NotesFAQContact Us
Collection
Advanced
Search Tips
Audience
Practitioners1
Laws, Policies, & Programs
Assessments and Surveys
Massachusetts Comprehensive…1
What Works Clearinghouse Rating
Meets WWC Standards with or without Reservations1
Showing 1 to 15 of 48 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Sami Mejri; Steven Borawski – International Journal on E-Learning, 2023
This article will address predictors of success for online students. A survey questionnaire was used to gather data concerning online students' social and educational readiness levels at a four-year private university in the Midwestern United States. Of the 4,050 potential participants, 250 (6.23%) responded to the survey. Stepwise regression…
Descriptors: Academic Persistence, Success, Online Courses, Readiness
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Epp, Carrie Demmans – International Educational Data Mining Society, 2022
The need to identify student cognitive engagement in online-learning settings has increased with our use of online learning approaches because engagement plays an important role in ensuring student success in these environments. Engaged students are more likely to complete online courses successfully, but this setting makes it more difficult for…
Descriptors: Online Courses, Group Discussion, Learner Engagement, Student Participation
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Clemente Rodríguez-Sabiote; Ana T. Valerio-Peña; Roberto A. Batista-Almonte; Álvaro M. Úbeda-Sánchez – International Review of Research in Open and Distributed Learning, 2024
The global pandemic caused by the SARS-CoV-2 virus brought about a true revolution in the predominant teaching-learning processes (i.e., face-to-face environment) that had been implemented up to that point. In this regard, virtual teaching-learning environments (VTLEs) have gained unprecedented significance. The main objectives of our research…
Descriptors: Electronic Learning, College Students, Online Courses, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Pei, Bo; Xing, Wanli – Journal of Educational Computing Research, 2022
This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…
Descriptors: At Risk Students, Learning Analytics, Information Retrieval, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Zhou, Liqiu; Xue, Sijia; Li, Ruiqian – SAGE Open, 2022
While online education has been increasingly adopted in different educational systems across the world, it is still a recent phenomenon in developing countries such as China. Various factors could affect learners' adoption of technology, including their online learning. In this study, we took the Technology Acceptance Model as the theoretical…
Descriptors: Online Courses, Integrated Learning Systems, Technology Integration, Intention
Peer reviewed Peer reviewed
Direct linkDirect link
Abuhassna, Hassan; Al-Rahmi, Waleed Mugahed; Yahya, Noraffandy; Zakaria, Megat Aman Zahiri Megat; Kosnin, Azlina Bt. Mohd; Darwish, Mohamad – International Journal of Educational Technology in Higher Education, 2020
This research aims to explore and investigate potential factors influencing students' academic achievements and satisfaction with using online learning platforms. This study was constructed based on Transactional Distance Theory (TDT) and Bloom's Taxonomy Theory (BTT). This study was conducted on 243 students using online learning platforms in…
Descriptors: Electronic Learning, Models, Academic Achievement, Student Satisfaction
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Rushkin, Ilia; Chuang, Isaac; Tingley, Dustin – Journal of Learning Analytics, 2019
Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time for the student to read the question and arrive at an answer to submit. If multiple attempts are allowed, and the first answer is incorrect, it takes some time to provide a second answer. Here we study the distribution of such…
Descriptors: Online Courses, Response Style (Tests), Models, Learner Engagement
Peer reviewed Peer reviewed
Direct linkDirect link
Peijian Paul Sun – Language Teaching Research, 2025
To sustain students' continuous learning in a COVID-19 pandemic context, schools and universities have shifted traditional classroom teaching to synchronous online teaching. However, there is limited understanding of acceptance and adoption of synchronous online teaching by university teachers of English as a foreign language (EFL). This study,…
Descriptors: Language Teachers, Teacher Attitudes, Attitude Change, English (Second Language)
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Chien, Hsiang-Yu; Kwok, Oi-Man; Yeh, Yu-Chen; Sweany, Noelle Wall; Baek, Eunkyeng; McIntosh, William – Online Learning, 2020
The purpose of this study was to investigate a predictive model of online learners' learning outcomes through machine learning. To create a model, we observed students' motivation, learning tendencies, online learning-motivated attention, and supportive learning behaviors along with final test scores. A total of 225 college students who were…
Descriptors: Identification, At Risk Students, College Students, Psychological Patterns
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Lee, Jeonghyun; Soleimani, Farahnaz; Irish, India; Hosmer, John, IV; Soylu, Meryem Yilmaz; Finkelberg, Roy; Chatterjee, Saurabh – Online Learning, 2022
In this study, we work towards a strategy to measure and enhance the quality of interactions in discussion forums at scale. We present a machine learning (ML) model which identifies the phase of cognitive presence exhibited by a student's post and suggest future applications of such a model to help online students develop higher-order thinking. We…
Descriptors: Online Courses, Models, Thinking Skills, Computer Mediated Communication
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Liu, Leping; Li, Wenzhen; Scherer, Rebecca – International Journal of Technology in Teaching and Learning, 2016
Technology has been increasingly used in counseling education and professional practice in the past two decades. To ensure technology is appropriately used to produce expected quality of counseling work, design of technology integration in counseling becomes a key point. The first part of this article presents a critical literature review and…
Descriptors: Technology Uses in Education, Counselor Training, Curriculum Design, Technology Integration
Alqurashi, Emtinan – ProQuest LLC, 2017
This study aimed to explore the relationship between four predictor variables (online learning self-efficacy, learner-content interaction, learner-instructor interaction, and learner-learner interaction) and student satisfaction and perceived learning. The purpose of this study was to investigate the extent to which the four variables are…
Descriptors: Self Efficacy, Interaction, Models, Student Satisfaction
Peer reviewed Peer reviewed
Direct linkDirect link
Pursel, B. K.; Zhang, L.; Jablokow, K. W.; Choi, G. W.; Velegol, D. – Journal of Computer Assisted Learning, 2016
Massive open online courses (MOOCs) continue to appear across the higher education landscape, originating from many institutions in the USA and around the world. MOOCs typically have low completion rates, at least when compared with traditional courses, as this course delivery model is very different from traditional, fee-based models, such as…
Descriptors: Online Courses, Graduation Rate, Delivery Systems, Models
Turayev, Oybek – ProQuest LLC, 2018
Over the last two decades, educational technology (ET) integration has become an increasingly important aspect of higher education, particularly with the growth of online, distance and hybrid courses and degree programs. Furthermore, accrediting agencies such as the Higher Learning Commission (HLC) are paying close attention to online and hybrid…
Descriptors: Educational Technology, Technology Integration, Community Colleges, College Faculty
Peer reviewed Peer reviewed
Direct linkDirect link
Shelton, Brett E.; Hung, Jui-Long; Lowenthal, Patrick R. – Distance Education, 2017
Early-warning intervention for students at risk of failing their online courses is increasingly important for higher education institutions. Students who show high levels of engagement appear less likely to be at risk of failing, and how engaged a student is in their online experience can be characterized as factors contributing to their social…
Descriptors: Asynchronous Communication, Online Courses, Educational Technology, Integrated Learning Systems
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4