IEEE Transactions on Learning Technologies最新文献_第4页

Embedding Educational Narrative Scripts in a Social Media Environment 在社交媒体环境中嵌入教育叙事脚本

IF 2.9 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-06-04 DOI: 10.1109/TLT.2024.3409063

Emily Theophilou;René Lobo-Quintero;Davinia Hernández-Leo;Roberto Sánchez-Reina;Dimitri Ognibene

{"title":"Embedding Educational Narrative Scripts in a Social Media Environment","authors":"Emily Theophilou;René Lobo-Quintero;Davinia Hernández-Leo;Roberto Sánchez-Reina;Dimitri Ognibene","doi":"10.1109/TLT.2024.3409063","DOIUrl":"https://doi.org/10.1109/TLT.2024.3409063","url":null,"abstract":"The impact of social media on teens’ mental health and development raises the need for educational interventions that equip them with the knowledge and skills to cope with dangerous situations. In spite of the growing effort to expand social media literacy among youngsters, social media interventions still rely on conventional methods that tend to prioritize cognitive skills while overlooking important socio-emotional competencies. To bridge this gap and offer innovative solutions to social media education, this article presents the narrative scripts (NS) approach implemented in a learning technology environment that integrates pedagogical strategies of authentic learning, narratives, and scripted collaborative learning within a simulated educational social media platform. A longitudinal study with 370 high school students in urban schools in Barcelona (Spain) was designed to assess NS in an intervention to foster the development of social media self-protection skills. The findings demonstrated that NS supported the development of social media self-protection skills, while the students expressed positive perceptions of their overall learning experience. The intervention notably enhanced the socio-emotional competencies of responsible decision-making, self-awareness, and social awareness. This research makes a valuable contribution to the design and development of technology aimed at facilitating authentic learning experiences for social media education, with a specific focus on interventions targeting socio-emotional competencies.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1820-1833"},"PeriodicalIF":2.9,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547444","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141494902","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

Leveraging Multi-Criteria Integer Programming Optimization for Effective Team Formation 利用多标准整数编程优化有效组建团队

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-05-16 DOI: 10.1109/tlt.2024.3401734

Pallavi Singh, Phat K. Huynh, Dang Nguyen, Trung Q. Le, Wilfrido Moreno

引用次数: 0

Can Autograding of Student-Generated Questions Quality by ChatGPT Match Human Experts? ChatGPT 的学生生成问题质量自动交易能否匹配人工专家？

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-30 DOI: 10.1109/TLT.2024.3394807

Kangkang Li;Qian Yang;Xianmin Yang

{"title":"Can Autograding of Student-Generated Questions Quality by ChatGPT Match Human Experts?","authors":"Kangkang Li;Qian Yang;Xianmin Yang","doi":"10.1109/TLT.2024.3394807","DOIUrl":"10.1109/TLT.2024.3394807","url":null,"abstract":"The student-generated question (SGQ) strategy is an effective instructional strategy for developing students' higher order cognitive and critical thinking. However, assessing the quality of SGQs is time consuming and domain experts intensive. Previous automatic evaluation work focused on surface-level features of questions. To overcome this limitation, the state-of-the-art language models GPT-3.5 and GPT-4.0 were used to evaluate 1084 SGQs for topic relevance, clarity of expression, answerability, challenging, and cognitive level. Results showed that GPT-4.0 exhibits superior grading consistency with experts compared to GPT-3.5 in terms of topic relevance, clarity of expression, answerability, and difficulty level. GPT-3.5 and GPT-4.0 had low consistency with experts in terms of cognitive level. Over three rounds of testing, GPT-4.0 demonstrated higher stability in autograding when contrasted with GPT-3.5. In addition, to validate the effectiveness of GPT in evaluating SGQs from different domains and subjects, we have done the same experiment on a part of LearningQ dataset. We also discussed the attitudes of teachers and students toward automatic grading by GPT models. The findings underscore the potential of GPT-4.0 to assist teachers in evaluating the quality of SGQs. Nevertheless, the cognitive level assessment of SGQs still needs manual examination by teachers.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1600-1610"},"PeriodicalIF":3.7,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826861","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

Preserving Both Privacy and Utility in Learning Analytics 在学习分析中同时保护隐私和实用性

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-25 DOI: 10.1109/TLT.2024.3393766

Chen Zhan;Srećko Joksimović;Djazia Ladjal;Thierry Rakotoarivelo;Ruth Marshall;Abelardo Pardo

{"title":"Preserving Both Privacy and Utility in Learning Analytics","authors":"Chen Zhan;Srećko Joksimović;Djazia Ladjal;Thierry Rakotoarivelo;Ruth Marshall;Abelardo Pardo","doi":"10.1109/TLT.2024.3393766","DOIUrl":"10.1109/TLT.2024.3393766","url":null,"abstract":"Data are fundamental to Learning Analytics (LA) research and practice. However, the ethical use of data, particularly in terms of respecting learners' privacy rights, is a potential barrier that could hinder the widespread adoption of LA in the education industry. Despite the policies and guidelines of privacy protection being available worldwide, this does not guarantee successful implementation in practice. It is necessary to develop practical approaches that would allow for the translation of the existing guidelines into practice. In this study, we examine an initial set of privacy-preserving mechanisms on a large-scale education dataset. The data utility is evaluated before and after privacy-preserving mechanisms are applied by fitting into commonly used LA models, providing an evaluation of the utility loss. We further explore the balance between preserving data privacy and maintaining data utility in LA. The results prove the compatibility between preserving learners' privacy and LA, providing a benchmark of utility loss to practitioners and researchers in the education sector. Our study reminds an imminent concern of data privacy and advocates that privacy preserving can and should be an integral part of the design of any LA technique.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1655-1667"},"PeriodicalIF":3.7,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799519","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

Scaffolding Computational Thinking With ChatGPT 用 ChatGPT 搭建计算思维的支架

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-24 DOI: 10.1109/TLT.2024.3392896

Jian Liao;Linrong Zhong;Longting Zhe;Handan Xu;Ming Liu;Tao Xie

{"title":"Scaffolding Computational Thinking With ChatGPT","authors":"Jian Liao;Linrong Zhong;Longting Zhe;Handan Xu;Ming Liu;Tao Xie","doi":"10.1109/TLT.2024.3392896","DOIUrl":"10.1109/TLT.2024.3392896","url":null,"abstract":"ChatGPT has received considerable attention in education, particularly in programming education because of its capabilities in automated code generation and program repairing and scoring. However, few empirical studies have investigated the use of ChatGPT to customize a learning system for scaffolding students’ computational thinking. Therefore, this article proposes an intelligent programming scaffolding system using ChatGPT following the theoretical framework of computational thinking and scaffolding. A mixed-method study was conducted to investigate the affordance of the scaffolding system using ChatGPT, and the findings show that most students had positive attitudes about the proposed system, and it was effective in improving their computational thinking generally but not their problem-solving skills. Therefore, more scaffolding strategies are discussed with the aim of improving student computational thinking, especially regarding problem-solving skills. The findings of this study are expected to guide future designs of generative artificial intelligence tools embedded in intelligent learning systems to foster students’ computational thinking and programming learning.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1668-1682"},"PeriodicalIF":3.7,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10508087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799691","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

A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development Through Domain Knowledge Learning in K–12 Settings 以人为本的学习和教学框架，利用生成式人工智能在 K-12 教育环境中通过领域知识学习促进自我调节的学习发展

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-23 DOI: 10.1109/TLT.2024.3392830

Siu-Cheung Kong;Yin Yang

{"title":"A Human-Centered Learning and Teaching Framework Using Generative Artificial Intelligence for Self-Regulated Learning Development Through Domain Knowledge Learning in K–12 Settings","authors":"Siu-Cheung Kong;Yin Yang","doi":"10.1109/TLT.2024.3392830","DOIUrl":"10.1109/TLT.2024.3392830","url":null,"abstract":"The advent of generative artificial intelligence (AI) has ignited an increase in discussions about generative AI tools in education. In this study, a human-centered learning and teaching framework that uses generative AI tools for self-regulated learning development through domain knowledge learning was proposed to catalyze changes in educational practices. The framework illustrates how generative AI tools can revolutionize educational practices and transform the processes of teaching and learning to become human-centered. It emphasizes the evolving roles of teachers, who increasingly become skillful facilitators and humanistic storytellers who craft differentiated instructions and attempt to develop students’ individualized learning. Drawing upon insights from neuroscience, the framework guides students to employ generative AI tools to augment their attentiveness, stimulate active engagement in learning, receive immediate feedback, and encourage self-reflection. The pedagogical approach is also reimagined; teachers equipped with generative AI tools and AI literacy can refine their teaching strategies to better equip students to meet future challenges. The practical application of the framework is demonstrated in a case study involving the development of Chinese language writing ability among primary students within a K–12 educational context. This article also reports the results of a 60-h development programme for teachers. Specifically, providing in-service teachers with cases involving uses of the proposed framework helped them to better understand the generative AI concepts and integrate them into their teaching and learning and increased their perceived ability to design AI-integrated courses that would enhance students’ attention, engagement, confidence, and satisfaction.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1588-1599"},"PeriodicalIF":3.7,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140799518","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

A Tutorial-Generating Method for Autonomous Online Learning 自主在线学习的教程生成方法

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-17 DOI: 10.1109/TLT.2024.3390593

Xiang Wu;Huanhuan Wang;Yongting Zhang;Baowen Zou;Huaqing Hong

{"title":"A Tutorial-Generating Method for Autonomous Online Learning","authors":"Xiang Wu;Huanhuan Wang;Yongting Zhang;Baowen Zou;Huaqing Hong","doi":"10.1109/TLT.2024.3390593","DOIUrl":"10.1109/TLT.2024.3390593","url":null,"abstract":"Generative artificial intelligence has become the focus of the intelligent education field, especially in the generation of personalized learning resources. Current learning resource generation methods recommend customized courses based on learning styles and interests, improving learning efficiency. However, these methods cannot generate personalized tutorials according to learners’ preferences, nor can they adjust tutorial content as moods or levels of knowledge change. Therefore, this study develops an intelligent tutorial-generating system (Self-GT) for self-aid learning, integrating cognitive computing and generative learning to capture learners’ dynamic preferences. The critical components of Self-GT are the tutorial-generating model based on cyclic deep reinforcement learning (RL) and the multimodal knowledge graph containing complex relationships. Specifically, the proposed RL model dynamically explores learners’ preferences from the temporal dimension, enabling RL agents to express learning behavior characteristics accurately and generate personalized tutorials. Then, relying on the internal self-developed education base and external Internet sources, a multimodal knowledge graph with multiple self-defined relationships is designed to enhance the precision of tutorial generation. Finally, the experimental results indicate that the Self-GT performs well in generating tutorials and has been successfully applied in the generating tutorial for “Hospital Network Architecture Planning and Design.”","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1558-1567"},"PeriodicalIF":3.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140614469","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

Reconceptualizing Self-Directed Learning in the Era of Generative AI: An Exploratory Analysis of Language Learning 在生成式人工智能时代重新认识自主学习：对语言学习的探索性分析

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-10 DOI: 10.1109/TLT.2024.3386098

Belle Li;Curtis J. Bonk;Chaoran Wang;Xiaojing Kou

{"title":"Reconceptualizing Self-Directed Learning in the Era of Generative AI: An Exploratory Analysis of Language Learning","authors":"Belle Li;Curtis J. Bonk;Chaoran Wang;Xiaojing Kou","doi":"10.1109/TLT.2024.3386098","DOIUrl":"10.1109/TLT.2024.3386098","url":null,"abstract":"This exploratory analysis investigates the integration of ChatGPT in self-directed learning (SDL). Specifically, this study examines YouTube content creators’ language-learning experiences and the role of ChatGPT in their SDL, building upon Song and Hill's conceptual model of SDL in online contexts. Thematic analysis of interviews with 19 YouTubers and relevant video contents reveals distinct constructs of ChatGPT-integrated SDL, suggesting a reconceptualization and refinement of the SDL framework in the consideration of generative artificial intelligence (AI). This framework emphasizes critical aspects of utilizing ChatGPT as an SDL tool on two distinct levels: 1) the interactive relationships and interplay between learners’ personal traits and their ongoing learning processes (local) and 2) the evolving nature of SDL in the rapidly advancing landscape of generative AI, with socio-political-cultural foundations of AI constantly shaping the learning environment where SDL occurs (global). The study highlights the potential of ChatGPT as a tool for promoting self-directed language learning (SDLL) and provides implications for the development of learning technologies and research on AI-facilitated SDL.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1515-1529"},"PeriodicalIF":3.7,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570306","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

New Scenarios and Trends in Nontraditional Laboratories From 2000 to 2020 2000 至 2020 年非传统实验室的新设想和新趋势

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-10 DOI: 10.1109/TLT.2024.3387280

Ricardo Martin Fernandez;Felix Garcia-Loro;Gustavo R. Alves;Africa López-Rey;Russ Meier;Manuel Castro

引用次数: 0

CoSKT: A Collaborative Self-Supervised Learning Method for Knowledge Tracing CoSKT：用于知识追踪的协作式自我监督学习方法

IF 3.7 3区教育学

IEEE Transactions on Learning Technologies Pub Date : 2024-04-09 DOI: 10.1109/TLT.2024.3386750

Chunyun Zhang;Hebo Ma;Chaoran Cui;Yumo Yao;Weiran Xu;Yunfeng Zhang;Yuling Ma

{"title":"CoSKT: A Collaborative Self-Supervised Learning Method for Knowledge Tracing","authors":"Chunyun Zhang;Hebo Ma;Chaoran Cui;Yumo Yao;Weiran Xu;Yunfeng Zhang;Yuling Ma","doi":"10.1109/TLT.2024.3386750","DOIUrl":"10.1109/TLT.2024.3386750","url":null,"abstract":"Knowledge tracing (KT) aims to trace students' evolving knowledge states based on their learning sequences. Recently, some deep learning based models have been proposed to incorporate the historical information of individuals to trace students' knowledge states and achieve encouraging progress. However, these works ignore the collaborative information among those students who have similar exercise–answering experiences, which may contain some valuable information. In this article, we present a novel collaborative self-supervised learning method for KT (CoSKT), which exploits both similar students' collaborative information and individual information to improve KT. We first use the overlap rate of students' learning experiences to retrieve similar students. Based on similar students' exercise–answering sequences, we leverage attention mechanism to learn the representation of their common knowledge state and expected response to the target exercise. Then, we introduce self-supervised learning by encouraging the consistency between the common knowledge state and individual knowledge state. Finally, we integrate collaborative information and individual knowledge state with a gate mechanism to conduct the response prediction of the target exercise. We compare CoSKT with nine existing KT methods on three public datasets, and the results show that CoSKT achieves the state-of-the-art performance.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1502-1514"},"PeriodicalIF":3.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570592","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