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Nan Xie; Zhengxu Li; Haipeng Lu; Wei Pang; Jiayin Song; Beier Lu – IEEE Transactions on Learning Technologies, 2025
Classroom engagement is a critical factor for evaluating students' learning outcomes and teachers' instructional strategies. Traditional methods for detecting classroom engagement, such as coding and questionnaires, are often limited by delays, subjectivity, and external interference. While some neural network models have been proposed to detect…
Descriptors: Learner Engagement, Artificial Intelligence, Technology Uses in Education, Educational Technology
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Israel Ulises Cayetano-Jimenez; Erick Axel Martinez-Rios; Rogelio Bustamante-Bello; Ricardo A. Ramirez-Mendoza; Maria Soledad Ramirez-Montoya – IEEE Transactions on Learning Technologies, 2024
Educational robotics (ER) is a discipline of applied robotics focused on teaching robot design, analysis, application, and operation. Traditionally, ER has favored rigid robots, overlooking the potential of soft robots (SRs). While rigid robots offer insights into dynamics, kinematics, and control, they have limitations in exploring the depths of…
Descriptors: Robotics, Educational Technology, Teaching Methods, Learning Activities
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Putnikovic, Marko; Jovanovic, Jelena – IEEE Transactions on Learning Technologies, 2023
Automatic grading of short answers is an important task in computer-assisted assessment (CAA). Recently, embeddings, as semantic-rich textual representations, have been increasingly used to represent short answers and predict the grade. Despite the recent trend of applying embeddings in automatic short answer grading (ASAG), there are no…
Descriptors: Automation, Computer Assisted Testing, Grading, Natural Language Processing
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Chih-Hsuan Chen; Chia-Ru Chung; Hsuan-Yu Yang; Shih-Ching Yeh; Eric Hsiao-Kuang Wu; Hsin-Jung Ting – IEEE Transactions on Learning Technologies, 2024
Possible symptoms of intellectual disability (ID) include delayed physical development that becomes more pronounced as the disability progresses, delayed development of gross and fine motor skills, sensory perception problems, and difficulty grasping the integrity of objects. Although there is no cure or reversal, research has shown that extensive…
Descriptors: Intellectual Disability, Disability Identification, Simulated Environment, Computer Simulation
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Mandviwalla, Munir; Schuff, David; Miller, Laurel; Chacko, Manoj – IEEE Transactions on Learning Technologies, 2023
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,…
Descriptors: Student Development, Academic Achievement, Systems Approach, Design
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Darvishi, Ali; Khosravi, Hassan; Rahimi, Afshin; Sadiq, Shazia; Gasevic, Dragan – IEEE Transactions on Learning Technologies, 2023
Engaging students in creating learning resources has demonstrated pedagogical benefits. However, to effectively utilize a repository of student-generated content (SGC), a selection process is needed to separate high- from low-quality resources as some of the resources created by students can be ineffective, inappropriate, or incorrect. A common…
Descriptors: Student Developed Materials, Educational Assessment, Peer Evaluation, Evaluation Methods
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Kim, Taehyun; Planey, James; Lindgren, Robb – IEEE Transactions on Learning Technologies, 2023
The metaverse entails modes of interactivity that hold enormous potential for education in various contexts and domains. The metaverse represents a convergence of technologies, such as immersive virtual reality (VR) and augmented reality, that allow for multimodal engagements with digital objects, virtual environments, and people. In this article,…
Descriptors: Educational Theories, Computer Simulation, Technology Uses in Education, Educational Environment
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Lishan Zhang; Linyu Deng; Sixv Zhang; Ling Chen – IEEE Transactions on Learning Technologies, 2024
With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically…
Descriptors: Automation, Classification, Artificial Intelligence, Tutoring
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Thomas, Chinchu; Jayagopi, Dinesh Babu – IEEE Transactions on Learning Technologies, 2022
Effective presentation skills are an important ability for students and professionals to possess. Automatic analysis of presentation skills can help provide feedback to a speaker, and a complete analysis is possible only with both speaker and audience measurement. In this article, we propose a methodology to predict presentation skills on a small…
Descriptors: Public Speaking, Prediction, Automation, Video Technology
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Gerardo Ibarra-Vazquez; Maria Soledad Ramirez-Montoya; Mariana Buenestado-Fernandez – IEEE Transactions on Learning Technologies, 2024
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…
Descriptors: Gender Differences, Open Education, Cross Cultural Studies, Student Attitudes
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Qiyun Wang; Qian Huang – IEEE Transactions on Learning Technologies, 2024
Blended synchronous learning enables online learners to participate in class activities from geographically separated sites. Due to various challenges, however, online learners are often harder to be engaged and their engagement levels are lower than that of classroom counterparts. This review summarized and synthesized the challenges that led to…
Descriptors: Journal Articles, Blended Learning, Synchronous Communication, Barriers
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Mao, Shun; Zhan, Jieyu; Wang, Yizhao; Jiang, Yuncheng – IEEE Transactions on Learning Technologies, 2023
For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict…
Descriptors: Intelligent Tutoring Systems, Learning Processes, Prediction, Prior Learning
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Ricardo Conejo Muñoz; Beatriz Barros Blanco; José del Campo-Ávila; José L. Triviño Rodriguez – IEEE Transactions on Learning Technologies, 2024
Automatic question generation and the assessment of procedural knowledge is still a challenging research topic. This article focuses on the case of it, the techniques of parsing grammars for compiler construction. There are two well-known techniques for parsing: top-down parsing with LL(1) and bottom-up with LR(1). Learning these techniques and…
Descriptors: Automation, Questioning Techniques, Knowledge Level, Language
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Gomez, Manuel J.; Ruipérez-Valiente, Jose A.; Clemente, Félix J. Garcia – IEEE Transactions on Learning Technologies, 2023
Technology has become an essential part of our everyday life, and its use in educational environments keeps growing. In addition, games are one of the most popular activities across cultures and ages, and there is ample evidence that supports the benefits of using games for assessment. This field is commonly known as game-based assessment (GBA),…
Descriptors: Literature Reviews, Game Based Learning, Evaluation Methods, Elementary Secondary Education
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Divasón, Jose; Martinez-de-Pison, Francisco Javier; Romero, Ana; Saenz-de-Cabezon, Eduardo – IEEE Transactions on Learning Technologies, 2023
The evaluation of student projects is a difficult task, especially when they involve both a technical and a creative component. We propose an artificial intelligence (AI)-based methodology to help in the evaluation of complex projects in engineering and computer science courses. This methodology is intended to evaluate the assessment process…
Descriptors: Student Projects, Student Evaluation, Artificial Intelligence, Models
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