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Experimenting with Soft Robotics in Education: A Systematic Literature Review from 2006 to 2022 IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-03-05 Israel Ulises Cayetano-Jiménez, Erick Axel Martinez-Ríos, Rogelio Bustamante-Bello, Ricardo Ramírez-Mendoza, María Soledad Ramírez-Montoya
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Enhancing Medical Training through Learning from Mistakes by Interacting with an Ill-trained Reinforcement Learning Agent IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-03-04 Yasar C. Kakdas, Sinan Kockara, Tansel Halic, Doga Demirel
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Supporting Teachers’ Professional Development With Generative AI: The Effects on Higher Order Thinking and Self-Efficacy IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-26 Jijian Lu, Ruxin Zheng, Zikun Gong, Huifen Xu
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Facilitating the Learning Engineering Process for Educational Conversational Modules Using Transformer-Based Language Models IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-20 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
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Exploring the Possibilities of Edu-Metaverse: A New 3D Ecosystem Model for Innovative Learning IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-12 Tracy Bobko, Mikiko Corsette, Minjuan Wang, Erin Springer
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Intelligent Retrieval and Comprehension of Entrepreneurship Education Resources Based on Semantic Summarization of Knowledge Graphs IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-09 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
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Using a Chatbot to Provide Formative Feedback: A Longitudinal Study of Intrinsic Motivation, Cognitive Load, and Learning Performance IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-08 Jiaqi Yin, Tiong-Thye Goh, Bing Yang, Yi Hu
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Serious Video Games for Agricultural Learning: Scoping Review IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-08 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
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Blended Laboratory Design Using Raspberry Pi Pico for Digital Circuits and Systems IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-02-07 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
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How Can Self-Evaluation and Self-Efficacy Skills of Young Learners be Scaffolded in a Web Application? IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-30 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
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Advancing Education Through Extended Reality and Internet of Everything Enabled Metaverses: Applications, Challenges, and Open Issues IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-26 Senthil Kumar Jagatheesaperumal, Kashif Ahmad, Ala Al-Fuqaha, Junaid Qadir
Metaverse has evolved as one of the popular research agenda that let users learn, socialize, and collaborate in a networked 3-D immersive virtual world. Due to the rich multimedia streaming capability and immersive user experience with high-speed communication, the metaverse is an ideal model for education, training, and skill development tasks. To facilitate research in this area, we provide a comprehensive
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A Flipped Remote Lab: Using a Peer-Assessment Tool for Learning 3-D Modeling IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-26 Mario Vallarino, Saverio Iacono, Edoardo Bellanti, Gianni V. Vercelli
This article introduces a novel approach to remote laboratory instruction, specifically designed for teaching three-dimensional modeling using Blender software. The lab uses virtual machines to provide students with the necessary computational power to carry out the course activities, along with the correct version of the software. The flipped remote lab approach combines the elements of flipped classroom
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Learning Style Identification Using Semisupervised Self-Taught Labeling IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-26 Hani Y. Ayyoub, Omar S. Al-Kadi
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that meets students’ needs. While learning management systems
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Assessment of Kinematic and Kinetic Attributes of Graphic Execution of Children With Autism and Typically Developing Children Using a Digitized Platform IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-18 Pragya Verma, Niravkumar Patel, Prachi Sharma, Manasi Anand Kanetkar, Madhu Singh, Uttama Lahiri
Intact graphic execution ability is considered an important gateway to one's academic success. It is often reported that the graphic execution ability of neurotypical children and those having autism, i.e., children with autism spectrum disorder (ASD) is differentiated. Although insightful, these reports had been mostly for text handwriting task that is not language agnostic (with observations related
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Supporting Collaborative Writing Tasks in Large-Scale Distance Education IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-18 Marc Burchart, Joerg M. Haake
In distance education courses with a large number of students and groups, the organization and facilitation of collaborative writing tasks are challenging. Teachers need support for planning, specification, execution, monitoring, and evaluation of collaborative writing tasks in their course. This requires a collaborative learning platform for coordinating all of the different phases in the writing
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Student Perceptions of ChatGPT Use in a College Essay Assignment: Implications for Learning, Grading, and Trust in Artificial Intelligence IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-16 Chad C. Tossell, Nathan L. Tenhundfeld, Ali Momen, Katrina Cooley, Ewart J. de Visser
This article examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre–post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational value and impact on the learning process. Our quantitative
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Student-Facing Learning Analytics Dashboard for Remote Lab Practical Work IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-15 David P. Reid, Timothy D. Drysdale
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment to provide feedback to students during remote lab activities
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Collaborative Learning in the Edu-Metaverse Era: An Empirical Study on the Enabling Technologies IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-12 Chen Li, Yue Jiang, Peter H. F. Ng, Yixin Dai, Francis Cheung, Henry C. B. Chan, Ping Li
Computer-supported collaborative learning aims to use information technologies to support collaborative knowledge construction by practicing the relevant pedagogical approaches, especially in the distance learning setting. The enabling technologies are fast advancing, and the need for solutions during the COVID-19 global pandemic led to the emergence of the Edu-Metaverse, which is conceptualized as
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PERKC: Personalized kNN With CPT for Course Recommendations in Higher Education IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-10 Gina George, Anisha M. Lal
E-learning is increasingly being used by students in the higher education level for their university credit purpose and some for improving their knowledge. E-learning is also used for skill enhancement purpose by organizations. Due to the availability of wide-ranging options, recommender systems that provide personalized suggestions are much needed. The proposed methodology takes advantage of compact
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When the Past != The Future: Assessing the Impact of Dataset Drift on the Fairness of Learning Analytics Models IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-09 Oscar Blessed Deho, Lin Liu, Jiuyong Li, Jixue Liu, Chen Zhan, Srecko Joksimovic
Learning analytics (LA), like much of machine learning, assumes the training and test datasets come from the same distribution. Therefore, LA models built on past observations are (implicitly) expected to work well for future observations. However, this assumption does not always hold in practice because the dataset may drift. Recently, algorithmic fairness has gained significant attention. Nevertheless
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A Modular Serious Game Development Framework for Virtual Laboratory Courses IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-04 Furkan Yücel, Hasret Sultan Ünal, Elif Surer, Nejan Huvaj
Laboratory experience is an integral part of the undergraduate curriculum in most engineering courses. When physical learning is not feasible, and when the demand cannot be met through actual hands-on laboratory sessions, as has been during the COVID-19 pandemic, virtual laboratory courses can be considered as an alternative education medium. This study focuses on developing a generic modular virtual
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Virtual Reality Body Swapping to Improve Self-Assessment in Job Interview Training IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-03 Sofia Seinfeld, Filippo Gabriele Pratticó, Chiara De Giorgi, Fabrizio Lamberti
Swapping visual perspective in virtual reality (VR) provides a unique means for embodying different virtual bodies and for self-distancing. Moreover, this technology is a powerful tool for experiential learning and for simulating realistic scenarios, with broad potential in the training of soft skills. However, there is scarce knowledge on how perspective swapping in VR might benefit the training of
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Hybrid Models for Knowledge Tracing: A Systematic Literature Review IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2024-01-01 Andrea Zanellati, Daniele Di Mitri, Maurizio Gabbrielli, Olivia Levrini
Knowledge tracing is a well-known problem in AI for education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their satisfactory performances, they have some pitfalls
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Deep Knowledge Tracing Incorporating a Hypernetwork With Independent Student and Item Networks IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-25 Emiko Tsutsumi, Yiming Guo, Ryo Kinoshita, Maomi Ueno
Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student and the difficulty of each item such as IRT. Nevertheless
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Guest Editorial The Metaverse and the Future of Education IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-15 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
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Cloud-Operated Open Literate Educational Resources: The Case of the MyBinder IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-19 Alberto Corbi, Daniel Burgos, Antonio María Pérez
Literate programming and cloud-operated open literate educational resources (COOLERs) have been catching the attention of the education community in recent years. This set of learning materials mainly comprises digital notebook-like documents, which are stored, backed, and delivered from cloud services and eventually displayed in students' web browsers. As we demonstrate in this article, the advent
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Predicting Student Engagement Using Sequential Ensemble Model IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-14 Xinran Tian, Bernardo Pereira Nunes, Yifeng Liu, Ruben Manrique
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
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The Middle East Higher Education Experience: Implementing Remote Labs to Improve the Acquisition of Skills in Industry 4.0 IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-13 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
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A Systematic Review of Research on Immersive Technology-Enhanced Writing Education: The Current State and a Research Agenda IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-12 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
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Toward Embedding Robotics in Learning Environments With Support to Teachers: The IDEE Experience IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-12-06 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
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Seamless Crime Scene Reconstruction in Mixed Reality for Investigation Training: A Design and Evaluation Study IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-28 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
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OPKT: Enhancing Knowledge Tracing With Optimized Pretraining Mechanisms in Intelligent Tutoring IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-23 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
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Embedding Spatial Augmented Reality in Culinary Training: A Comparative Evaluation of sAR Kitchen and Video Tutorials IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-20 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
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Student-AI Question Cocreation for Enhancing Reading Comprehension IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-17 Ming Liu, Jingxu Zhang, Lucy Michael Nyagoga, Li Liu
Student question generation (SQG) is an effective strategy for improving reading comprehension. It helps students improve their understanding of reading materials, metacognitively monitor their comprehension, and self-correct comprehension gaps. Internet technologies have been used to facilitate SQG process through intensive peer support. However, the availability, level of task commitment, and capabilities
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Designing and Evaluating an Interactive Learning Technology to Foster Privacy Literacy IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-17 Jan Doria, Petra Grimm, Michel Hohendanner, Susanne Kuhnert
The “Privat-o-Mat” is a media ethics learning technology to promote reflection on privacy literacy. In this article, we discuss its ethical conception, design, and development and present a qualitative evaluation of the tool with a sample of secondary school students. Children, adolescents, and young adults (CAYA) today grow up in digitalized environments that threaten their privacy in various ways
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Advanced Mathematics Exercise Recommendation Based on Automatic Knowledge Extraction and Multilayer Knowledge Graph IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-16 Shi Dong, Xueyun Tao, Rui Zhong, Zhifeng Wang, Mingzhang Zuo, Jianwen Sun
Higher education is rapidly growing in the online learning landscape. However, current personalized recommendation techniques struggle with the precise extraction of complex mathematical semantics, hindering accurate perception of learners' cognitive states and relevance of recommendations. This article proposes a framework for extracting complex mathematical semantics and providing personalized exercise
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Towards Optimization of Learning Analytics Dashboards That are Customized for the Students’ Requirements IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-15 Rotem Israel-Fishelson, Dan Kohen-Vacs
Educational dashboards enable students to monitor and reflect on academic performance and administrative aspects of the learning processes. Occasionally, educational institutions integrate dashboards using the information found in their learning management systems or their students' information desks. Learning analytics offers ways to enrich these dashboards and expose students to analyzed information
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A Dual-Mode Grade Prediction Architecture for Identifying At-Risk Students IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-15 Wei Qiu, Andy W. H. Khong, S. Supraja, Wenyin Tang
Predicting student performance in an academic institution is important for detecting at-risk students and to administer early intervention strategies. In this article, we develop a new architecture that achieves grade prediction based only on grades achieved over past semesters. Our proposed architecture involves two stages—weighted loss function incorporated to the long short-term memory (LSTM) model
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Audio Instructional Design Best Practices for an Enhanced Learning Experience. A Mixed Holistic-Systematic Mapping Study IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-06 Raj Kishen Moloo
Instructional design guidelines for multimedia educational materials are widely available; however, the recent literature on instructional audio is sparse. Existing studies fall short in organizing and synthesizing various findings to provide practical and generic guidelines in the design of audio-learning content. A systematic mapping study on the characteristics of audio instructional design over
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Natural Language Processing of Student's Feedback to Instructors: A Systematic Review IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-06 Ayse Saliha Sunar, Md Saifuddin Khalid
Course developers, providers and instructors gather feedback from students to gain insights into student satisfaction, success, and difficulties in the learning process. The traditional manual analysis is time-consuming and resource-intensive, resulting in decreased insights and pedagogical impact. To address the problems, researchers use natural language processing techniques that apply the fields
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A Virtual Reality Training System for Automotive Engines Assembly and Disassembly IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-11-06 Gongjin Lan, Qiangqiang Lai, Bing Bai, Zirui Zhao, Qi Hao
Automotive engine assembly and disassembly are common and crucial programs in the automotive industry. Traditional education trains students to learn automotive engine assembly and disassembly in lecture courses and then to operate with physical engines, which are generally low effectiveness and high cost. In this article, we developed a multilayer structured virtual reality (VR) system to provide
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Educational Escape Rooms Are Effective Learning Activities Across Educational Levels and Contexts: A Meta-Analysis IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-31 Sonsoles López-Pernas
Educational escape rooms are taxing in terms of the time needed to design, create, conduct, and evaluate. Therefore, a high “return on investment” is expected regarding their potential to improve teaching and learning. Whereas many studies have been conducted to assess the impact of educational escape rooms on learning, results have been so far inconclusive. Several studies have reported positive learning
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Development of Design Principles for AR Authoring Tools for Education Based on Teacher's Perspectives IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-25 Manoela Silva, Rafael Roberto, Iulian Radu, Patricia Cavalcante, Bertrand Schneider, Veronica Teichrieb
Augmented reality (AR) can have a positive impact on students' motivation and cognitive performance in varied age levels and different contexts. However, its use is still far from widespread in education. One of the reasons mentioned in the literature is the lack of AR authoring tools that consider the educational perspective, which means they do not usually take pedagogic aspects into account. This
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Goal Ontology for Personalized Learning and Its Implementation in Child's Health Self-Management Support IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-23 Rifca Rijgersberg-Peters, Willeke van Vught, Joost Broekens, Mark A. Neerincx
Intelligent tutoring systems need a model of learning goals for the personalization of educational content, tailoring of the learning path, progress monitoring, and adaptive feedback. This article presents such a model and corresponding interaction designs for the coaches and learners (respectively, a monitor-and-control dashboard and mobile app with supportive communications trough a virtual agent)
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Contrastive Personalized Exercise Recommendation With Reinforcement Learning IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-23 Siyu Wu, Jun Wang, Wei Zhang
Personalized exercise recommendation is a challenging task in the field of artificial intelligence in education due to several problems. First, the mainstream approaches focus more on the exercises that students have not mastered, while overlooking their long-term needs during the learning process. Second, it is difficult to capture students' knowledge states caused by sparse interactions with exercises
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Measuring Cognitive Load in Virtual Reality Training via Pupillometry IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-23 Joy Yeonjoo Lee, Nynke de Jong, Jeroen Donkers, Halszka Jarodzka, Jeroen J. G. van Merriënboer
Pupillometry is known as a reliable technique to measure cognitive load in learning and performance. However, its applicability to virtual reality (VR) environments, an emerging technology for simulation-based training, has not been well-verified in educational contexts. Specifically, the VR display causes light reflexes that confound task-evoked pupillary responses (TEPRs), impairing cognitive load
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Multisource Soft Labeling and Hard Negative Sampling for Retrieval Distractor Ranking IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-19 Jiayun Wang, Wenge Rong, Jun Bai, Zhiwei Sun, Yuanxin Ouyang, Zhang Xiong
Multiple-choice questions (MCQs) are a kind of widely adopted approaches in learning assessment. Recently, the automatic generation of MCQs has become a popular research area. In this task, distractor ranking (DR) is one of the most meaningful and challenging subtasks, where the DR models learn to select high-quality distractors from numerous candidates. Currently, some DR methods adopt a two-stage
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Editorial An Update and A Reflection on TLT's Year of 2023 IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-17 Minjuan Wang
Even though it has been a while since I wrote an editorial, the entire board of IEEE Transactions on Learning Technologies (TLT) has been working nonstop, including holidays and this past summer. The advantage of having an international team and the silver lining of having time differences! There are always a group of editors who are carrying on the reviews and interacting with authors. After a year
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Unleashing ChatGPT's Power: A Case Study on Optimizing Information Retrieval in Flipped Classrooms via Prompt Engineering IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-16 Mo Wang, Minjuan Wang, Xin Xu, Lanqing Yang, Dunbo Cai, Minghao Yin
This research project investigates the impact of prompt engineering, a key aspect of chat generative pretrained transformer (ChatGPT), on college students' information retrieval in flipped classrooms. In recent years, an increasing number of students have been using AI-based tools, such as ChatGPT rather than traditional research engines to learn and to complete course assignments. Despite this growing
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Extended Reality as a Training Approach for Visual Real-Time Feedback in Golf IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-06 Mai Geisen, André Nicklas, Tobias Baumgartner, Stefanie Klatt
Visual feedback can enhance motor learning in golf; however, so far, this has mostly been time-delayed. Using extended reality (XR), learners can receive visual feedback in real time without relying on an external perspective. The aim of this study was to investigate a novel XR-based method of real-time feedback for golf putt training. Thirty-two subjects were divided between an XR group and a technique
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The Extent of AI Applications in EFL Learning and Teaching IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-05 Yousif A. Alshumaimeri, Abdulrahman K. Alshememry
Foreign language teaching, like almost all other aspects of human existence, has been substantially influenced by recent advances in modern information and communication technologies, such as augmented reality, virtual reality, and artificial intelligence (AI). Although AI has been in use for almost 30 years, educators remain skeptical toward the use of AI-technology in the education field more broadly
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Improving Peer Assessment Validity and Reliability Through a Fuzzy Coherence Measure IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-03 Mohamed El Alaoui
Classical evaluation methods, assessments, exams, and so forth accentuate the perception of one against all, professor versus learners. Including students in the assessment process, allows transforming the professor from an opponent to a critical friend, with the role of helping students to recognize both their strengths and weaknesses. However, assessing peers is not as easy as it may appear, since
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A Study on the Adoption of Virtual Reality in Industrial Design Education IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-10-02 Ahmet Hamurcu, Şebnem Timur, Kerem Rızvanoğlu
Virtual reality (VR) technology has been commercially and economically accessible to industrial designers for the past seven years, following the introduction of VR glasses and headsets, e.g., the HTC Vive and the Oculus Rift, in 2016. However, despite the growing popularity of VR implementations in education, it remains unclear to what extent industrial design (ID) students and instructors will adopt
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A Data-Driven Approach for the Identification of Features for Automated Feedback on Academic Essays IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-29 Mohsin Abbas, Peter van Rosmalen, Marco Kalz
For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological, and semantic features) can be used to provide formative feedback to the students in higher education. In this study, the goal was to identify a sufficient number of features that exhibit a fair proxy of the scores given by the human raters via a data-driven approach. Using an existing corpus
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A System to Structure, Measure, and Improve Student Development IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-28 Munir Mandviwalla, David Schuff, Laurel Miller, Manoj Chacko
In this article, we develop and evaluate a novel system and computing platform to structure, measure, and improve student development using points. We define student development broadly as the achievement of learning to do, know, live together, and be. The system leverages individual agency, social influences, content generation and sharing, institutional requirements, and gamification as development
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Collaborative Virtual Reality in Higher Education: Students' Perceptions on Presence, Challenges, Affordances, and Potential IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-27 Teemu H. Laine, Woohyun Lee
The metaverse is a network of interoperable and persistent 3-D virtual worlds where users can coexist and interact through mechanisms, such as gamification, nonfungible tokens, and cryptocurrencies. Although the metaverse is a theoretical construct today, many collaborative virtual reality (CVR) applications have emerged as potential components of the metaverse. The demand for distance learning in
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On the Supervision of Peer Assessment Tasks: An Efficient Instructor Guidance Technique IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-27 Jerónimo Hernández-González, Pedro Javier Herrera
In peer assessment, students assess a task done by their peers, provide feedback and usually a grade. The extent to which these peer grades can be used to formally grade the task is unclear, with doubts often arising regarding their validity. The instructor could supervise the peer assessments, but would not then benefit from workload reduction, one of the most appealing features of peer assessment
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Automatically Difficulty Grading Method for English Reading Corpus With Multifeature Embedding Based on a Pretrained Language Model IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-26 Yuchen Wang, Juxiang Zhou, Zijie Li, Shu Zhang, Xiaoyu Han
Graded reading is one of the important ways of English learning. How to automatically judge and grade the difficulty of the English reading corpus is of great significance for precision teaching and personalized learning. However, the current rule-based readability assessment methods have some limitations, such as low efficiency and poor accuracy. In particular, these traditional methods usually lack
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Kung Fu Metaverse: A Movement Guidance Training System IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-22 Feng Tian, Jiahui Zou, Keying Li, Yuzhi Li
There are a large number of Chinese Kung Fu enthusiasts worldwide. Offline learning has been the primary method for teaching and learning Chinese Kung Fu due to the inability of online 2-D videos to convey spatial information and provide feedback. To address the issues of the high cost of offline teaching, fixed perspectives in 2-D videos, lack of spatial path feedback, and the absence of practical
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A Journey to Identify Users' Classification Strategies to Customize Game-Based and Gamified Learning Environments IEEE Trans. Learning Technol. (IF 3.7) Pub Date : 2023-09-20 Marcela Pessoa, Márcia Lima, Fernanda Pires, Gabriel Haydar, Rafaela Melo, Luiz Rodrigues, David Oliveira, Elaine Oliveira, Leandro Galvão, Bruno Gadelha, Seiji Isotani, Isabela Gasparini, Tayana Conte
Game designers and researchers have sought to create gameful environments that consider user preferences to increase engagement and motivation. In this sense, it is essential to identify the most suitable game elements for users' profiles. Designers and researchers must choose strategies to classify users into predefined profiles and select the most appropriate game elements for each user. This activity