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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
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
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)
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
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
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
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
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)
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)
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
Gary Cheng; Gloria Shu-Mei Chwo; Wing Shui Ng – Interactive Learning Environments, 2023
Teacher feedback can be useful in helping English as Foreign Language (EFL) students revise their draft writing. Investigating how EFL students respond to various types of teacher feedback in draft revision has been regarded as an important field of study. However, such investigation is time-consuming and labour-intensive, which limits its…
Descriptors: English (Second Language), Second Language Instruction, Feedback (Response), Writing Instruction
Shang, Hui-Fang – Interactive Learning Environments, 2022
Previous studies have been done to research the effects of different electronic feedback (e-feedback) modes of helping English as a foreign language (EFL) students improve their writing. The purpose of this study was to employ online peer feedback (OPF) and automated corrective feedback (ACF) to assess EFL learners' writing performance in the…
Descriptors: Computer Mediated Communication, Peer Evaluation, Feedback (Response), Automation
Cheng, Gary – Interactive Learning Environments, 2022
This study aimed to investigate the impact of using an automated tracking system on the writing performance of English as Foreign Language (EFL) students in a 13-week academic writing course. Sixty-eight first year university students participated in the study. They received the same instruction on academic writing and were allocated to one of two…
Descriptors: Automation, Writing Skills, English (Second Language), Second Language Instruction
Brita-Paja, J. L.; Gregorio, C.; Llana, L.; Pareja, C.; Riesco, A. – Interactive Learning Environments, 2019
During the last years online education, in particular Massive Open Online Courses (MOOCs), has contributed to spread and popularize educational methodologies such as peer-review, automatic assessment, self-paced courses, self-evaluation, etc. Although these techniques can benefit face-to-face courses, most of them are not yet widely used in these…
Descriptors: Online Courses, Synchronous Communication, Undergraduate Study, Peer Evaluation
Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification
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