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Showing 1 to 15 of 26 results Save | Export
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Jing Fang; Xiong Xiao; Xiuling He; Yangyang Li; Huanhuan Yuan; Xiaomin Jiao – Interactive Learning Environments, 2024
Knowledge maps are teaching tools that can promote deeply learning and avoid knowledge loss by helping students plan learning paths. Mining potential association rules of concepts from student exercise data was a common method to construct knowledge maps automatically. While manual conditions should be set to filter the association rules future to…
Descriptors: Concept Mapping, Multivariate Analysis, Associative Learning, Learning Strategies
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Hanxiang Du; Wanli Xing; Bo Pei – Interactive Learning Environments, 2023
Participating in online communities has significant benefits to students learning in terms of students' motivation, persistence, and learning outcomes. However, maintaining and supporting online learning communities is very challenging and requires tremendous work. Automatic support is desirable in this situation. The purpose of this work is to…
Descriptors: Electronic Learning, Communities of Practice, Automation, Artificial Intelligence
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Tan, Hongye; Wang, Chong; Duan, Qinglong; Lu, Yu; Zhang, Hu; Li, Ru – Interactive Learning Environments, 2023
Automatic short answer grading (ASAG) is a challenging task that aims to predict a score for a given student response. Previous works on ASAG mainly use nonneural or neural methods. However, the former depends on handcrafted features and is limited by its inflexibility and high cost, and the latter ignores global word cooccurrence in a corpus and…
Descriptors: Automation, Grading, Computer Assisted Testing, Graphs
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Zhang, Lishan; Huang, Yuwei; Yang, Xi; Yu, Shengquan; Zhuang, Fuzhen – Interactive Learning Environments, 2022
Automatic short-answer grading has been studied for more than a decade. The technique has been used for implementing auto assessment as well as building the assessor module for intelligent tutoring systems. Many early works automatically grade mainly based on the similarity between a student answer and the reference answer to the question. This…
Descriptors: Automation, Grading, Models, Artificial Intelligence
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Thuy Thi-Nhu Ngo; Howard Hao-Jan Chen; Kyle Kuo-Wei Lai – Interactive Learning Environments, 2024
The present study performs a three-level meta-analysis to investigate the overall effectiveness of automated writing evaluation (AWE) on EFL/ESL student writing performance. 24 primary studies representing 85 between-group effect sizes and 34 studies representing 178 within-group effect sizes found from 1993 to 2021 were separately meta-analyzed.…
Descriptors: Writing Evaluation, Automation, Computer Software, English (Second Language)
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Kanwal Zahoor; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the…
Descriptors: Artificial Intelligence, Computer Oriented Programs, Courseware, Learning Processes
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Ning Ma; Yan-Ling Zhang; Chun-Ping Liu; Lei Du – Interactive Learning Environments, 2024
Online asynchronous interaction is considered a core part of online teacher training, which has an important impact on learners' learning experience and learning outcomes. How to provide immediate and effective feedback through technical support based on the learners' interactive content and enhance interactive connection has become a key issue in…
Descriptors: Foreign Countries, Teacher Education, Online Courses, Asynchronous Communication
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Soomaiya Hamid; Narmeen Zakaria Bawany – Interactive Learning Environments, 2024
E-learning is the process of sharing knowledge out of the traditional classrooms through different online tools using internet. The availability and use of these tools are not easy for every student. Many institutions gather e-learning feedback to know the problems of students to improve their systems. In e-learning systems, typically a high…
Descriptors: Feedback (Response), Electronic Learning, Automation, Classification
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Saha, Sujan Kumar; Rao C. H., Dhawaleswar – Interactive Learning Environments, 2022
Assessment plays an important role in education. Recently proposed machine learning-based systems for answer grading demand a large training data which is not available in many application areas. Creation of sufficient training data is costly and time-consuming. As a result, automatic long answer grading is still a challenge. In this paper, we…
Descriptors: Middle School Students, Grading, Artificial Intelligence, Automation
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Anita Pásztor-Kovács; Attila Pásztor; Gyöngyvér Molnár – Interactive Learning Environments, 2023
In this paper, we present an agenda for the research directions we recommend in addressing the issues of realizing and evaluating communication in CPS instruments. We outline our ideas on potential ways to improve: (1) generalizability in Human-Human assessment tools and ecological validity in Human-Agent ones; (2) flexible and convenient use of…
Descriptors: Cooperation, Problem Solving, Evaluation Methods, Teamwork
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Slavko Žitnik; Glenn Gordon Smith – Interactive Learning Environments, 2024
In the recent, and ongoing, COVID-19 pandemic, remote or online K-12 schooling became the norm. Even if the pandemic tails off somewhat, remote K-12 schooling will likely remain more frequent than it was before the pandemic. A mainstay technique of online learning, at least at the college and graduate level, has been the online discussion. Since…
Descriptors: Grade 4, Elementary School Students, Discussion, Automation
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Qi Wang; Shengquan Yu – Interactive Learning Environments, 2024
Learning resources are quite important for online learning while resource provision based on algorithms could not address learners' ubiquitous needs well. Moreover, the structure and content of resources are pre-defined which makes the "Structure" and "Content" coupled closely and could not easily adjust when learners' needs…
Descriptors: Electronic Learning, Educational Resources, Automation, Models
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Shuxin Tan; Young Woo Cho; Wensi Xu – Interactive Learning Environments, 2023
With the rapid advance in educational technology, electronic feedback (e-feedback) has found its way to EFL writing process. The aim of this study is to investigate the effects of three e-feedback modes, that is, automated written corrective feedback (AWCF), asynchronous computer-mediated communication (ACMC), and their combination on EFL…
Descriptors: Foreign Countries, English (Second Language), Second Language Learning, Feedback (Response)
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Lu-Ho Hsia; Gwo-Jen Hwang; Jan-Pan Hwang – Interactive Learning Environments, 2024
To improve students' sports skills performance, it is important to engage them in reflective practice. However, in physical classes, a teacher generally needs to face a number of students, and hence it is almost impossible to provide detailed guidance or feedback to individual students. Scholars have been trying to use Artificial Intelligence (AI)…
Descriptors: Artificial Intelligence, Technology Uses in Education, Physical Education, Feedback (Response)
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Yung-Hsiang Hu; Jo Shan Fu; Hui-Chin Yeh – Interactive Learning Environments, 2024
Artificial intelligence aims to restructure and process re-engineering education and teaching processes and accelerate the evolution of the whole education system from information to intelligence. Robotic Process Automation (RPA) robots learn by observing people at work, analyzing user processes repeatedly, and adjusting or correcting automated…
Descriptors: Intelligent Tutoring Systems, Robotics, Automation, Instructional Effectiveness
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