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Preet Chandan Kaur; Leena Ragha – Education and Information Technologies, 2025
Video summarization is a method of deducing the content of video content for generating a summary in video format. The generated summary should have the significant segments of raw video. Recently, the content of video has been rapidly increasing, thus automatic video summarization is beneficial for individuals who want to keep time and learn more…
Descriptors: Semantics, Video Technology, Audio Equipment, Linguistic Input
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Hui Han; Silvana Trimi – Education and Information Technologies, 2024
Cloud computing-based online education has played a vital role in enabling uninterrupted learning during crises such as the COVID-19 pandemic. This study explored the key variables associated with cloud computing that can effectively support the operation of online education platforms. By analyzing real data from 63 online learning platforms, the…
Descriptors: Computer Software, Learning Management Systems, Online Courses, Correlation
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Isabel M. Romero Albaladejo; María del Mar García López – Education and Information Technologies, 2024
The mathematical-related affect research agenda demands studies on the affect-cognition relationship, as well as interventions aimed at improving affective aspects of mathematical learning. The potential of technological environments for promoting cognitive changes in students has been widely informed and there is evidence of their influence in…
Descriptors: Geometry, Mathematics Instruction, Student Attitudes, Computer Software
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Yunus Kökver; Hüseyin Miraç Pektas; Harun Çelik – Education and Information Technologies, 2025
This study aims to determine the misconceptions of teacher candidates about the greenhouse effect concept by using Artificial Intelligence (AI) algorithm instead of human experts. The Knowledge Discovery from Data (KDD) process model was preferred in the study where the Analyse, Design, Develop, Implement, Evaluate (ADDIE) instructional design…
Descriptors: Artificial Intelligence, Misconceptions, Preservice Teachers, Natural Language Processing
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K. Keerthi Jain; J. N. V. Raghuram – Education and Information Technologies, 2024
This research delves into the multifaceted landscape of various factors that influence the adoption of Generation-Artificial Intelligence (Gen-AI) in Higher Education. By employing a comprehensive framework that includes perceived risk, perceived ease of use, usefulness, Technological Pedagogical Content Knowledge (TPACK), and trust, the study…
Descriptors: Prediction, Artificial Intelligence, Technological Literacy, Pedagogical Content Knowledge
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Yandong Zhang; Yongliang Zhang – Education and Information Technologies, 2025
In the digital age, technology-assisted language learning (TALL) has emerged as a pivotal approach to enhancing English linguistic skills among college students. This study explores the effectiveness of TALL innovations in enhancing English linguistic skills among college students. Employing a mixed-methods approach, data were collected through…
Descriptors: Technology Uses in Education, Computer Assisted Instruction, Second Language Instruction, English (Second Language)
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Parhizkar, Amirmohammad; Tejeddin, Golnaz; Khatibi, Toktam – Education and Information Technologies, 2023
Increasing productivity in educational systems is of great importance. Researchers are keen to predict the academic performance of students; this is done to enhance the overall productivity of educational system by effectively identifying students whose performance is below average. This universal concern has been combined with data science…
Descriptors: Algorithms, Grade Point Average, Interdisciplinary Approach, Prediction
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Iatrellis, Omiros; Savvas, Ilias ?.; Fitsilis, Panos; Gerogiannis, Vassilis C. – Education and Information Technologies, 2021
Learning analytics have proved promising capabilities and opportunities to many aspects of academic research and higher education studies. Data-driven insights can significantly contribute to provide solutions for curbing costs and improving education quality. This paper adopts a two-phase machine learning approach, which utilizes both…
Descriptors: Prediction, Outcomes of Education, Higher Education, Data Analysis
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Bahreini, Kiavash; Nadolski, Rob; Westera, Wim – Education and Information Technologies, 2016
This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…
Descriptors: Affective Behavior, Emotional Response, Electronic Learning, Recognition (Psychology)