NotesFAQContact Us
Collection
Advanced
Search Tips
What Works Clearinghouse Rating
Showing 196 to 210 of 1,752 results Save | Export
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Zilong Pan; Lauren Biegley; Allen Taylor; Hua Zheng – Journal of Learning Analytics, 2024
The learning management system (LMS) is widely used in educational settings to support teaching and learning practices. The usage log data, generated by both learners and instructors, enables the development and implementation of learning analytics (LA) interventions aimed at facilitating teaching and learning activities. To examine the current…
Descriptors: Learning Analytics, Learning Management Systems, Intervention, Teacher Improvement
Peer reviewed Peer reviewed
Direct linkDirect link
Pankaj Chejara; Reet Kasepalu; Luis P. Prieto; María Jesús Rodríguez-Triana; Adolfo Ruiz Calleja; Bertrand Schneider – British Journal of Educational Technology, 2024
Multimodal learning analytics (MMLA) research has made significant progress in modelling collaboration quality for the purpose of understanding collaboration behaviour and building automated collaboration estimation models. Deploying these automated models in authentic classroom scenarios, however, remains a challenge. This paper presents findings…
Descriptors: Cooperation, Learning Activities, Models, Learning Modalities
Peer reviewed Peer reviewed
Direct linkDirect link
Marijn Martens; Ralf De Wolf; Lieven De Marez – Education and Information Technologies, 2024
Algorithmic systems such as Learning Analytics (LA) are driving the datafication and algorithmization of education. In this research, we focus on the appropriateness of LA systems from the perspective of parents and students in secondary education. Anchored in the contextual integrity framework (Nissenbaum, "Washington Law Review, 79,"…
Descriptors: Parent Attitudes, Student Attitudes, Learning Analytics, Algorithms
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Enes Küçük; Fidaye Cincil; Yasemin Karal – Journal of Theoretical Educational Science, 2025
AI technology, which is becoming more widespread day by day, also affects education and training processes. The use of AI tools in educational environments provides many benefits to teachers and students. However, the use of AI in education also raises some ethical concerns. The aim of this study was to reveal the ethical issues arising from the…
Descriptors: Ethics, Teaching Methods, Learning Analytics, Internet
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Neil Dixon; Rob Howe; Uwe Matthias Richter – Research in Learning Technology, 2025
Learning analytics (LA) provides insight into student performance and progress, allowing for targeted interventions and support to improve the student learning experience. Uses of LA are diverse, including measuring student engagement, retention, progression, student well-being and curriculum development. This article provides perspectives on the…
Descriptors: Learning Analytics, Educational Benefits, Case Studies, Higher Education
Peer reviewed Peer reviewed
Direct linkDirect link
Flávio Marques; Leonardo Lignani; João Quadros; Myrna Amorim; Windson Viana; Eduardo Ogasawara; Joel dos Santos – Technology, Knowledge and Learning, 2025
Educational games help reinforce educational concepts. They help students learn through hypothesizing, probing, and reflecting upon the game environment. Understanding the impact of a game is important before deploying it in a class. Recent studies in learning analysis describe methodologies and approaches for analyzing educational games. However,…
Descriptors: Design, Educational Games, Reinforcement, Game Based Learning
Peer reviewed Peer reviewed
Direct linkDirect link
Stanja, Judith; Gritz, Wolfgang; Krugel, Johannes; Hoppe, Anett; Dannemann, Sarah – British Journal of Educational Technology, 2023
Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for…
Descriptors: Formative Evaluation, Student Attitudes, Learning Analytics, Student Evaluation
Peer reviewed Peer reviewed
Direct linkDirect link
Bulut, Okan; Gorgun, Guher; Yildirim-Erbasli, Seyma N.; Wongvorachan, Tarid; Daniels, Lia M.; Gao, Yizhu; Lai, Ka Wing; Shin, Jinnie – British Journal of Educational Technology, 2023
As universities around the world have begun to use learning management systems (LMSs), more learning data have become available to gain deeper insights into students' learning processes and make data-driven decisions to improve student learning. With the availability of rich data extracted from the LMS, researchers have turned much of their…
Descriptors: Formative Evaluation, Learning Analytics, Models, Learning Management Systems
Peer reviewed Peer reviewed
Direct linkDirect link
Ley, Tobias; Tammets, Kairit; Pishtari, Gerti; Chejara, Pankaj; Kasepalu, Reet; Khalil, Mohammad; Saar, Merike; Tuvi, Iiris; Väljataga, Terje; Wasson, Barbara – Journal of Computer Assisted Learning, 2023
Background: With increased use of artificial intelligence in the classroom, there is now a need to better understand the complementarity of intelligent learning technology and teachers to produce effective instruction. Objective: The paper reviews the current research on intelligent learning technology designed to make models of student learning…
Descriptors: Artificial Intelligence, Technology Uses in Education, Learning Analytics, Instructional Effectiveness
Peer reviewed Peer reviewed
Direct linkDirect link
Han, Songhee; Liu, Min; Pan, Zilong; Cai, Ying; Shao, Peixia – International Journal of Artificial Intelligence in Education, 2023
In this study, we examined interaction behaviors between a small number of participants in two massive open online courses (MOOCs) and an FAQ chatbot, focusing on the participants' native language markers. We used a binary native language marker (non-native English user vs. native English user) to distinguish between two groups in this multiple…
Descriptors: Artificial Intelligence, MOOCs, Native Language, Computer Mediated Communication
Peer reviewed Peer reviewed
Direct linkDirect link
Liu, Zhi; Kong, Xi; Chen, Hao; Liu, Sannyuya; Yang, Zongkai – IEEE Transactions on Learning Technologies, 2023
In a massive open online courses (MOOCs) learning environment, it is essential to understand students' social knowledge constructs and critical thinking for instructors to design intervention strategies. The development of social knowledge constructs and critical thinking can be represented by cognitive presence, which is a primary component of…
Descriptors: MOOCs, Cognitive Processes, Students, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Kitto, Kirsty; Hicks, Ben; Shum, Simon Buckingham – British Journal of Educational Technology, 2023
An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results…
Descriptors: Causal Models, Learning Analytics, Educational Theories, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Jones, Kyle M. L.; Goben, Abigail; Perry, Michael R.; Regalado, Mariana; Salo, Dorothea; Asher, Andrew D.; Smale, Maura A.; Briney, Kristin A. – portal: Libraries and the Academy, 2023
Higher education data mining and analytics, like learning analytics, may improve learning experiences and outcomes. However, such practices are rife with student privacy concerns and other ethics issues. It is crucial that student privacy expectations and preferences are considered in the design of educational data analytics. This study forefronts…
Descriptors: College Students, Student Attitudes, Data Collection, Learning Analytics
Peer reviewed Peer reviewed
Direct linkDirect link
Sghir, Nabila; Adadi, Amina; Lahmer, Mohammed – Education and Information Technologies, 2023
The last few years have witnessed an upsurge in the number of studies using Machine and Deep learning models to predict vital academic outcomes based on different kinds and sources of student-related data, with the goal of improving the learning process from all perspectives. This has led to the emergence of predictive modelling as a core practice…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, Data Collection
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Anni Silvola; Amanda Sjöblom; Piia Näykki; Egle Gedrimiene; Hanni Muukkonen – Frontline Learning Research, 2023
An in-depth understanding of student experiences and evaluations of learning analytics dashboards (LADs) is needed to develop supportive learning analytics tools. This study investigates how students (N = 140) evaluated two student-facing LADs as a support for academic path-level self-regulated learning (SRL) through the concrete processes of…
Descriptors: Learning Analytics, Student Evaluation, Student Experience, Student Attitudes
Pages: 1  |  ...  |  10  |  11  |  12  |  13  |  14  |  15  |  16  |  17  |  18  |  ...  |  117