Publication Date
In 2025 | 81 |
Since 2024 | 391 |
Since 2021 (last 5 years) | 1307 |
Since 2016 (last 10 years) | 1712 |
Since 2006 (last 20 years) | 1732 |
Descriptor
Source
Author
Gaševic, Dragan | 24 |
Ogata, Hiroaki | 15 |
Baker, Ryan S. | 14 |
Dragan Gaševic | 14 |
Pardo, Abelardo | 13 |
Ouyang, Fan | 12 |
Prinsloo, Paul | 12 |
Rienties, Bart | 12 |
Roberto Martinez-Maldonado | 12 |
Saqr, Mohammed | 12 |
Hershkovitz, Arnon | 11 |
More ▼ |
Publication Type
Education Level
Location
Australia | 73 |
China | 56 |
United Kingdom | 37 |
Spain | 25 |
Turkey | 23 |
Japan | 21 |
Europe | 19 |
Taiwan | 18 |
United States | 18 |
Finland | 17 |
Netherlands | 16 |
More ▼ |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Bertrand Schneider; Joseph Reilly; Iulian Radu – Journal for STEM Education Research, 2020
In an increasingly data-driven world, large volumes of fine-grained data are infiltrating all aspects of our lives. The world of education is no exception to this phenomenon: in classrooms, we are witnessing an increasing amount of information being collected on learners and teachers. Because educational practitioners have so much contextual and…
Descriptors: Learning Analytics, Classroom Techniques, Multimedia Materials, Graduate Students
Yasmin – Asian Journal of Distance Education, 2019
Knowing insights as to why learners from diverse social and demographic profile choose to enroll in distance education can be a useful tool for Open and Distance Learning (ODL) Institutions to understand the requirements of their target segment, help in fine-tuning service offerings for attracting potential students and finally retaining them…
Descriptors: Learning Analytics, Enrollment Influences, Open Universities, Distance Education
Palucki Blake, Laura; Wynn, T. Colleen – New Directions for Institutional Research, 2019
Contemporary students have a varied set of needs--the "lifecycle" of a typical student may no longer be 4 years of continuous enrollment between the ages of 18 and 22, and many students bring rich and varied experiences with them to college. As institutions strive to allocate resources in ways that provide the most benefit to student…
Descriptors: Academic Achievement, Small Colleges, College Students, Institutional Research
Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
Dollinger, Mollie; Lodge, Jason – Educational Media International, 2019
The growing practice of students as partners (SaP) has sparked numerous conversations in higher education about the roles students do and should play in shaping the future. SaP scholars contend that by engaging with students in meaningful partnership, underpinned by values such reciprocity, students can have deeper and more meaningful learning…
Descriptors: Learning Analytics, Partnerships in Education, Student Role, Teacher Student Relationship
Luckin, Rosemary; Cukurova, Mutlu – British Journal of Educational Technology, 2019
Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for…
Descriptors: Instructional Design, Educational Technology, Artificial Intelligence, Mathematics
Rosé, Carolyn P.; McLaughlin, Elizabeth A.; Liu, Ran; Koedinger, Kenneth R. – British Journal of Educational Technology, 2019
Using data to understand learning and improve education has great promise. However, the promise will not be achieved simply by AI and Machine Learning researchers developing innovative models that more accurately predict labeled data. As AI advances, modeling techniques and the models they produce are getting increasingly complex, often involving…
Descriptors: Discovery Learning, Man Machine Systems, Artificial Intelligence, Models
Burstein, Jill; McCaffrey, Daniel; Beigman Klebanov, Beata; Ling, Guangming; Holtzman, Steven – Grantee Submission, 2019
Writing is a challenge and a potential obstacle for students in U.S. 4-year postsecondary institutions lacking prerequisite writing skills. This study aims to address the research question: Is there a relationship between specific features (analytics) in coursework writing and broader success predictors? Knowledge about this relationship could…
Descriptors: Undergraduate Students, Writing (Composition), Writing Evaluation, Learning Analytics
Pigeau, Antoine; Aubert, Olivier; Prié, Yannick – International Educational Data Mining Society, 2019
Success prediction in Massive Open Online Courses (MOOCs) is now tackled in numerous works, but still needs new case studies to compare the solutions proposed. We study here a specific dataset from a French MOOC provided by the OpenClassrooms company, featuring 12 courses. We exploit various features present in the literature and test several…
Descriptors: Success, Large Group Instruction, Online Courses, Prediction
Harrison, Scott; Villano, Renato; Lynch, Grace; Chen, George – Journal of Learning Analytics, 2021
Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for…
Descriptors: Learning Analytics, School Holding Power, Integrated Learning Systems, Microeconomics
Mahroeian, Hamid; Daniel, Ben – International Journal of Artificial Intelligence in Education, 2021
Interest in the use of analytics to support evidence-based decision-making in higher education is relatively a new phenomenon. The available research suggests that analytics can enhance an institution's ability to make evidence-based informed decisions that foster growth and increased productivity. The present study explored how institutions of…
Descriptors: Foreign Countries, Higher Education, Decision Making, Learning Analytics
Guzsvinecz, Tibor; Szucs, Judit – Education Sciences, 2021
Face-to-face education has changed to blended or distance teaching due to the COVID-19 pandemic. Since education took a digital format, it can be investigated when course materials are accessed relative to online exams: are they opened before exams or during them? Therefore, four subjects were chosen for investigation at the University of…
Descriptors: Learning Analytics, Instructional Materials, Computer Assisted Testing, Electronic Learning
Kokoç, Mehmet; Kara, Mehmet – Educational Technology & Society, 2021
The purposes of the two studies reported in this research are to adapt and validate the instrument of the Evaluation Framework for Learning Analytics (EFLA) for learners into the Turkish context, and to examine how metacognitive and behavioral factors predict learner performance. Study 1 was conducted with 83 online learners enrolled in a 16-week…
Descriptors: Learning Analytics, Electronic Learning, Measures (Individuals), Test Validity
Shabbir, Shahzad; Ayub, Muhammad Adnan; Khan, Farman Ali; Davis, Jeffrey – Interactive Technology and Smart Education, 2021
Purpose: Short-term motivation encompasses specific, challenging and attainable goals that develop in the limited timespan. On the other hand, long-term motivation indicates a sort of continuing commitment that is required to complete assigned task. As short-term motivational problems span for a limited period of time, such as a session,…
Descriptors: Learning Motivation, Electronic Learning, Time Factors (Learning), Learning Processes
Bosch, Nigel – Journal of Educational Data Mining, 2021
Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…
Descriptors: Accuracy, Learning Analytics, Models, National Competency Tests