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Chatti, Mohamed Amine; Muslim, Arham – International Review of Research in Open and Distributed Learning, 2019
Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing…
Descriptors: Guidelines, Data Analysis, Learning Experience, Metacognition
Beemer, Joshua; Spoon, Kelly; Fan, Juanjuan; Stronach, Jeanne; Frazee, James P.; Bohonak, Andrew J.; Levine, Richard A. – Journal of Statistics Education, 2018
Estimating the efficacy of different instructional modalities, techniques, and interventions is challenging because teaching style covaries with instructor, and the typical student only takes a course once. We introduce the individualized treatment effect (ITE) from analyses of personalized medicine as a means to quantify individual student…
Descriptors: Learning Modalities, Academic Achievement, Intervention, Educational Research
Gelan, Anouk; Fastré, Greet; Verjans, Martine; Martin, Niels; Janssenswillen, Gert; Creemers, Mathijs; Lieben, Jonas; Depaire, Benoît; Thomas, Michael – Computer Assisted Language Learning, 2018
Learning analytics (LA) has emerged as a field that offers promising new ways to prevent drop-out and aid retention. However, other research suggests that large datasets of learner activity can be used to understand online learning behaviour and improve pedagogy. While the use of LA in language learning has received little attention to date,…
Descriptors: Data Collection, Data Analysis, Computer Assisted Instruction, Second Language Instruction
Bendjebar, Safia; Lafifi, Yacine; Zedadra, Amina – International Journal of Distance Education Technologies, 2016
In e-learning systems, tutors have a significant impact on learners' life to increase their knowledge level and to make the learning process more effective. They are characterized by different features. Therefore, identifying tutoring styles is a critical step in understanding the preference of tutors on how to organize and help the learners. In…
Descriptors: Tutors, Tutoring, Tutor Training, Tutorial Programs
Carver, Lin B.; Mukherjee, Keya; Lucio, Robert – Online Learning, 2017
Online education is rapidly becoming a significant method of course delivery in higher education. Consequently, instructors analyze student performance in an attempt to better scaffold student learning. Learning analytics can provide insight into online students' course behaviors. Archival data from 167 graduate level education students enrolled…
Descriptors: Graduate Students, Correlation, Grades (Scholastic), Time on Task
Strang, Kenneth David – Journal of Educational Technology Systems, 2016
This article starts with a detailed literature review of recent studies that focused on using learning analytics software or learning management system data to determine the nature of any relationships between online student activity and their academic outcomes within university-level business courses. The article then describes how data was…
Descriptors: Outcomes of Education, Higher Education, Computer Software, Academic Achievement
Zhang, Ke – International Journal on E-Learning, 2015
This article starts with an overview on China's MOOC phenomenon and social media, and then reports a comparative, multiple case study on three selected MOOC communities that have emerged on social media in China. These representative MOOC communities included: (a) MOOC Academy, the largest MOOC community in China, (b) Zhejiang University of…
Descriptors: Foreign Countries, Data Collection, Online Courses, Social Networks
Frydenberg, Jia – International Review of Research in Open and Distance Learning, 2007
This study presents persistence and attrition data from two years of data collection. Over the eight quarters studied, the persistence rate in online courses was 79 percent. The persistence rate for similar onground courses was 84 percent. The drops for both course modalities were disaggregated by the "time" of the request for withdrawal: before…
Descriptors: Academic Persistence, Online Courses, Continuing Education, Dropout Rate
US Department of Education, 2008
This guide is designed as a resource for leaders and evaluators of K-12 online learning programs. In this guide, the term "online learning" is used to refer to a range of education programs and resources in the K-12 arena, including distance learning courses offered by universities, private providers, or teachers at other schools;…
Descriptors: Elementary Secondary Education, Distance Education, Online Courses, Web Sites
Bishop, M. J.; White, Sally A. – Online Submission, 2005
Lehigh University's Clipper Project collected data on the short- and long-term effects of offering five Web-based, college-level introductory courses to early-decision, non-matriculated high school seniors for 1) the students who participated; 2) the faculty who developed the courses and taught them online; and 3) the institution that offered…
Descriptors: Research and Development, High Schools, Models, Introductory Courses
Zang, Wei; Lin, Fuzong – International Journal of Distance Education Technologies, 2006
Student behavior analysis is an active research topic in distance education in recent years. In this article, we propose a new method called Boosting to investigate students' behaviors. The Boosting Algorithm can be treated as a data mining method, trying to infer from a large amount of training data the essential factors and their relations that…
Descriptors: Student Behavior, Distance Education, Data Collection, Data Analysis
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries
Stamper, John, Ed.; Pardos, Zachary, Ed.; Mavrikis, Manolis, Ed.; McLaren, Bruce M., Ed. – International Educational Data Mining Society, 2014
The 7th International Conference on Education Data Mining held on July 4th-7th, 2014, at the Institute of Education, London, UK is the leading international forum for high-quality research that mines large data sets in order to answer educational research questions that shed light on the learning process. These data sets may come from the traces…
Descriptors: Information Retrieval, Data Processing, Data Analysis, Data Collection
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers