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From Discovery to Design and Implementation: A Guide on Integrating Immersive Technologies in Public Safety Training
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-04-21 DOI: 10.1109/TLT.2025.3555649
Ronak R. Mohanty;Peter Selly;Lindsey Brenner;Shantanu Vyas;Cassidy R. Nelson;Jason B. Moats;Joseph L. Gabbard;Ranjana K. Mehta
Immersive extended reality (XR) technologies, including augmented reality (AR), virtual reality, and mixed reality, are transforming the landscape of education and training through experiences that promote skill acquisition and enhance memory retention. These technologies have notably improved decision making and situational awareness in public safety training. Despite the promise of these advancements, XR adoption for emergency response has been slow. This hesitancy can be partially attributed to a lack of guidance for integrating these novel technologies into existing curricula. This work aims to guide instructional designers, curriculum developers, and technologists in seamlessly integrating immersive technologies into public safety training curricula. This work provides a comprehensive account of our collaboration with instructional designers, public safety personnel, and subject matter experts in developing an AR-based training curriculum for the Sort, Assess, Life-saving Interventions, Treatment/Transport triage technique used in mass casualty incidents (MCIs). In addition, we introduce a systematic framework for public safety curriculum development based on the Analyze, Design, Develop, Implement, Evaluate instructional design model. Leveraging a human-centered design approach, we first analyze the necessity for immersive learning in public safety. Next, we identify the obstacles in developing XR training experiences and outline our construct of a training prototype through iterative evaluations based on stakeholder feedback. Finally, we share qualitative insights through iterative evaluations with firefighters and emergency medical technicians performing MCI triage tasks in AR, supplemented by survey questionnaires and semistructured interviews. Our goal is to provide a blueprint for a successful integration of immersive technologies into public safety training curricula.
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引用次数: 0
Academic Performance Prediction Using Machine Learning Approaches: A Survey
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-24 DOI: 10.1109/TLT.2025.3554174
Jialun Pan;Zhanzhan Zhao;Dongkun Han
Properly predicting students'academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to predict students' academic performance has proven to be less accurate and efficient than desired. Consequently, the past decade has witnessed a marked surge in employing machine learning and data mining techniques to forecast students' performance. However, the academic community has yet to agree on the most effective algorithm for predicting academic outcomes. Nonetheless, conducting an analysis and comparison of the existing algorithms in this field remains meaningful. Furthermore, recommendations for selecting an appropriate algorithm will be provided to interested researchers and educators based on their specific requirements. This article reviews the state-of-the-art literature on academic performance predictions using machine learning approaches in recent years. It details the variables analyzed, the algorithms implemented, the datasets utilized, and the evaluation metrics applied to assess model efficacy. What makes this work different is that relevant surveys in the past 10 years are also analyzed and compared, highlighting their contributions and review methods. In addition, we compared the accuracy of various machine learning models using popular open-access datasets and determined the best-performing algorithms among them. Our dataset and source codes are released for future algorithm comparisons and evaluations in this community.
正确预测学生的学习成绩对于提高各学科的教育成果至关重要。通过精确的成绩预测,学校可以迅速找到面临挑战的学生,并提供适合其特定学习需求的定制教材。事实证明,依靠教师的经验来预测学生学业成绩的准确性和效率都不如人意。因此,在过去的十年中,采用机器学习和数据挖掘技术预测学生成绩的现象明显增多。然而,学术界尚未就预测学习成绩的最有效算法达成一致。尽管如此,对这一领域的现有算法进行分析和比较仍然很有意义。此外,还将根据感兴趣的研究人员和教育工作者的具体要求,为他们提供选择合适算法的建议。本文回顾了近年来使用机器学习方法预测学习成绩的最新文献。文章详细介绍了分析的变量、实施的算法、使用的数据集以及用于评估模型有效性的评价指标。与众不同的是,本文还对过去 10 年的相关调查进行了分析和比较,重点介绍了它们的贡献和审查方法。此外,我们还使用流行的开放存取数据集比较了各种机器学习模型的准确性,并确定了其中表现最佳的算法。我们发布的数据集和源代码可供该社区今后进行算法比较和评估。
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引用次数: 0
Motivating Students With Different Needs to Learn Chinese in a Mixed-Background Classroom by Robot-Assisted Learning
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-14 DOI: 10.1109/TLT.2025.3551256
Ka-Yan Fung;Kwong-Chiu Fung;Tze Leung Rick Lui;Kuen-Fung Sin;Lik-Hang Lee;Huamin Qu;Shenghui Song
Mastering the visually complex characters of the Chinese language poses significant challenges for students. The situation is even worse in Hong Kong, where students with different backgrounds, including students with/without dyslexia and non-Chinese speaking (NCS) students, are placed in the same class. Interactive design has been proven effective in enhancing students' learning performance and engagement. However, developing a learning tool for students with diverse backgrounds is challenging. This study proposes a robot-assisted Chinese learning system (RACLS) for those with diverse backgrounds and investigates its impact on learning motivation by a comparison study. In particular, 39 students participate in a five-day robot-led training program, while another 39 students received traditional teacher-led training. The comparison results show that RACLS can enhance the emotional engagement of students with dyslexia and strengthen the behavioral engagement of students without dyslexia. Interestingly, the learning motivation of NCS students in the experimental and control groups is enhanced similarly.
掌握视觉复杂的汉字对学生来说是一项巨大的挑战。在香港,不同背景的学生,包括有/没有读写困难的学生和非华语学生,被安排在同一个班级,情况更为严重。事实证明,互动设计能有效提高学生的学习成绩和参与度。然而,为背景各异的学生开发学习工具却极具挑战性。本研究针对不同背景的学生提出了机器人辅助中文学习系统(RACLS),并通过对比研究探讨了该系统对学习动机的影响。具体而言,39 名学生参加了为期五天的机器人指导培训项目,而另外 39 名学生则接受了传统的教师指导培训。对比结果表明,RACLS 可以提高有阅读障碍学生的情感参与度,加强无阅读障碍学生的行为参与度。有趣的是,实验组和对照组非华语学生的学习动机也得到了类似的提升。
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引用次数: 0
Exploring the Impact of the Metaverse on Promoting Students’ Access to Quality Education: A Meta-Analysis
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-11 DOI: 10.1109/TLT.2025.3550714
Yuanbin Diao;Yu-Sheng Su
With technological advancements, the Metaverse is being used to enhance learning effects and learning experience to ensure quality education. However, current empirical studies have produced varying results. Therefore, a meta-analysis was executed, leveraging the capabilities of Version 3 of the Comprehensive Meta-Analysis software to effectively synthesize the data, drawing insights from 34 studies published prior to October 2024. The goal was to analyze the effects of the Metaverse on quality education, and to investigate the moderating influences of four variables: Metaverse tools, educational stages, subject area, and treatment duration. The results showed that the overall effect sizes for learning effects and learning experience were 0.922 and 1.153, respectively, suggesting that the Metaverse substantially influences educational effects and learning experience. The four moderating variables all play a significant role in shaping the influence of the Metaverse on both learning effects and experience. This meta-analysis highlights a striking trend: the Metaverse's effects were especially pronounced for elementary and secondary school students, but less so for university students. In addition, the Metaverse's effects were most significant in science disciplines.
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引用次数: 0
Reducing English Major Students’ Writing Errors With an Automated Writing Evaluation System: Evidence From Eye-Tracking Technology
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-03-05 DOI: 10.1109/TLT.2025.3547321
Bei Cai;Ziyu He;Hong Fu;Yang Zheng;Yanjie Song
Much research has applied automated writing evaluation (AWE) systems to English writing instruction; however, understanding how students internalize and apply this feedback to reduce writing errors is difficult, largely due to the personal and private nature of this process. Therefore, this research utilized eye-tracking technology to explore the AWE system's effectiveness in reducing the writing errors of English major students. A total of 118 higher vocational college students majoring in English in China participated in this eight-week study. The experimental group studied with and received feedback from both the AWE system (Pigai) and the teacher, whereas the control group studied without the AWE system and only received teacher feedback. Eye-tracking experiments were conducted before and after the writing instruction. Participants’ responses during the eye-tracking experiment, first-person eye movement video data, and corresponding gaze data were collected. Leveraging the application of neural network technology in optical character recognition (OCR), combined with data from an eye-tracking device, we developed a system that can transform first-person eye movement video data and gaze data into heatmaps and eye-tracking indices conducive to analysis. Various data analysis methods were employed, including neural network algorithms, heatmap analysis, Mann–Whitney U test, independent-samples t-test, and Welch's t-test. The results for the post-eye-tracking experiment responses, heatmaps, and eye-tracking indices indicate the advantages of using the AWE system, which effectively enhances students’ ability to recognize writing errors while reducing processing time by facilitating the internalization of writing errors through continuous feedback on such errors, and enabling them to apply this knowledge to new materials, thereby recognizing writing errors more quickly and accurately, and thus helping them to reduce writing errors. The pedagogical implications are fully discussed.
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引用次数: 0
PythonPal: Enhancing Online Programming Education Through Chatbot-Driven Personalized Feedback
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-28 DOI: 10.1109/TLT.2025.3545084
Sirinda Palahan
The rise of online programming education has necessitated more effective personalized interactions, a gap that PythonPal aims to fill through its innovative learning system integrated with a chatbot. This research delves into PythonPal's potential to enhance the online learning experience, especially in contexts with high student-to-teacher ratios where there is a need for personalized feedback. PythonPal's design, featuring modules for conversation, tutorials, and exercises, was evaluated through student interactions and feedback. Key findings reveal PythonPal's proficiency in syntax error recognition and user query comprehension, with its intent classification model showing high accuracy. The system's performance in error feedback, though varied, demonstrates both strengths and areas for enhancement. Student feedback indicated satisfactory query understanding and feedback accuracy but also pointed out the need for faster responses and improved interaction quality. PythonPal's deployment promises to significantly enhance online programming education by providing immediate personalized feedback and interactive learning experiences, fostering a deeper understanding of programming concepts among students. These benefits mark a step forward in addressing the challenges of distance learning, making programming education more accessible and effective.
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引用次数: 0
An Intelligent Tutoring System to Support Code Maintainability Skill Development
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-25 DOI: 10.1109/TLT.2025.3545641
Nikola M. Luburić;Luka Ž. Dorić;Jelena J. Slivka;Dragan Lj. Vidaković;Katarina-Glorija G. Grujić;Aleksandar D. Kovačević;Simona B. Prokić
Software engineers are tasked with writing functionally correct code of high quality. Maintainability is a crucial code quality attribute that determines the ease of analyzing, modifying, reusing, and testing a software component. This quality attribute significantly affects the software's lifetime cost, contributing to developer productivity and other quality attributes. Consequently, academia and industry emphasize the need to train software engineers to build maintainable software code. Unfortunately, code maintainability is an ill-defined domain and is challenging to teach and learn. This problem is aggravated by a rising number of software engineering students and a lack of capable instructors. Existing instructors rely on scalable one-size-fits-all teaching methods that are ineffective. Advances in e-learning technologies can alleviate these issues. Our primary contribution is the design of a novel assessment item type, the maintainability challenge. It integrates into the standard intelligent tutoring system (ITS) architecture to develop skills for analyzing and refactoring high-level code maintainability issues. Our secondary contributions include the code maintainability knowledge component model and the implementation of an ITS that supports the maintainability challenge for the C# programming language. We designed, developed, and evaluated the ITS over two years of working with undergraduate students using a mixed-method approach anchored in design science. The empirical evaluations culminated with a field study with 59 undergraduate students. We report on the evaluation results that showcase the utility of our contributions. Our contributions support software engineering instructors in developing the code maintainability skills of their students at scale.
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引用次数: 0
Enhancing Sand-Table-Based Incident Command Training With Extended Reality and Interactive Simulations: A Use Case in Forest Firefighting
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-24 DOI: 10.1109/TLT.2025.3545436
Lorenzo Valente;Federico De Lorenzis;Davide Calandra;Fabrizio Lamberti
In recent years, first responders have faced increasing challenges in their operations, highlighting a growing need for specialized and comprehensive training. In particular, the firefighting incident commanders (ICs) are playing a pivotal role, providing directions to field operators and making critical decisions in emergency situations. Over time, traditional training tools in this field have evolved, reaching their pinnacle with augmented sand tables (ASTs). ASTs build on spatial augmented reality (SAR), a form of extended reality (XR) that utilizes projections. Although ASTs enable large-scale visualization of the morphological features of the terrain, by relying solely on SAR, it is not possible to fully leverage the potential of XR, which is increasingly recognized as a powerful tool for training. This work introduces a novel approach to training ICs by integrating ASTs with XR, incorporating a learning-by-doing methodology alongside an objective measurement of trainees' performance. To this end, an XR training system (XRTS) has been developed, combining the capabilities of an AST with personal mixed reality devices and integrating a physically accurate interactive fire simulator. This system was deployed within a forest firefighting IC training course. All the system components were designed based on the theoretical foundations of decision making to effectively develop the necessary skills. The proposed approach was compared with traditional AST-based training methods for these roles, focusing on the analysis of learning outcomes, user experience, usability, and cognitive load. The study demonstrated several advantages associated with the use of the XRTS, including improvements in training effectiveness and a notable reduction in overall cognitive load.
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引用次数: 0
LEMON: A Knowledge-Enhanced, Type-Constrained, and Grammar-Guided Model for Question Generation Over Knowledge Graphs
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-20 DOI: 10.1109/TLT.2025.3544454
Sheng Bi;Zeyi Miao;Qizhi Min
The objective of question generation from knowledge graphs (KGQG) is to create coherent and answerable questions from a given subgraph and a specified answer entity. KGQG has garnered significant attention due to its pivotal role in enhancing online education. Encoder–decoder architectures have advanced traditional KGQG approaches. However, these approaches encounter challenges in achieving question diversity and grammatical accuracy. They often suffer from a disconnect between the phrasing of the question and the type of the answer entity, a phenomenon known as semantic drift. To address these challenges, we introduce LEMON, a knowledge-enhanced, type-constrained, and grammar-guided model for KGQG. LEMON enhances the input by integrating entity-related knowledge using heuristic rules, which fosters diversity in question generation. It employs a hierarchical global relation embedding with translation loss to align questions with entity types. In addition, it utilizes a graph-based module to aggregate type information from neighboring nodes. The LEMON model incorporates a type-constrained decoder to generate diverse expressions and improves grammatical accuracy through a syntactic and semantic reward function via reinforcement learning. Evaluations on benchmark datasets demonstrate LEMON's strong competitiveness. The study also examines the impact of question generation quality on question-answering systems, providing guidance for future research endeavors in this domain.
从知识图谱生成问题(KGQG)的目的是根据给定的子图谱和指定的答案实体创建连贯且可回答的问题。KGQG 在加强在线教育方面发挥着举足轻重的作用,因而备受关注。编码器-解码器架构推进了传统的 KGQG 方法。然而,这些方法在实现问题多样性和语法准确性方面遇到了挑战。它们经常会遇到问题措辞与答案实体类型脱节的问题,这种现象被称为语义漂移。为了应对这些挑战,我们引入了 LEMON,这是一种知识增强型、类型受限型和语法指导型 KGQG 模型。LEMON 通过使用启发式规则整合实体相关知识来增强输入,从而促进问题生成的多样性。它采用带有翻译损失的分层全局关系嵌入,使问题与实体类型保持一致。此外,它还利用基于图的模块,从相邻节点汇总类型信息。LEMON 模型包含一个类型受限解码器,可生成多样化的表达,并通过强化学习的句法和语义奖励功能提高语法准确性。在基准数据集上进行的评估证明了 LEMON 的强大竞争力。研究还探讨了问题生成质量对问题解答系统的影响,为该领域未来的研究工作提供了指导。
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引用次数: 0
Navigating the Textual Maze: Enhancing Textual Analytical Skills Through an Innovative GAI Prompt Framework
IF 2.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2025-02-05 DOI: 10.1109/TLT.2025.3539104
Xuefan Li;Tingsong Li;Minjuan Wang;Sining Tao;Xiaoxu Zhou;Xiaoqing Wei;Naiqing Guan
With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students’ analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically targeting readability, accuracy, completeness, logicality, and critical thinking. Conducted among high school students in Canada, the research assesses how GAI prompt frameworks significantly affect the quality of students’ analytical responses. Results showed significant improvements in all five aspects of readability, accuracy, completeness, logicality, and critical thinking, especially for students with no prior knowledge of the topic. However, enhancements in completeness and critical thinking were less pronounced, suggesting that while the ChatGPT framework substantially supports basic analytical skills, its effectiveness varies depending on the complexity of cognitive tasks and the extent of students’ existing knowledge. The study underscores the significant role that advanced GAI tools can play in modern educational environments, promoting deeper engagement with learning materials and enhancing students’ analytical abilities. It highlights the necessity of integrating these technologies to cater to diverse learning needs and cognitive challenges.
{"title":"Navigating the Textual Maze: Enhancing Textual Analytical Skills Through an Innovative GAI Prompt Framework","authors":"Xuefan Li;Tingsong Li;Minjuan Wang;Sining Tao;Xiaoxu Zhou;Xiaoqing Wei;Naiqing Guan","doi":"10.1109/TLT.2025.3539104","DOIUrl":"https://doi.org/10.1109/TLT.2025.3539104","url":null,"abstract":"With the rapid advancement of generative artificial intelligence (GAI), its application in educational settings has increasingly become a focal point, particularly in enhancing students’ analytical capabilities. This study examines the effectiveness of the ChatGPT prompt framework in improving text analysis skills among students, specifically targeting readability, accuracy, completeness, logicality, and critical thinking. Conducted among high school students in Canada, the research assesses how GAI prompt frameworks significantly affect the quality of students’ analytical responses. Results showed significant improvements in all five aspects of readability, accuracy, completeness, logicality, and critical thinking, especially for students with no prior knowledge of the topic. However, enhancements in completeness and critical thinking were less pronounced, suggesting that while the ChatGPT framework substantially supports basic analytical skills, its effectiveness varies depending on the complexity of cognitive tasks and the extent of students’ existing knowledge. The study underscores the significant role that advanced GAI tools can play in modern educational environments, promoting deeper engagement with learning materials and enhancing students’ analytical abilities. It highlights the necessity of integrating these technologies to cater to diverse learning needs and cognitive challenges.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"18 ","pages":"206-215"},"PeriodicalIF":2.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521505","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
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