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Yavuz Akbulut; Onur Dönmez; Beril Ceylan; Tayfun Firat – Journal of Computing in Higher Education, 2025
Providing pre-training on new material can simplify complex content for learners who may need guidance to understand basic facts and organize their efforts. However, the effect of pre-training on learning outcomes is controversial because it tends to vary by context. Our aim was to investigate the effectiveness of pre-training in reducing…
Descriptors: Training, Cognitive Processes, Difficulty Level, Academic Achievement
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Smolinski, Pawel Robert; Szostakowski, Marcin; Winiarski, Jacek – Electronic Journal of e-Learning, 2023
The COVID-19 pandemic has caused an increase in the use of e-learning software. From the perspective of the decision-makers (school/university administration), it is crucial to understand what characteristics of the software are perceived by the users (teachers) as necessary for a task (e-learning). A popular method of determining these…
Descriptors: Electronic Learning, Computer Software, Use Studies, Teacher Behavior
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Bright Samohembo; Som Pal Baliyan – Journal of Education and e-Learning Research, 2024
This quantitative study identified challenges undergraduates faced in Botswana and predicted their readiness for online learning during COVID-19. A descriptive and correlational survey research design was adopted using the Technology Acceptance Model (TAM). A questionnaire was constructed for data collection from a randomly sampled 75 agriculture…
Descriptors: Foreign Countries, Undergraduate Students, Predictor Variables, Readiness
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Tiana P. Johnson-Clements; Guy J. Curtis; Joseph Clare – Journal of Academic Ethics, 2025
Concerns over students engaging in various forms of academic misconduct persist, especially with the post-COVID-19 rise in online learning and assessment. Research has demonstrated a clear role of the personality trait psychopathy in cheating, yet little is known about why this relationship exists. Building on the research by Curtis et al.…
Descriptors: Pandemics, COVID-19, Cheating, Electronic Learning
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Linda J. Sax; Kaitlyn N. Stormes; Maxx F. Pereyra – ACM Transactions on Computing Education, 2025
To cultivate more computing talent (including more diverse talent), it is important to understand how college students experience their computing courses and if such experiences vary based on students' gender and racial/ethnic identities. In this paper, we focus on course modality to understand whether taking courses in-person, online, or a hybrid…
Descriptors: Computer Science Education, Electronic Learning, Online Courses, Delivery Systems
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Abdessamad Chanaa; Nour-eddine El Faddouli – Journal of Education and Learning (EduLearn), 2024
Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners' cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners' cognitive levels during the online learning…
Descriptors: Electronic Learning, Evaluation Methods, Artificial Intelligence, Taxonomy
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Obeng, Asare Yaw – Cogent Education, 2023
The learning processes have been significantly impacted by technology. Numerous learners have adopted technology-based learning systems as the preferred form of learning. It is then necessary to identify the learning styles of learners to deliver appropriate resources, engage them, increase their motivation, and enhance their satisfaction and…
Descriptors: Predictor Variables, Cognitive Style, Electronic Learning, College Freshmen
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Duggal, Sanya – Asian Association of Open Universities Journal, 2022
Purpose: This study aims to identify the most significant factors that influence acceptance of e-learning in India. As e-learning has gained popularity in the wake of the COVID-19 pandemic and continues to be one of the most sustainable methods of education, it is pertinent to examine learners' perception towards its acceptance. There is limited…
Descriptors: Electronic Learning, Foreign Countries, Student Attitudes, Adult Students
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Schwarzenberg, Pablo; Navon, Jaime; Pérez-Sanagustín, Mar – Journal of Computing in Higher Education, 2020
The flipped classroom gives students the flexibility to organize their learning, while teachers can monitor their progress analyzing their online activity. In massive courses where there are a variety of activities, automated analysis techniques are required in order to process the large volume of information that is generated, to help teachers…
Descriptors: Models, Blended Learning, Teaching Methods, Electronic Learning
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Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
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Lucia Uguina-Gadella; Iria Estevez-Ayres; Jesus Arias Fisteus; Carlos Alario-Hoyos; Carlos Delgado Kloos – IEEE Transactions on Learning Technologies, 2024
Students learn not only directly from their teachers and books, but also by using their computers, tablets, and phones. Monitoring these learning environments creates new opportunities for teachers to track students' progress. In particular, this article is based on gathering real-time events as students interact with learning tools and materials…
Descriptors: Predictor Variables, Academic Achievement, Computer Assisted Instruction, Electronic Learning
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Ibili, Emin – International Online Journal of Education and Teaching, 2020
In this study, the e-readiness levels of university students studying in the field of health sciences were examined in terms of different variables. In this context, whether the level of e-readiness differs according to gender, department, class level, type of education, device ownership, working status and economic level has been examined. In…
Descriptors: Health Education, College Students, Learning Readiness, Electronic Learning
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Katsarou, Eirene – Journal of Education and e-Learning Research, 2021
Low computer anxiety (CA) and high computer self-efficacy (CSE) levels are important affective factors that promote students' academic success in the current digital era. In an effort to understand their role in successful and effective participation in online learning environments for language learning purposes, the study investigated their…
Descriptors: Anxiety, Computer Attitudes, Computer Literacy, Second Language Learning
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Villena-Taranilla, Rafael; Cózar-Gutiérrez, Ramón; González-Calero, José Antonio; Diago, Pascual D. – Technology, Pedagogy and Education, 2023
Virtual Reality (VR) is an emerging technology with an increasing number of studies assessing its benefits in educational settings. However, there is a shortage of empirical studies aimed at evaluating the acceptance of this technology in primary education. The authors propose an extended version of the Technological Acceptance Model to examine…
Descriptors: Educational Technology, Computer Simulation, Electronic Learning, Elementary School Students
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Dommett, Eleanor J. – Education and Information Technologies, 2019
The aim of this study was to better understand how students use online forums and Twitter in undergraduate learning. Students completed an anonymous online survey (N = 50, 54% completion rate) to assess their general approach to these tools, the types of interaction experienced and specific uses. Students were also asked to relate their use to…
Descriptors: Undergraduate Students, Social Media, Computer Mediated Communication, Higher Education
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