,本文提出了一种创新的自动化多模式教学行为分析框架,即 AMTBA。首先,本文介绍了一种课堂事件分割模型,该模型将教师行为序列进行了逻辑分割。接下来,本文利用性能最优的深度学习策略,对分割后的课堂事件进行多模式分析和识别,从而能够从语言互动、情绪、目光和位置等方面对教师行为进行精细测量。总之,我们建立了一个统一的描述框架。我们利用 AMTBA 框架分析了 8 个课堂,并利用获得的教师行为数据分析了差异。实证结果揭示了不同类型教师、不同教学模式和不同班级的教师行为差异。这些发现为大规模、多学科的教育分析提供了有效的解决方案,并证明了 AMTBA 在教育分析中的实用价值。
{"title":"Automated Multimode Teaching Behavior Analysis: A Pipeline-Based Event Segmentation and Description","authors":"Qiuyu Zheng;Zengzhao Chen;Mengke Wang;Yawen Shi;Shaohui Chen;Zhi Liu","doi":"10.1109/TLT.2024.3396159","DOIUrl":"10.1109/TLT.2024.3396159","url":null,"abstract":"The rationality and the effectiveness of classroom teaching behavior directly influence the quality of classroom instruction. Analyzing teaching behavior intelligently can provide robust data support for teacher development and teaching supervision. By observing verbal and nonverbal behaviors of teachers in the classroom, valuable data on teacher–student interaction, classroom atmosphere, and teacher–student rapport can be obtained. However, traditional approaches of teaching behavior analysis primarily focus on student groups in the classroom, neglecting intelligent analysis and intervention of teacher behavior. Moreover, these traditional methods often rely on manual annotation and decision making, which are time consuming and labor intensive, and cannot efficiently facilitate analysis. To address these limitations, this article proposes an innovative automated multimode teaching behavior analysis framework, known as AMTBA. First, a model for segmenting classroom events is introduced, which separates teacher behavior sequences logically. Next, this article utilizes deep learning strategies with optimal performance to conduct multimode analysis and identification of split classroom events, enabling the fine-grained measurement of teacher's behavior in terms of verbal interaction, emotion, gaze, and position. Overall, we establish a uniform description framework. The AMTBA framework is utilized to analyze eight classrooms, and the obtained teacher behavior data are used to analyze differences. The empirical results reveal the differences of teacher behavior in different types of teachers, different teaching modes, and different classes. These findings provide an efficient solution for large-scale and multidisciplinary educational analysis and demonstrate the practical value of AMTBA in educational analytics.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1717-1733"},"PeriodicalIF":3.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827092","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}
引用次数: 0
Development of an Intelligent Tutoring System That Assesses Internal Visualization Skills in Engineering Using Multimodal Triangulation 利用多模态三角测量法开发评估工程学内部可视化技能的智能辅导系统
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-03-02 DOI: 10.1109/TLT.2024.3396393
Hanall Sung;Martina A. Rau;Barry D. Van Veen
In many science, technology, engineering, and mathematics (STEM) domains, instruction on foundational concepts heavily relies on visuals. Instructors often assume that students can mentally visualize concepts but students often struggle with internal visualization skills—the ability to mentally visualize information. In order to address this issue, we developed a formal, as well as an informal assessment of students’ internal visualization skills in the context of engineering instruction. To validate the assessments, we used data triangulation methods. We drew on data from two separate studies conducted in a small-scale lab experiment and in a larger-scale classroom context. Our studies demonstrate that an intelligent tutoring system with interactive visual representations can serve as an informal assessment of students’ internal visualization skills, predicting their performance on a formal assessment of these skills. Our study enriches methodological and theoretical underpinnings in educational research and practices in multiple ways: it contributes to research methodologies by illustrating how multimodal triangulation can be used for test development, theories of learning by offering pathways to assessing internal visualization skills that are not directly observable, and instructional practices in STEM education by enabling instructors to determine when and where they should provide additional scaffoldings.
在许多科学、技术、工程和数学(STEM)领域,基础概念的教学在很大程度上依赖于视觉效果。教师通常认为学生能够在头脑中将概念视觉化,但学生往往在内部视觉化技能--在头脑中将信息视觉化的能力--方面存在困难。为了解决这个问题,我们开发了一种正式和非正式的评估方法,以评估学生在工程学教学中的内部可视化技能。为了验证评估结果,我们采用了数据三角测量法。我们利用了在小规模实验室实验和大规模课堂背景下进行的两项独立研究的数据。我们的研究表明,具有交互式可视化表示的智能辅导系统可以作为对学生内部可视化技能的非正式评估,预测他们在这些技能的正式评估中的表现。我们的研究以多种方式丰富了教育研究和实践的方法论和理论基础:它通过说明如何将多模态三角测量用于测试开发,为研究方法论做出了贡献;通过提供评估无法直接观察到的内部可视化技能的途径,为学习理论做出了贡献;通过使教师能够确定何时何地应该提供额外的支架,为 STEM 教育的教学实践做出了贡献。
{"title":"Development of an Intelligent Tutoring System That Assesses Internal Visualization Skills in Engineering Using Multimodal Triangulation","authors":"Hanall Sung;Martina A. Rau;Barry D. Van Veen","doi":"10.1109/TLT.2024.3396393","DOIUrl":"10.1109/TLT.2024.3396393","url":null,"abstract":"In many science, technology, engineering, and mathematics (STEM) domains, instruction on foundational concepts heavily relies on visuals. Instructors often assume that students can mentally visualize concepts but students often struggle with internal visualization skills—the ability to mentally visualize information. In order to address this issue, we developed a formal, as well as an informal assessment of students’ internal visualization skills in the context of engineering instruction. To validate the assessments, we used data triangulation methods. We drew on data from two separate studies conducted in a small-scale lab experiment and in a larger-scale classroom context. Our studies demonstrate that an intelligent tutoring system with interactive visual representations can serve as an informal assessment of students’ internal visualization skills, predicting their performance on a formal assessment of these skills. Our study enriches methodological and theoretical underpinnings in educational research and practices in multiple ways: it contributes to research methodologies by illustrating how multimodal triangulation can be used for test development, theories of learning by offering pathways to assessing internal visualization skills that are not directly observable, and instructional practices in STEM education by enabling instructors to determine when and where they should provide additional scaffoldings.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1625-1638"},"PeriodicalIF":3.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826860","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}
引用次数: 0
Supporting Teachers’ Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy 用生成式人工智能支持教师专业发展:对高阶思维和自我效能的影响
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-26 DOI: 10.1109/TLT.2024.3369690
Jijian Lu;Ruxin Zheng;Zikun Gong;Huifen Xu
Generative artificial intelligence (AI) has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted preservice teaching skills training on preservice teachers’ self-efficacy and higher order thinking. The participants of this study were 215 preservice mathematics, science, and computer teachers from a university in China. First, a pretest–post-test quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Finally, a semistructured interview comprising open-ended questions was administered to 25 preservice teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of preservice teachers in the experimental group, who used generative AI for teachers’ professional development, were considerably higher than those of the control group, both in teacher self-efficacy (F = 8.589, p = 0.0084 < 0.05) and higher order thinking (F = 7.217, p = 0.008 < 0.05). It revealed that generative AI can be effective in supporting teachers’ professional development. This study produced a practical teachers’ professional development method for preservice teachers with generative AI.
生成式人工智能(AI)已成为人类科学技术领域主要学科发展史上值得关注的里程碑和重大进步。本研究旨在探讨生成式人工智能辅助职前教学技能培训对职前教师自我效能感和高阶思维的影响。本研究的参与者是来自中国某大学的 215 名职前数学、科学和计算机教师。首先,对实验组(采用生成式人工智能进行教学技能培训)和对照组(采用传统方法进行教学技能培训)进行了前测-后测的准实验设计,调查了实验前后两组教师的自我效能感和高阶思维。最后,对实验组的 25 名职前教师进行了由开放式问题组成的半结构化访谈,以了解他们对生成式人工智能辅助教学的看法。结果显示,实验组的职前教师在教师自我效能感(F = 8.589,p = 0.0084 < 0.05)和高阶思维(F = 7.217,p = 0.008 < 0.05)方面的得分都大大高于对照组,这说明生成式人工智能可以帮助教师提高专业发展。研究表明,生成式人工智能可以有效地支持教师的专业发展。本研究利用生成式人工智能为职前教师提供了一种实用的教师专业发展方法。
{"title":"Supporting Teachers’ Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy","authors":"Jijian Lu;Ruxin Zheng;Zikun Gong;Huifen Xu","doi":"10.1109/TLT.2024.3369690","DOIUrl":"10.1109/TLT.2024.3369690","url":null,"abstract":"Generative artificial intelligence (AI) has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted preservice teaching skills training on preservice teachers’ self-efficacy and higher order thinking. The participants of this study were 215 preservice mathematics, science, and computer teachers from a university in China. First, a pretest–post-test quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Finally, a semistructured interview comprising open-ended questions was administered to 25 preservice teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of preservice teachers in the experimental group, who used generative AI for teachers’ professional development, were considerably higher than those of the control group, both in teacher self-efficacy (\u0000<italic>F</i>\u0000 = 8.589, \u0000<italic>p</i>\u0000 = 0.0084 < 0.05) and higher order thinking (\u0000<italic>F</i>\u0000 = 7.217, \u0000<italic>p</i>\u0000 = 0.008 < 0.05). It revealed that generative AI can be effective in supporting teachers’ professional development. This study produced a practical teachers’ professional development method for preservice teachers with generative AI.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1279-1289"},"PeriodicalIF":3.7,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139978999","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}
引用次数: 0
Facilitating the Learning Engineering Process for Educational Conversational Modules Using Transformer-Based Language Models 利用基于转换器的语言模型促进教育对话模块的学习工程过程
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-20 DOI: 10.1109/TLT.2024.3367738
Behzad Mirzababaei;Viktoria Pammer-Schindler
In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure, i.e., the turns the classifiers can choose between. Our primary purpose is to evaluate the effectiveness of conversational modules if a learning engineer follows our workflow. Notably, our workflow is technically lightweight, in the sense that no further training of the models is expected. To evaluate the workflow, we created three different conversational modules. For each, we assessed classifier quality and how coherent the follow-up question asked by the agent was based on the classification results of the user response. The classifiers reached F1-macro scores between 0.66 and 0.86, and the percentage of coherent follow-up questions asked by the agent was between 79% and 84%. These results highlight, first, the potential of transformer-based models to support learning engineers in developing dedicated conversational agents. Second, it highlights the necessity to consider the quality of the adaptation mechanism together with the adaptive dialogue. As such models continue to be improved, their benefits for learning engineering will rise. Future work would be valuable to investigate the usability of this workflow by learning engineers with different backgrounds and prior knowledge on the technical and pedagogical aspects of learning engineering.
在本文中,我们研究了一个系统化的工作流程,该流程可支持学习工程过程,即根据现有的学习材料为会话模块制定起始问题,指定基于转换器的语言模型作为分类器运行所需的输入,以及指定自适应对话结构,即分类器可以选择的转折。我们的主要目的是在学习工程师遵循我们的工作流程的情况下,评估对话模块的有效性。值得注意的是,我们的工作流程在技术上是轻量级的,即不需要对模型进行进一步的训练。为了评估工作流程,我们创建了三个不同的对话模块。对于每个模块,我们都评估了分类器的质量以及代理根据用户回答的分类结果提出的后续问题的连贯性。分类器的 F1-macro 分数介于 0.66 和 0.86 之间,而代理所提后续问题的连贯性比例介于 79% 和 84% 之间。这些结果首先凸显了基于转换器的模型在支持学习工程师开发专用会话代理方面的潜力。其次,它强调了将适应机制的质量与自适应对话一起考虑的必要性。随着这类模型的不断改进,它们对学习工程的益处也会越来越大。未来的工作将是研究具有不同背景的学习工程师在学习工程的技术和教学方面的可用性。
{"title":"Facilitating the Learning Engineering Process for Educational Conversational Modules Using Transformer-Based Language Models","authors":"Behzad Mirzababaei;Viktoria Pammer-Schindler","doi":"10.1109/TLT.2024.3367738","DOIUrl":"10.1109/TLT.2024.3367738","url":null,"abstract":"In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure, i.e., the turns the classifiers can choose between. Our primary purpose is to evaluate the effectiveness of conversational modules if a learning engineer follows our workflow. Notably, our workflow is technically lightweight, in the sense that no further training of the models is expected. To evaluate the workflow, we created three different conversational modules. For each, we assessed classifier quality and how coherent the follow-up question asked by the agent was based on the classification results of the user response. The classifiers reached F1-macro scores between 0.66 and 0.86, and the percentage of coherent follow-up questions asked by the agent was between 79% and 84%. These results highlight, first, the potential of transformer-based models to support learning engineers in developing dedicated conversational agents. Second, it highlights the necessity to consider the quality of the adaptation mechanism together with the adaptive dialogue. As such models continue to be improved, their benefits for learning engineering will rise. Future work would be valuable to investigate the usability of this workflow by learning engineers with different backgrounds and prior knowledge on the technical and pedagogical aspects of learning engineering.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1222-1235"},"PeriodicalIF":3.7,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10440567","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956841","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}
引用次数: 0
Exploring the Possibilities of Edu-Metaverse: A New 3-D Ecosystem Model for Innovative Learning 探索 Edu-Metaverse 的可能性:创新学习的全新 3D 生态系统模型
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-12 DOI: 10.1109/TLT.2024.3364908
Tracy Bobko;Mikiko Corsette;Minjuan Wang;Erin Springer
This article discusses the transformative impact of technology on knowledge acquisition and sharing, focusing on the emergence of the metaverse as a virtual community with vast potential for virtual learning. Learning in the metaverse is found to enhance engagement, motivation, and retention, while fostering 21st-century skills. It also offers personalized and quality education, benefiting students in remote areas. This article explores the Edu-Metaverse ecosystem, which illustrates the interconnectedness of various metaverse components supporting sustainable and equitable learning. The study aims to investigate the alignment of this ecosystem model with teaching and learning activities in exemplary metaverse platforms, its role in fostering inclusive and sustainable learning environments, and how to enhance and rebuild it through 3-D modeling and real metaverse teaching settings experimentation. Throughout this article, the terms “metaverse in education” and “Edu-Metaverse” are used interchangeably. The metaverse is defined as a virtual shared space, ranging from fully virtual worlds, such as virtual reality to partially virtual ones, such as augmented reality. The Edu-Metaverse ecosystem encompasses technologies, platforms, and stakeholders responsible for virtual learning environments. Sustainability, in this context, entails designing systems that withstand environmental, economic, and social pressures while providing equitable and inclusive learning opportunities. Continuous engagement through missions and quests ensures sustainable learning experiences for students. This article highlights the potential of the metaverse to revolutionize education and emphasizes the importance of research before widespread implementation in educational institutions and talent development fields. The Edu-Metaverse ecosystem is presented as a promising framework for advancing virtual learning and fostering inclusive and sustainable education.
这篇文章讨论了技术对知识获取和共享的变革性影响,重点是作为虚拟社区出现的具有巨大虚拟学习潜力的元宇宙。人们发现,在元宇宙中学习能提高参与度、积极性和保持力,同时培养 21 世纪的技能。它还提供个性化的优质教育,使偏远地区的学生受益。本文探讨了 Edu-Metaverse 生态系统,它说明了支持可持续和公平学习的各种元网组件之间的相互联系。本研究旨在探讨该生态系统模型与示范性元数据平台中教学活动的一致性、其在促进包容性和可持续学习环境中的作用,以及如何通过三维建模和实际元数据教学设置实验来增强和重建该模型。在本文中,"教育中的元宇宙 "和 "Edu-Metaverse "这两个术语可以互换使用。元宇宙被定义为虚拟共享空间,既包括完全虚拟的世界,如虚拟现实,也包括部分虚拟的世界,如增强现实。Edu-Metaverse 生态系统包括负责虚拟学习环境的技术、平台和利益相关者。在此背景下,可持续性要求设计的系统既能承受环境、经济和社会压力,又能提供公平、包容的学习机会。通过任务和探索持续参与,可确保学生获得可持续的学习体验。这篇文章强调了元世界给教育带来革命性变化的潜力,并强调了在教育机构和人才培养领域广泛实施之前开展研究的重要性。Edu-Metaverse 生态系统是推进虚拟学习、促进全纳和可持续教育的一个前景广阔的框架。
{"title":"Exploring the Possibilities of Edu-Metaverse: A New 3-D Ecosystem Model for Innovative Learning","authors":"Tracy Bobko;Mikiko Corsette;Minjuan Wang;Erin Springer","doi":"10.1109/TLT.2024.3364908","DOIUrl":"10.1109/TLT.2024.3364908","url":null,"abstract":"This article discusses the transformative impact of technology on knowledge acquisition and sharing, focusing on the emergence of the metaverse as a virtual community with vast potential for virtual learning. Learning in the metaverse is found to enhance engagement, motivation, and retention, while fostering 21st-century skills. It also offers personalized and quality education, benefiting students in remote areas. This article explores the Edu-Metaverse ecosystem, which illustrates the interconnectedness of various metaverse components supporting sustainable and equitable learning. The study aims to investigate the alignment of this ecosystem model with teaching and learning activities in exemplary metaverse platforms, its role in fostering inclusive and sustainable learning environments, and how to enhance and rebuild it through 3-D modeling and real metaverse teaching settings experimentation. Throughout this article, the terms “metaverse in education” and “Edu-Metaverse” are used interchangeably. The metaverse is defined as a virtual shared space, ranging from fully virtual worlds, such as virtual reality to partially virtual ones, such as augmented reality. The Edu-Metaverse ecosystem encompasses technologies, platforms, and stakeholders responsible for virtual learning environments. Sustainability, in this context, entails designing systems that withstand environmental, economic, and social pressures while providing equitable and inclusive learning opportunities. Continuous engagement through missions and quests ensures sustainable learning experiences for students. This article highlights the potential of the metaverse to revolutionize education and emphasizes the importance of research before widespread implementation in educational institutions and talent development fields. The Edu-Metaverse ecosystem is presented as a promising framework for advancing virtual learning and fostering inclusive and sustainable education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1290-1301"},"PeriodicalIF":3.7,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945618","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}
引用次数: 0
Intelligent Retrieval and Comprehension of Entrepreneurship Education Resources Based on Semantic Summarization of Knowledge Graphs 基于知识图谱语义总结的创业教育资源智能检索与理解
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-09 DOI: 10.1109/TLT.2024.3364155
Haiyang Yu;Entai Wang;Qi Lang;Jianan Wang
The latest technologies in natural language processing provide creative, knowledge retrieval, and question-answering technologies in the design of intelligent education, which can provide learners with personalized feedback and expert guidance. Entrepreneurship education aims to cultivate and develop the innovative thinking and entrepreneurial skills of students, making it a practical form of education. However, a knowledge retrieval and question-answering teaching assistant system for entrepreneurship education has not been proposed. This observation motivated us to develop a reading comprehension framework to address the challenges of domain-specific knowledge gaps and the weak comprehension of complex texts encountered by intelligent education models in practical applications. The proposed framework mainly includes: question understanding, relevant knowledge retrieval, mathematical calculation, and answer prediction. The techniques involved in the aforementioned modules mainly include text embedding, similarity retrieval, graph convolution, and long short-term memory network. By integrating this model into entrepreneurship courses, learners can participate in real-time discussions and receive immediate feedback, creating a more dynamic and interactive learning environment. To assess the effectiveness of the proposed model, this article conducts answer prediction on single-choice exercises related to entrepreneurship education courses. This study employs the potential of using a question-and-answer format to enhance intelligent entrepreneurship education, paving the way for a more effective and engaging online learning experience.
自然语言处理的最新技术为智能教育设计提供了创意、知识检索和问题解答技术,可以为学习者提供个性化反馈和专家指导。创业教育旨在培养和发展学生的创新思维和创业能力,是一种实践性很强的教育形式。然而,针对创业教育的知识检索和问题解答辅助教学系统尚未被提出。这一现象促使我们开发了一个阅读理解框架,以解决智能教育模型在实际应用中遇到的特定领域知识空白和复杂文本理解能力弱的难题。所提出的框架主要包括:问题理解、相关知识检索、数学计算和答案预测。上述模块涉及的技术主要包括文本嵌入、相似性检索、图卷积和长短期记忆网络。将这一模型集成到创业课程中,学习者可以参与实时讨论并获得即时反馈,从而创造一个更加动态和互动的学习环境。为了评估所提出模型的有效性,本文对创业教育课程相关的单项选择练习进行了答案预测。这项研究利用问答形式来提高智能创业教育的潜力,为更有效、更吸引人的在线学习体验铺平了道路。
{"title":"Intelligent Retrieval and Comprehension of Entrepreneurship Education Resources Based on Semantic Summarization of Knowledge Graphs","authors":"Haiyang Yu;Entai Wang;Qi Lang;Jianan Wang","doi":"10.1109/TLT.2024.3364155","DOIUrl":"10.1109/TLT.2024.3364155","url":null,"abstract":"The latest technologies in natural language processing provide creative, knowledge retrieval, and question-answering technologies in the design of intelligent education, which can provide learners with personalized feedback and expert guidance. Entrepreneurship education aims to cultivate and develop the innovative thinking and entrepreneurial skills of students, making it a practical form of education. However, a knowledge retrieval and question-answering teaching assistant system for entrepreneurship education has not been proposed. This observation motivated us to develop a reading comprehension framework to address the challenges of domain-specific knowledge gaps and the weak comprehension of complex texts encountered by intelligent education models in practical applications. The proposed framework mainly includes: question understanding, relevant knowledge retrieval, mathematical calculation, and answer prediction. The techniques involved in the aforementioned modules mainly include text embedding, similarity retrieval, graph convolution, and long short-term memory network. By integrating this model into entrepreneurship courses, learners can participate in real-time discussions and receive immediate feedback, creating a more dynamic and interactive learning environment. To assess the effectiveness of the proposed model, this article conducts answer prediction on single-choice exercises related to entrepreneurship education courses. This study employs the potential of using a question-and-answer format to enhance intelligent entrepreneurship education, paving the way for a more effective and engaging online learning experience.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1210-1221"},"PeriodicalIF":3.7,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945748","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}
引用次数: 0
Using a Chatbot to Provide Formative Feedback: A Longitudinal Study of Intrinsic Motivation, Cognitive Load, and Learning Performance 使用聊天机器人提供形成性反馈:关于内在动机、认知负荷和学习成绩的纵向研究
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-08 DOI: 10.1109/TLT.2024.3364015
Jiaqi Yin;Tiong-Thye Goh;Yi Hu
This study aimed to examine sustainable effects of chatbot-based formative feedback on intrinsic motivation, cognitive load, and learning performance. A longitudinal quasi-experimental design with 173 undergraduate students was conducted. The experiment is a between-subject design. Students either received formative feedback from a chatbot or a teacher. Utilizing linear mixed model and t-test for data analysis, results showed the following. First, chatbot-based feedback resulted in increased learning interest, perceived choice, and value while decreasing perceived pressure over time. Second, chatbot-based feedback was effective in reducing cognitive load, particularly when learning contents involved conceptual or difficult knowledge. Finally, chatbot-based feedback was found to be more efficient and effective in supporting the mastery of application-based knowledge compared with teacher-based feedback. This study has practical implications for the design of chatbots, and it also enriches the methods of providing ongoing formative feedback in large-scale classrooms.
本研究旨在考察基于聊天机器人的形成性反馈对内在动机、认知负荷和学习成绩的可持续影响。研究采用纵向准实验设计,共有 173 名本科生参加。实验采用被试间设计。学生可以从聊天机器人或教师那里获得形成性反馈。利用线性混合模型和 t 检验进行数据分析,结果显示如下。首先,随着时间的推移,基于聊天机器人的反馈提高了学习兴趣、感知选择和价值,同时降低了感知压力。其次,基于聊天机器人的反馈能有效减轻认知负荷,尤其是当学习内容涉及概念性或难度较大的知识时。最后,与基于教师的反馈相比,基于聊天机器人的反馈在支持掌握应用型知识方面更加高效和有效。这项研究对聊天机器人的设计具有实际意义,同时也丰富了在大规模课堂上提供持续性形成性反馈的方法。
{"title":"Using a Chatbot to Provide Formative Feedback: A Longitudinal Study of Intrinsic Motivation, Cognitive Load, and Learning Performance","authors":"Jiaqi Yin;Tiong-Thye Goh;Yi Hu","doi":"10.1109/TLT.2024.3364015","DOIUrl":"10.1109/TLT.2024.3364015","url":null,"abstract":"This study aimed to examine sustainable effects of chatbot-based formative feedback on intrinsic motivation, cognitive load, and learning performance. A longitudinal quasi-experimental design with 173 undergraduate students was conducted. The experiment is a between-subject design. Students either received formative feedback from a chatbot or a teacher. Utilizing linear mixed model and t-test for data analysis, results showed the following. First, chatbot-based feedback resulted in increased learning interest, perceived choice, and value while decreasing perceived pressure over time. Second, chatbot-based feedback was effective in reducing cognitive load, particularly when learning contents involved conceptual or difficult knowledge. Finally, chatbot-based feedback was found to be more efficient and effective in supporting the mastery of application-based knowledge compared with teacher-based feedback. This study has practical implications for the design of chatbots, and it also enriches the methods of providing ongoing formative feedback in large-scale classrooms.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1404-1415"},"PeriodicalIF":3.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945547","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}
引用次数: 0
Serious Video Games for Agricultural Learning: Scoping Review 用于农业学习的严肃视频游戏:范围审查
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-08 DOI: 10.1109/TLT.2024.3364086
Ismael E. Espinosa-Curiel;Carlos A. García de Alba-Chávez
Serious video games provide a immersive learning environment for agriculture by simulating real-life challenges scenarios. However, empirical evidence of their effectiveness is sparse. This scoping review follows PRISMA-ScR guidelines to summarize literature on serious video games for agricultural learning, highlighting research trends and identifying gaps. We systematically searched nine prominent research databases for papers on serious video games for agriculture learning published between January 2000 and July 2022. Two independent reviewers conducted screening, data extraction, and synthesized the collected data using a narrative approach. The initial search identified 3,297 articles, of which 0.58% (n = 19) were included in the review. Most reviewed games were released in the last five years, with a predominant presence in the mobile platform. They commonly employed a simulation-based approach, featuring 2-D graphics and designed for single-player experiences. These games mainly target students, focusing on crop production and sustainable agriculture. Educational theories were often unspecified in the studies. Evaluation protocols primarily consisted of pilot studies, emphasizing user experience and knowledge enhancement. Positive outcomes, such as improved user experiences, knowledge, and attitude and behavior changes, were commonly observed in these studies. This study highlights advancements in using serious video games for agricultural learning over 20 years. However, it stresses the need for deeper exploration of game elements' impact on user experience and effectiveness. Creating games for underrepresented players and specific agricultural challenges is essential, as is enhancing theoretical foundations and learning approaches. Rigorous research designs are vital for assessing game effectiveness across short, medium, and long terms.
严肃视频游戏通过模拟现实生活中的挑战场景,为农业提供了一个身临其境的学习环境。然而,有关其有效性的实证证据却很少。本范围界定综述遵循 PRISMA-ScR 指南,总结了有关用于农业学习的严肃视频游戏的文献,强调了研究趋势并找出了差距。我们在九个著名的研究数据库中系统检索了 2000 年 1 月至 2022 年 7 月间发表的有关农业学习的严肃视频游戏的论文。两位独立审稿人进行了筛选和数据提取,并采用叙述的方法对收集到的数据进行了综合。初步检索发现了 3297 篇文章,其中 0.58%(n = 19)被纳入综述。大多数被收录的游戏都是在过去五年中发布的,主要集中在移动平台上。这些游戏通常采用基于模拟的方法,以 2-D 图形为特色,专为单人游戏体验而设计。这些游戏的主要目标受众是学生,侧重于作物生产和可持续农业。研究中往往没有具体说明教育理论。评估方案主要包括试点研究,强调用户体验和知识提升。这些研究普遍观察到了积极的成果,如用户体验的改善、知识的提高、态度和行为的改变。本研究强调了 20 年来利用严肃视频游戏进行农业学习所取得的进展。不过,它强调了深入探讨游戏元素对用户体验和效果的影响的必要性。为代表性不足的玩家和特定的农业挑战制作游戏至关重要,加强理论基础和学习方法也同样重要。严格的研究设计对于评估游戏在短期、中期和长期的有效性至关重要。
{"title":"Serious Video Games for Agricultural Learning: Scoping Review","authors":"Ismael E. Espinosa-Curiel;Carlos A. García de Alba-Chávez","doi":"10.1109/TLT.2024.3364086","DOIUrl":"10.1109/TLT.2024.3364086","url":null,"abstract":"Serious video games provide a immersive learning environment for agriculture by simulating real-life challenges scenarios. However, empirical evidence of their effectiveness is sparse. This scoping review follows PRISMA-ScR guidelines to summarize literature on serious video games for agricultural learning, highlighting research trends and identifying gaps. We systematically searched nine prominent research databases for papers on serious video games for agriculture learning published between January 2000 and July 2022. Two independent reviewers conducted screening, data extraction, and synthesized the collected data using a narrative approach. The initial search identified 3,297 articles, of which 0.58% (\u0000<italic>n</i>\u0000 = 19) were included in the review. Most reviewed games were released in the last five years, with a predominant presence in the mobile platform. They commonly employed a simulation-based approach, featuring 2-D graphics and designed for single-player experiences. These games mainly target students, focusing on crop production and sustainable agriculture. Educational theories were often unspecified in the studies. Evaluation protocols primarily consisted of pilot studies, emphasizing user experience and knowledge enhancement. Positive outcomes, such as improved user experiences, knowledge, and attitude and behavior changes, were commonly observed in these studies. This study highlights advancements in using serious video games for agricultural learning over 20 years. However, it stresses the need for deeper exploration of game elements' impact on user experience and effectiveness. Creating games for underrepresented players and specific agricultural challenges is essential, as is enhancing theoretical foundations and learning approaches. Rigorous research designs are vital for assessing game effectiveness across short, medium, and long terms.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1155-1169"},"PeriodicalIF":3.7,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139956845","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}
引用次数: 0
Blended Laboratory Design Using Raspberry Pi Pico for Digital Circuits and Systems 使用 Raspberry Pi Pico 进行数字电路与系统的混合实验室设计
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-02-07 DOI: 10.1109/TLT.2024.3363230
Zoe C. M. Davidson;Shuping Dang;Xenofon Vasilakos
Raspberry Pi Pico, based on chip RP2040, is an easy-to-use development microcontroller board that can provide flexible input/output functions and meets the teaching needs of basic electronics to first-year university undergraduates. This article presents our blended laboratory design using Raspberry Pi Pico for the course unit Digital Circuits and Systems. Considering the impacts of Coronavirus Disease 2019 (COVID-19) and the reduced number of students attending the in-person laboratory, we provide an alternative approach using an online Raspberry Pi Pico simulator produced by Wokwi for those students who cannot attend the physical laboratory. The entire laboratory is designed by design-based learning pedagogical methodology and consists of three dependent sessions. Throughout the three laboratory sessions, first-year undergraduates are expected to understand the basic digital logic and electronic circuits by building a simplified interactive traffic light controller system using Raspberry Pi Pico and Python programming. The intended learning outcomes, full details of the blended laboratory design, and the laboratory design evaluation results are given and discussed in this article to verify the effectiveness of the blended laboratory design using Raspberry Pi Pico. By analyzing the empirical data collected from laboratory participants, the effectiveness of the proposed blended laboratory design can be well supported, and all intended learning outcomes are successfully achieved subject to the impacts of COVID-19.
基于芯片 RP2040 的 Raspberry Pi Pico 是一种易于使用的开发型微控制器板,可提供灵活的输入/输出功能,满足大学一年级本科生基础电子学的教学需求。本文介绍了我们在《数字电路与系统》课程单元中使用 Raspberry Pi Pico 进行的混合实验室设计。考虑到 2019 年冠状病毒病(COVID-19)的影响以及参加现场实验室的学生人数减少,我们提供了一种替代方法,即使用 Wokwi 制作的在线 Raspberry Pi Pico 模拟器,为无法参加实体实验室的学生提供服务。整个实验室采用基于设计的学习教学方法,由三个依存环节组成。在这三个实验环节中,一年级本科生将通过使用 Raspberry Pi Pico 和 Python 编程建立一个简化的交互式交通灯控制器系统,了解基本的数字逻辑和电子电路。本文给出并讨论了混合实验室设计的预期学习成果、全部细节以及实验室设计评估结果,以验证使用 Raspberry Pi Pico 的混合实验室设计的有效性。通过分析从实验室参与者那里收集到的实证数据,可以很好地支持所建议的混合实验室设计的有效性,并且在 COVID-19 的影响下成功实现了所有预期的学习成果。
{"title":"Blended Laboratory Design Using Raspberry Pi Pico for Digital Circuits and Systems","authors":"Zoe C. M. Davidson;Shuping Dang;Xenofon Vasilakos","doi":"10.1109/TLT.2024.3363230","DOIUrl":"10.1109/TLT.2024.3363230","url":null,"abstract":"Raspberry Pi Pico, based on chip RP2040, is an easy-to-use development microcontroller board that can provide flexible input/output functions and meets the teaching needs of basic electronics to first-year university undergraduates. This article presents our blended laboratory design using Raspberry Pi Pico for the course unit Digital Circuits and Systems. Considering the impacts of Coronavirus Disease 2019 (COVID-19) and the reduced number of students attending the in-person laboratory, we provide an alternative approach using an online Raspberry Pi Pico simulator produced by Wokwi for those students who cannot attend the physical laboratory. The entire laboratory is designed by design-based learning pedagogical methodology and consists of three dependent sessions. Throughout the three laboratory sessions, first-year undergraduates are expected to understand the basic digital logic and electronic circuits by building a simplified interactive traffic light controller system using Raspberry Pi Pico and Python programming. The intended learning outcomes, full details of the blended laboratory design, and the laboratory design evaluation results are given and discussed in this article to verify the effectiveness of the blended laboratory design using Raspberry Pi Pico. By analyzing the empirical data collected from laboratory participants, the effectiveness of the proposed blended laboratory design can be well supported, and all intended learning outcomes are successfully achieved subject to the impacts of COVID-19.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1170-1183"},"PeriodicalIF":3.7,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945555","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}
引用次数: 0
How Can Self-Evaluation and Self-Efficacy Skills of Young Learners be Scaffolded in a Web Application? 如何在网络应用中培养青少年的自我评价和自我效能感技能?
IF 3.7 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2024-01-30 DOI: 10.1109/TLT.2024.3360121
Thomas Sergent;Morgane Daniel;François Bouchet;Thibault Carron
Self-regulated learning (SRL) skills are critical for students of all ages to maximize their learning. Two key processes of SRL are being aware of one's performance (self-evaluation) and believing in one's capabilities to produce given attainments (self-efficacy). To assess and improve these capabilities in young children (5–8), we use a literacy web application, where we introduced two randomly triggered prompts to evaluate perceived difficulty and desired difficulty. Comparing students' actual performance with their responses to self-regulatory prompts provides information about their ability to self-regulate their learning, in particular their self-evaluation and self-efficacy. The novelty of this work resides in studying the SRL of young children (5–8) in digital learning environments while learning another task (reading in our case), measuring and improving some SRL abilities themselves and not only measuring and improving academic results in other tasks, and the large number of students on which the studies were carried (over 400 000). Using 15 982 994 responses from 467 116 students, we first measured two types of SRL deficits, and then, we assessed how a scaffolding and remediation strategy can reduce these deficits. In Study 1, we compare a group receiving remediation feedback to a control group, whereas in Study 2, we determine the impact of age and level on the remediation efficiency. Our contribution is twofold: a method to address on the long term a deficit in self-evaluation or in self-efficacy in a digital learning environment, and a corroboration of the fact that students who are academically at risk lack self-efficacy and avoid tackling challenging exercises compared with their level. We, therefore, recommend that digital learning environments integrate an overlay of SRL, such as self-evaluation and self-efficacy remediation loops, especially for younger students and students who are struggling academically. We included notes for educational practitioners in this article for this purpose.
自我调节学习(SRL)技能对于各年龄段的学生最大限度地提高学习成绩至关重要。自我调节学习的两个关键过程是意识到自己的表现(自我评价)和相信自己有能力取得既定成绩(自我效能感)。为了评估和提高幼儿(5-8 岁)的这些能力,我们使用了一个识字网络应用程序,其中引入了两个随机触发的提示,以评估感知难度和期望难度。比较学生的实际表现和他们对自我调节提示的反应,可以了解他们自我调节学习的能力,特别是他们的自我评价和自我效能感。这项工作的新颖之处在于:在数字学习环境中研究幼儿(5-8 岁)的自律学习能力,同时学习另一项任务(在我们的案例中是阅读);测量和提高自律学习能力本身的某些能力,而不仅仅是测量和提高其他任务的学习成绩;研究的学生人数众多(超过 400 000 人)。利用来自 467 116 名学生的 15 982 994 份答卷,我们首先测量了两种类型的自学能力缺陷,然后评估了支架和补救策略如何能够减少这些缺陷。在研究 1 中,我们将接受补救反馈的小组与对照组进行了比较,而在研究 2 中,我们确定了年龄和水平对补救效率的影响。我们的贡献有两个方面:一是提供了一种在数字化学习环境中长期解决自我评价或自我效能感不足问题的方法;二是证实了学业有风险的学生缺乏自我效能感,并避免处理与其水平相比具有挑战性的练习这一事实。因此,我们建议在数字化学习环境中加入自我学习方法,如自我评价和自我效能感补救循环,尤其是针对低年级学生和学业有困难的学生。为此,我们在本文中为教育从业者提供了注意事项。
{"title":"How Can Self-Evaluation and Self-Efficacy Skills of Young Learners be Scaffolded in a Web Application?","authors":"Thomas Sergent;Morgane Daniel;François Bouchet;Thibault Carron","doi":"10.1109/TLT.2024.3360121","DOIUrl":"10.1109/TLT.2024.3360121","url":null,"abstract":"Self-regulated learning (SRL) skills are critical for students of all ages to maximize their learning. Two key processes of SRL are being aware of one's performance (self-evaluation) and believing in one's capabilities to produce given attainments (self-efficacy). To assess and improve these capabilities in young children (5–8), we use a literacy web application, where we introduced two randomly triggered prompts to evaluate perceived difficulty and desired difficulty. Comparing students' actual performance with their responses to self-regulatory prompts provides information about their ability to self-regulate their learning, in particular their self-evaluation and self-efficacy. The novelty of this work resides in studying the SRL of young children (5–8) in digital learning environments while learning another task (reading in our case), measuring and improving some SRL abilities themselves and not only measuring and improving academic results in other tasks, and the large number of students on which the studies were carried (over 400 000). Using 15 982 994 responses from 467 116 students, we first measured two types of SRL deficits, and then, we assessed how a scaffolding and remediation strategy can reduce these deficits. In Study 1, we compare a group receiving remediation feedback to a control group, whereas in Study 2, we determine the impact of age and level on the remediation efficiency. Our contribution is twofold: a method to address on the long term a deficit in self-evaluation or in self-efficacy in a digital learning environment, and a corroboration of the fact that students who are academically at risk lack self-efficacy and avoid tackling challenging exercises compared with their level. We, therefore, recommend that digital learning environments integrate an overlay of SRL, such as self-evaluation and self-efficacy remediation loops, especially for younger students and students who are struggling academically. We included notes for educational practitioners in this article for this purpose.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1184-1197"},"PeriodicalIF":3.7,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945561","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}
引用次数: 0
期刊
IEEE Transactions on Learning Technologies
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1