NotesFAQContact Us
Collection
Advanced
Search Tips
Laws, Policies, & Programs
What Works Clearinghouse Rating
Showing 1 to 15 of 53 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Ujjwal Biswas; Samit Bhattacharya – Education and Information Technologies, 2024
The application of machine learning (ML) has grown and is now used to enhance learning outcomes. In blended classroom settings, ML, emerging smartphones and wearable technologies are commonly used to improve teaching and learning. The combination of these advanced technologies and ML plays a crucial role in enhancing real-time feedback quality.…
Descriptors: Artificial Intelligence, Blended Learning, Flipped Classroom, Technology Uses in Education
Peer reviewed Peer reviewed
Direct linkDirect link
Jian-Wei Tzeng; Nen-Fu Huang; Yi-Hsien Chen; Ting-Wei Huang; Yu-Sheng Su – Educational Technology & Society, 2024
Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and…
Descriptors: MOOCs, Artificial Intelligence, Electronic Learning, Student Participation
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Karasavvidis, Ilias; Papadimas, Charalampos; Ragazou, Vasiliki – Themes in eLearning, 2022
The digital trails that students leave behind on e-learning environments have attracted considerable attention in the past decade. Typically, some of these traces involve the production of different kinds of texts. While students routinely produce a bulk of texts in online learning settings, the potential of such linguistic features has not been…
Descriptors: Video Technology, Electronic Learning, Prediction, Academic Achievement
Peer reviewed Peer reviewed
Direct linkDirect link
Sprenger, David A.; Schwaninger, Adrian – British Journal of Educational Technology, 2023
The technology acceptance model (TAM) uses perceived usefulness and perceived ease of use to predict the intention to use a technology which is important when deciding to invest in a technology. Its extension for e-learning (the general extended technology acceptance model for e-learning; GETAMEL) adds subjective norm to predict the intention to…
Descriptors: Video Technology, Demonstrations (Educational), Prediction, Intention
Peer reviewed Peer reviewed
Direct linkDirect link
Huang, Tao; Hu, Shengze; Yang, Huali; Geng, Jing; Liu, Sannyuya; Zhang, Hao; Yang, Zongkai – IEEE Transactions on Learning Technologies, 2023
The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which…
Descriptors: Educational Technology, Prediction, Electronic Learning, Intelligent Tutoring Systems
Peer reviewed Peer reviewed
Direct linkDirect link
Shiyi Liu; Juan Zheng; Tingting Wang; Zeda Xu; Jie Chao; Shiyan Jiang – AERA Online Paper Repository, 2024
This study introduces a novel approach for predicting student engagement levels in a language-based AI curriculum. The curriculum was integrated into English Language Arts classrooms, in which 106 students from five classes participated five web-based machine learning and text mining modules for 2 weeks. Sentiment and categorical analyses,…
Descriptors: Learner Engagement, Artificial Intelligence, Technology Uses in Education, Language Arts
Peer reviewed Peer reviewed
Direct linkDirect link
Rezaei, Mohammadsadegh; Bobarshad, Hossein; Badie, Kambiz – Interactive Learning Environments, 2021
The development of information technology and social networks has created new opportunities to access lifelong learning in the form of informal learning. In an informal learning environment, learning takes place via Communities of Practice (CoP). The learning success factors in online CoPs are learners' similarity in learning interests and…
Descriptors: Prediction, Electronic Learning, Communities of Practice, Information Technology
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Nathalie Rzepka; Linda Fernsel; Hans-Georg Müller; Katharina Simbeck; Niels Pinkwart – Computer-Based Learning in Context, 2023
Algorithms and machine learning models are being used more frequently in educational settings, but there are concerns that they may discriminate against certain groups. While there is some research on algorithmic fairness, there are two main issues with the current research. Firstly, it often focuses on gender and race and ignores other groups.…
Descriptors: Algorithms, Artificial Intelligence, Models, Bias
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Bulathwela, Sahan; Verma, Meghana; Pérez-Ortiz, María; Yilmaz, Emine; Shawe-Taylor, John – International Educational Data Mining Society, 2022
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces: (1) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to…
Descriptors: Video Technology, Lecture Method, Data Analysis, Prediction
Peer reviewed Peer reviewed
Direct linkDirect link
Xu, Xiaoqiu; Dugdale, Deborah M.; Wei, Xin; Mi, Wenjuan – American Journal of Distance Education, 2023
The recent surge of online language learning services in the past decade has benefitted second language learners. However, there is a lack of understanding of whether learners, especially young learners, are engaged in online learning, and how educators can enhance the engagement of the online learning experience. This study examines an artificial…
Descriptors: Artificial Intelligence, Prediction, Electronic Learning, Learner Engagement
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Al-Azawei, Ahmed; Abdullah, Alharith A.; Mohammed, Mahmood K.; Abod, Zaid A. – International Review of Research in Open and Distributed Learning, 2023
Although online learning has become ubiquitous worldwide, earlier research has neglected the relationship between its actual use and security concerns. Learners' lack of security awareness while using learning technologies remains rarely studied. This paper integrates Delone and McLean's information system success (D&M-ISS) model with the…
Descriptors: Foreign Countries, Online Courses, Electronic Learning, Success
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Sales, Adam C.; Prihar, Ethan B.; Gagnon-Bartsch, Johann A.; Heffernan, Neil T. – Journal of Educational Data Mining, 2023
Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered,…
Descriptors: Data Use, Research Methodology, Randomized Controlled Trials, Educational Technology
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Khor, Ean Teng; Dave, Darshan – International Review of Research in Open and Distributed Learning, 2022
The COVID-19 pandemic induced a digital transformation of education and inspired both instructors and learners to adopt and leverage technology for learning. This led to online learning becoming an important component of the new normal, with home-based virtual learning an essential aspect for learners on various levels. This, in turn, has caused…
Descriptors: Learning Analytics, Social Networks, Network Analysis, Classification
Peer reviewed Peer reviewed
Direct linkDirect link
Alshurideh, Muhammad; Al Kurdi, Barween; Salloum, Said A.; Arpaci, Ibrahim; Al-Emran, Mostafa – Interactive Learning Environments, 2023
Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in…
Descriptors: Electronic Learning, Prediction, Least Squares Statistics, Structural Equation Models
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Xu, Yufan; Taniguchi, Yuta; Goda, Yoshiko; Shimada, Atsushi; Yamada, Masanori – International Association for Development of the Information Society, 2020
This study examined the factors of learning behaviors in digital-learning-material reader that predict university students' productive participation identified by social presence indicators in online collaborative learning. Data were collected from 76 first-year university students, including learning behaviors in digital learning-material…
Descriptors: Student Behavior, Student Participation, Prediction, Cooperative Learning
Previous Page | Next Page »
Pages: 1  |  2  |  3  |  4