Pub Date : 2023-12-15DOI: 10.1109/TLT.2023.3324843
Anasol Peña-Rios;Junjie Gavin Wu
The metaverse is seen as an evolution paradigm of the next-generation Internet, able to support a diverse range of persistent and always-on interconnected synchronous multiuser virtual environments where people can engage with others in real time, merging the physical and virtual world [1], [2], [3]. The concept was first mentioned in 1992s Neal Stephenson novel “ Snow Crash ” [4], and it follows the web and mobile Internet revolutions, allowing users to experience virtual environments in an immersive and hyperspatiotemporal manner [1]. Thus, it represents a paradigm shift in digital interaction, enabling real-time, multidimensional experiences that transcend the boundaries of physical space with the promise of bringing new levels of social connection and collaboration. The metaverse exists within the Internet, but not in the traditional way of seeing the world through a screen [1]. Instead, the metaverse aims to provide immersive experiences based on the convergence of spatial computing technologies that enable multisensory user interactions [e.g., virtual reality (VR), augmented reality (AR), and mixed reality (MR)] [2], [3] combined with 3-D data and artificial intelligence. The metaverse is also related to the concept of digital twins (DTs), which are digital replicas of elements in the real world (e.g., assets and processes) that mirror and synchronize in real time with their source, creating a bidirectional connection between them. While DTs focus more on the bidirectional connection between real and virtual and the accuracy of the representation toward better decision-making, the metaverse looks at sociotechnical challenges of seamless embodied communication between users and the dynamic interactions with the virtual spaces.
{"title":"Guest Editorial The Metaverse and the Future of Education","authors":"Anasol Peña-Rios;Junjie Gavin Wu","doi":"10.1109/TLT.2023.3324843","DOIUrl":"https://doi.org/10.1109/TLT.2023.3324843","url":null,"abstract":"The metaverse is seen as an evolution paradigm of the next-generation Internet, able to support a diverse range of persistent and always-on interconnected synchronous multiuser virtual environments where people can engage with others in real time, merging the physical and virtual world [1], [2], [3]. The concept was first mentioned in 1992s Neal Stephenson novel “\u0000<italic>Snow Crash</i>\u0000” [4], and it follows the web and mobile Internet revolutions, allowing users to experience virtual environments in an immersive and \u0000<italic>hyperspatiotemporal</i>\u0000 manner [1]. Thus, it represents a paradigm shift in digital interaction, enabling real-time, multidimensional experiences that transcend the boundaries of physical space with the promise of bringing new levels of social connection and collaboration. The metaverse exists within the Internet, but not in the traditional way of seeing the world through a screen [1]. Instead, the metaverse aims to provide immersive experiences based on the convergence of spatial computing technologies that enable multisensory user interactions [e.g., virtual reality (VR), augmented reality (AR), and mixed reality (MR)] [2], [3] combined with 3-D data and artificial intelligence. The metaverse is also related to the concept of digital twins (DTs), which are digital replicas of elements in the real world (e.g., assets and processes) that mirror and synchronize in real time with their source, creating a bidirectional connection between them. While DTs focus more on the bidirectional connection between real and virtual and the accuracy of the representation toward better decision-making, the metaverse looks at sociotechnical challenges of seamless embodied communication between users and the dynamic interactions with the virtual spaces.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"16 6","pages":"887-891"},"PeriodicalIF":3.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.1109/TLT.2023.3329612
{"title":"IEEE Education Society Information","authors":"","doi":"10.1109/TLT.2023.3329612","DOIUrl":"https://doi.org/10.1109/TLT.2023.3329612","url":null,"abstract":"","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"16 6","pages":"C2-C2"},"PeriodicalIF":3.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678714","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}
Predicting student engagement can provide timely feedback and help teachers make adjustments to their practices to meet student needs and improve their learning experience. This article proposes a four-step approach using a sequential ensemble model for engagement prediction, discusses the contribution of different features to the model and the influence of video segmentation in the prediction, reports on two in-the-wild datasets-The Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset published in 2018 as part of a student engagement task and the Dataset for Affective States in E-Environments (DAiSEE), a general purpose dataset also used in the educational context but not limited to it, and, finally, presents a comprehensive and thorough critical analysis, highlighting crucial factors to consider when using AI/computer vision models in educational datasets for learning purposes. Experiments show that our proposed approach outperforms state-of-the-art approaches by obtaining a mean square error of 0.0386 on the DAiSEE dataset and 0.0610 on the EmotiW-EP dataset. We conclude this article with a critical analysis of the reliability of such predictions in learning environments and propose future directions for the effective use of AI/computer vision models in education.
{"title":"Predicting Student Engagement Using Sequential Ensemble Model","authors":"Xinran Tian;Bernardo Pereira Nunes;Yifeng Liu;Ruben Manrique","doi":"10.1109/TLT.2023.3342860","DOIUrl":"https://doi.org/10.1109/TLT.2023.3342860","url":null,"abstract":"Predicting student engagement can provide timely feedback and help teachers make adjustments to their practices to meet student needs and improve their learning experience. This article proposes a four-step approach using a sequential ensemble model for engagement prediction, discusses the contribution of different features to the model and the influence of video segmentation in the prediction, reports on two in-the-wild datasets-The Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset published in 2018 as part of a student engagement task and the Dataset for Affective States in E-Environments (DAiSEE), a general purpose dataset also used in the educational context but not limited to it, and, finally, presents a comprehensive and thorough critical analysis, highlighting crucial factors to consider when using AI/computer vision models in educational datasets for learning purposes. Experiments show that our proposed approach outperforms state-of-the-art approaches by obtaining a mean square error of 0.0386 on the DAiSEE dataset and 0.0610 on the EmotiW-EP dataset. We conclude this article with a critical analysis of the reliability of such predictions in learning environments and propose future directions for the effective use of AI/computer vision models in education.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"939-950"},"PeriodicalIF":3.7,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-13DOI: 10.1109/TLT.2023.3341490
Abdallah Al-Zoubi;Elio San Cristobal;Fadi R. Shahroury;Manuel Castro
In the ever-evolving landscape of technology, Industry 4.0 stands as a monumental revolution that intertwines man and machine, reshaping the dynamics of labor and work environments. This paradigm shift demands a new outlook and necessitates a fresh set of skills and competencies to navigate the intricate web of advancements. From engineers to entrepreneurs, programmers to workers, the fourth industrial revolution mandates a versatile skillset to embrace its technological leaps. Amidst this transformation, the role of remote labs emerges as a potent platform, offering efficient and dependable means to cultivate students’ expertise, thus preparing them for the dawn of the Work 4.0 era. This article delves into the story of Princess Sumaya University for Technology, shedding light on its ingenious employment of remote labs to provide students with a firsthand encounter with Industry 4.0. Furthermore, it explores innovative assessment techniques to foster virtual collaboration, social intelligence, and communication skills among students, highlighting remarkable enhancements in performance and achievements.
{"title":"The Middle East Higher Education Experience: Implementing Remote Labs to Improve the Acquisition of Skills in Industry 4.0","authors":"Abdallah Al-Zoubi;Elio San Cristobal;Fadi R. Shahroury;Manuel Castro","doi":"10.1109/TLT.2023.3341490","DOIUrl":"https://doi.org/10.1109/TLT.2023.3341490","url":null,"abstract":"In the ever-evolving landscape of technology, Industry 4.0 stands as a monumental revolution that intertwines man and machine, reshaping the dynamics of labor and work environments. This paradigm shift demands a new outlook and necessitates a fresh set of skills and competencies to navigate the intricate web of advancements. From engineers to entrepreneurs, programmers to workers, the fourth industrial revolution mandates a versatile skillset to embrace its technological leaps. Amidst this transformation, the role of remote labs emerges as a potent platform, offering efficient and dependable means to cultivate students’ expertise, thus preparing them for the dawn of the Work 4.0 era. This article delves into the story of Princess Sumaya University for Technology, shedding light on its ingenious employment of remote labs to provide students with a firsthand encounter with Industry 4.0. Furthermore, it explores innovative assessment techniques to foster virtual collaboration, social intelligence, and communication skills among students, highlighting remarkable enhancements in performance and achievements.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"982-991"},"PeriodicalIF":3.7,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1109/TLT.2023.3341420
Yuting Chen;Ming Li;Changqin Huang;Mutlu Cukurova;Qing Ma
Immersive technology has received extensive attention in both L1 and L2 writing education. Its unique capabilities to offer virtual experiences alongside real-world experiences can create authentic learning environments that support students' experiential learning and enable the observation of events beyond the confines of traditional classrooms. However, there has been a lack of systematic analysis of recent publications in this area. To address this gap and improve the research and practice of writing education, a systematic review was conducted to examine the literature on immersive technology in writing education (ITWE). In this review, 37 articles (30 SSCI-indexed papers from the Web of Science database and seven additional articles from a meticulous forward–backward scan of the references of these studies) were analyzed. The analysis focused on theoretical foundations, participants, types of adopted immersive technology, methods, and research findings. Our review shows that although most studies revealed positive outcomes, a significant number lacked a solid theoretical foundation to interpret the findings in many ITWE studies. Moreover, there is a pressing need for further research on ITWE in middle schools, especially within the realm of English as a foreign language courses. In addition, the review identified some potential negative effects of ITWE, which were often attributed to poorly designed instructional activities. It was observed that conventional research methods like questionnaire surveys and interviews, were commonly used in ITWE. However, the potential benefits of emerging areas like learning analytics and AI in education (e.g., logged actions, facial emotion detection, electroencephalogram (EEG) analysis were rarely used. The article is concluded with the current research evidence on emerging directions and opportunities for future trends in empowering writing education with immersive technology.
{"title":"A Systematic Review of Research on Immersive Technology-Enhanced Writing Education: The Current State and a Research Agenda","authors":"Yuting Chen;Ming Li;Changqin Huang;Mutlu Cukurova;Qing Ma","doi":"10.1109/TLT.2023.3341420","DOIUrl":"https://doi.org/10.1109/TLT.2023.3341420","url":null,"abstract":"Immersive technology has received extensive attention in both L1 and L2 writing education. Its unique capabilities to offer virtual experiences alongside real-world experiences can create authentic learning environments that support students' experiential learning and enable the observation of events beyond the confines of traditional classrooms. However, there has been a lack of systematic analysis of recent publications in this area. To address this gap and improve the research and practice of writing education, a systematic review was conducted to examine the literature on immersive technology in writing education (ITWE). In this review, 37 articles (30 SSCI-indexed papers from the Web of Science database and seven additional articles from a meticulous forward–backward scan of the references of these studies) were analyzed. The analysis focused on theoretical foundations, participants, types of adopted immersive technology, methods, and research findings. Our review shows that although most studies revealed positive outcomes, a significant number lacked a solid theoretical foundation to interpret the findings in many ITWE studies. Moreover, there is a pressing need for further research on ITWE in middle schools, especially within the realm of English as a foreign language courses. In addition, the review identified some potential negative effects of ITWE, which were often attributed to poorly designed instructional activities. It was observed that conventional research methods like questionnaire surveys and interviews, were commonly used in ITWE. However, the potential benefits of emerging areas like learning analytics and AI in education (e.g., logged actions, facial emotion detection, electroencephalogram (EEG) analysis were rarely used. The article is concluded with the current research evidence on emerging directions and opportunities for future trends in empowering writing education with immersive technology.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"919-938"},"PeriodicalIF":3.7,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-06DOI: 10.1109/TLT.2023.3339882
Samantha Orlando;Elena Gaudioso;Félix de la Paz
Nowadays, there is an increasing interest in using different technologies, such as educational robotics in classrooms. However, in many cases, teachers have neither the necessary background to efficiently use these kits nor the information about how students are using robotics in classroom. To support teachers, learning environments with robotics tools should monitor the students' interaction data while they are interacting with the different resources provided. With the analysis of this data, teachers can obtain valuable information about students' learning progress. In previous work, we presented integrated didactic educational environment (IDEE), an integrated learning environment that uses robotics to support physics laboratories in secondary education. Students' interactions with IDEE are stored and analyzed using the additive factor model to show the teachers the most significant skills in the learning process and those students who have difficulties with these skills. Now, our goal is to enhance the information given to the teachers to allow them to focus on the specific needs of each student on every different skill involved in the activities and not only the significant skills. To this end, we use a conjunctive knowledge tracing model based on a hidden Markov model. In this article: first, we describe how the CKT model has been adapted to the pedagogical model of IDEE, second, we show that this model can identify the skills that each student masters, and thus, support teachers in identifying learning criticalities in students.
如今，在课堂上使用教育机器人等不同技术的兴趣与日俱增。然而，在许多情况下，教师既不具备有效使用这些工具包的必要背景，也不了解学生在课堂上使用机器人的情况。为了给教师提供支持，使用机器人工具的学习环境应监控学生与所提供的不同资源进行交互时的交互数据。通过分析这些数据，教师可以获得有关学生学习进度的宝贵信息。在之前的工作中，我们介绍了综合教学教育环境（IDEE），这是一种利用机器人技术为中学物理实验室提供支持的综合学习环境。学生与 IDEE 的互动被存储起来，并通过加因子模型进行分析，从而向教师展示学习过程中最重要的技能，以及在这些技能上有困难的学生。现在，我们的目标是加强提供给教师的信息，使他们能够关注每个学生在活动中所涉及的每种不同技能上的具体需求，而不仅仅是重要技能。为此，我们使用了基于隐马尔可夫模型的连接知识追踪模型。在本文中：首先，我们介绍了如何将 CKT 模型调整为 IDEE 的教学模型；其次，我们展示了该模型可以识别每个学生掌握的技能，从而支持教师识别学生的学习关键点。
{"title":"Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE Experience","authors":"Samantha Orlando;Elena Gaudioso;Félix de la Paz","doi":"10.1109/TLT.2023.3339882","DOIUrl":"https://doi.org/10.1109/TLT.2023.3339882","url":null,"abstract":"Nowadays, there is an increasing interest in using different technologies, such as educational robotics in classrooms. However, in many cases, teachers have neither the necessary background to efficiently use these kits nor the information about how students are using robotics in classroom. To support teachers, learning environments with robotics tools should monitor the students' interaction data while they are interacting with the different resources provided. With the analysis of this data, teachers can obtain valuable information about students' learning progress. In previous work, we presented integrated didactic educational environment (IDEE), an integrated learning environment that uses robotics to support physics laboratories in secondary education. Students' interactions with IDEE are stored and analyzed using the additive factor model to show the teachers the most significant skills in the learning process and those students who have difficulties with these skills. Now, our goal is to enhance the information given to the teachers to allow them to focus on the specific needs of each student on every different skill involved in the activities and not only the significant skills. To this end, we use a conjunctive knowledge tracing model based on a hidden Markov model. In this article: first, we describe how the CKT model has been adapted to the pedagogical model of IDEE, second, we show that this model can identify the skills that each student masters, and thus, support teachers in identifying learning criticalities in students.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"874-884"},"PeriodicalIF":3.7,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10345689","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and attitudes or values related to open education and its subcompetencies from a 30-item questionnaire using machine learning models to forecast participants' gender, 2) validation of performance through cross-validation methods, 3) statistical analysis to find significant differences between machine learning models, and 4) an analysis from explainable machine learning models to find relevant features to forecast gender. The results confirm our hypothesis that the performance of machine learning models can effectively forecast gender based on the student's perceptions of knowledge, skills, and attitudes or values related to open education competency.
{"title":"Forecasting Gender in Open Education Competencies: A Machine Learning Approach","authors":"Gerardo Ibarra-Vazquez;María Soledad Ramírez-Montoya;Mariana Buenestado-Fernández","doi":"10.1109/TLT.2023.3336541","DOIUrl":"https://doi.org/10.1109/TLT.2023.3336541","url":null,"abstract":"This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and attitudes or values related to open education and its subcompetencies from a 30-item questionnaire using machine learning models to forecast participants' gender, 2) validation of performance through cross-validation methods, 3) statistical analysis to find significant differences between machine learning models, and 4) an analysis from explainable machine learning models to find relevant features to forecast gender. The results confirm our hypothesis that the performance of machine learning models can effectively forecast gender based on the student's perceptions of knowledge, skills, and attitudes or values related to open education competency.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1236-1247"},"PeriodicalIF":3.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140192052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.1109/TLT.2023.3337107
Meshal Albeedan;Hoshang Kolivand;Ramy Hammady;Tanzila Saba
Investigation training at the real crime scene is a critical component of forensic science education. However, bringing young investigators to real crime scenes is costly and faces significant challenges. Mixed reality (MR) is one of the most evolving technologies that provides unlimited possibilities for practical activities in the education sector. This article aims to propose and evaluate a novel design of an MR system using Microsoft HoloLens 2.0 that is tailored to work in a spatial 3-D-scanned and reconstructed crime scene. The system was designed to be a cost-effective experience that helps young Kuwaiti police officers enhance their investigation skills. The proposed system has been evaluated through system usability, user interaction, and performance metrics quantitatively via 44 young police officers and qualitatively using the think-aloud protocols via a group of experts. Both groups showed positive levels of usability, user interaction, and overall satisfaction, with minimal negative feedback. Based on the positive feedback, the system will be taken into the commercialization stage in the future. Despite the high cost of the MR device, experts stated that the system is needed as an essential tool for crime scene education and investigation practices.
{"title":"Seamless Crime Scene Reconstruction in Mixed Reality for Investigation Training: A Design and Evaluation Study","authors":"Meshal Albeedan;Hoshang Kolivand;Ramy Hammady;Tanzila Saba","doi":"10.1109/TLT.2023.3337107","DOIUrl":"https://doi.org/10.1109/TLT.2023.3337107","url":null,"abstract":"Investigation training at the real crime scene is a critical component of forensic science education. However, bringing young investigators to real crime scenes is costly and faces significant challenges. Mixed reality (MR) is one of the most evolving technologies that provides unlimited possibilities for practical activities in the education sector. This article aims to propose and evaluate a novel design of an MR system using Microsoft HoloLens 2.0 that is tailored to work in a spatial 3-D-scanned and reconstructed crime scene. The system was designed to be a cost-effective experience that helps young Kuwaiti police officers enhance their investigation skills. The proposed system has been evaluated through system usability, user interaction, and performance metrics quantitatively via 44 young police officers and qualitatively using the think-aloud protocols via a group of experts. Both groups showed positive levels of usability, user interaction, and overall satisfaction, with minimal negative feedback. Based on the positive feedback, the system will be taken into the commercialization stage in the future. Despite the high cost of the MR device, experts stated that the system is needed as an essential tool for crime scene education and investigation practices.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"856-873"},"PeriodicalIF":3.7,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-23DOI: 10.1109/TLT.2023.3336240
Liqing Qiu;Menglin Zhu;Jingcheng Zhou
Knowledge tracing (KT) is essential in intelligent tutoring systems for tracking learners' knowledge states and predicting their future performance. Numerous prevailing KT methods prioritize modeling learners' behavioral patterns in acquiring knowledge and the relationship among interactions. However, due to the sparsity problem, they frequently encounter challenges in effectively uncovering latent contextual features embedded within the learning sequences. This limitation may impose certain constraints on the predictive performance. In light of this concern, this article focuses on extracting latent features from learning sequences to enhance the assessment of knowledge states. Consequently, we design optimized pretraining mechanisms and introduce an enhanced deep KT method, optimized pretraining deep KT (OPKT). In the pretraining phase, the self-supervised learning approach is effectively employed to train comprehensive contextual encodings of the learning sequences. During fine-tuning, the contextual encodings are transferred to the downstream KT model, which then generates the knowledge states and makes predictions. Through our experiments, the superiority of our method over six existing KT models on five publicly available datasets is demonstrated. Furthermore, extensive ablation studies and visualized analysis validate the rationality and effectiveness of every component of the OPKT architecture.
{"title":"OPKT: Enhancing Knowledge Tracing With Optimized Pretraining Mechanisms in Intelligent Tutoring","authors":"Liqing Qiu;Menglin Zhu;Jingcheng Zhou","doi":"10.1109/TLT.2023.3336240","DOIUrl":"https://doi.org/10.1109/TLT.2023.3336240","url":null,"abstract":"Knowledge tracing (KT) is essential in intelligent tutoring systems for tracking learners' knowledge states and predicting their future performance. Numerous prevailing KT methods prioritize modeling learners' behavioral patterns in acquiring knowledge and the relationship among interactions. However, due to the sparsity problem, they frequently encounter challenges in effectively uncovering latent contextual features embedded within the learning sequences. This limitation may impose certain constraints on the predictive performance. In light of this concern, this article focuses on extracting latent features from learning sequences to enhance the assessment of knowledge states. Consequently, we design optimized pretraining mechanisms and introduce an enhanced deep KT method, optimized pretraining deep KT (OPKT). In the pretraining phase, the self-supervised learning approach is effectively employed to train comprehensive contextual encodings of the learning sequences. During fine-tuning, the contextual encodings are transferred to the downstream KT model, which then generates the knowledge states and makes predictions. Through our experiments, the superiority of our method over six existing KT models on five publicly available datasets is demonstrated. Furthermore, extensive ablation studies and visualized analysis validate the rationality and effectiveness of every component of the OPKT architecture.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"841-855"},"PeriodicalIF":3.7,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1109/TLT.2023.3334788
Yalda Ghasemi;Allison Bayro;Justin MacDonald;Heejin Jeong;Joel Reynolds;Chang S. Nam
Cooking is a multitasking and rule-based task that can benefit from augmented reality (AR). This article introduces sAR Kitchen , an AR-based cooking assistant designed to incorporate spatial AR (sAR) into culinary training. We investigated the effects of instructions provided through our proposed sAR system compared to a monitor display featuring video tutorials in a task involving making playdough, which serves as a representation of a cooking task. We assessed perceived workload, usability, and performance measures (including task completion time, product quality, and cooking station messiness) through a user study involving 22 participants. We conducted a statistical comparison between the two methods to explore significant differences. In addition, we analyzed open-ended questions to provide further insights based on the participants' statements. The results indicated that sAR significantly reduced the perceived workload and improved the system's usability. Furthermore, the AR instructions for training proved to be as effective as conventional methods of instruction while also addressing some of the limitations associated with standard displays and enhancing the overall user experience. This study has the potential to pave the way for more efficient learning and training methods by incorporating sAR.
烹饪是一项多任务和基于规则的任务，可以从增强现实（AR）中获益。本文介绍了 sAR Kitchen，这是一款基于 AR 的烹饪助手，旨在将空间 AR（sAR）融入烹饪培训中。我们研究了在一项涉及制作橡皮泥的任务中，通过我们提出的 sAR 系统提供的指导与显示器上的视频教程相比所产生的效果。我们通过一项有 22 名参与者参与的用户研究，对感知工作量、可用性和性能指标（包括任务完成时间、产品质量和烹饪台的混乱程度）进行了评估。我们对两种方法进行了统计比较，以探究它们之间的显著差异。此外，我们还分析了开放式问题，以便根据参与者的陈述提供进一步的见解。研究结果表明，sAR 大大降低了感知工作量，提高了系统的可用性。此外，用于培训的 AR 指导被证明与传统指导方法一样有效，同时还解决了与标准显示器相关的一些局限性，并增强了整体用户体验。这项研究有可能通过结合 sAR 为更高效的学习和培训方法铺平道路。
{"title":"Embedding Spatial Augmented Reality in Culinary Training: A Comparative Evaluation of sAR Kitchen and Video Tutorials","authors":"Yalda Ghasemi;Allison Bayro;Justin MacDonald;Heejin Jeong;Joel Reynolds;Chang S. Nam","doi":"10.1109/TLT.2023.3334788","DOIUrl":"https://doi.org/10.1109/TLT.2023.3334788","url":null,"abstract":"Cooking is a multitasking and rule-based task that can benefit from augmented reality (AR). This article introduces \u0000<italic>sAR Kitchen</i>\u0000, an AR-based cooking assistant designed to incorporate spatial AR (sAR) into culinary training. We investigated the effects of instructions provided through our proposed sAR system compared to a monitor display featuring video tutorials in a task involving making playdough, which serves as a representation of a cooking task. We assessed perceived workload, usability, and performance measures (including task completion time, product quality, and cooking station messiness) through a user study involving 22 participants. We conducted a statistical comparison between the two methods to explore significant differences. In addition, we analyzed open-ended questions to provide further insights based on the participants' statements. The results indicated that sAR significantly reduced the perceived workload and improved the system's usability. Furthermore, the AR instructions for training proved to be as effective as conventional methods of instruction while also addressing some of the limitations associated with standard displays and enhancing the overall user experience. This study has the potential to pave the way for more efficient learning and training methods by incorporating sAR.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"765-775"},"PeriodicalIF":3.7,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419475","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}
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