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De Silva, Liyanachchi Mahesha Harshani; Chounta, Irene-Angelica; Rodríguez-Triana, María Jesús; Roa, Eric Roldan; Gramberg, Anna; Valk, Aune – Journal of Learning Analytics, 2022
Although the number of students in higher education institutions (HEIs) has increased over the past two decades, it is far from assured that all students will gain an academic degree. To that end, institutional analytics (IA) can offer insights to support strategic planning with the aim of reducing dropout and therefore of minimizing its negative…
Descriptors: College Students, Dropouts, Dropout Prevention, Data Analysis
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Cannistrà, Marta; Masci, Chiara; Ieva, Francesca; Agasisti, Tommaso; Paganoni, Anna Maria – Studies in Higher Education, 2022
This paper combines a theoretical-based model with a data-driven approach to develop an Early Warning System that detects students who are more likely to dropout. The model uses innovative multilevel statistical and machine learning methods. The paper demonstrates the validity of the approach by applying it to administrative data from a leading…
Descriptors: Dropouts, Potential Dropouts, Dropout Prevention, Dropout Characteristics
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Abdulkadir Palanci; Rabia Meryem Yilmaz; Zeynep Turan – Education and Information Technologies, 2024
This study aims to reveal the main trends and findings of the studies examining the use of learning analytics in distance education. For this purpose, journal articles indexed in the SSCI index in the Web of Science database were reviewed, and a total of 400 journal articles were analysed within the scope of this study. The systematic review…
Descriptors: Learning Analytics, Distance Education, Educational Trends, Periodicals
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Mao, Ye; Zhi, Rui; Khoshnevisan, Farzaneh; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2019
Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this…
Descriptors: Difficulty Level, Learning Activities, Prediction, Programming
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Cohen, Anat – Educational Technology Research and Development, 2017
Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student…
Descriptors: Academic Persistence, Predictor Variables, Dropouts, At Risk Students
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Atapattu, Thushari; Falkner, Katrina – Journal of Learning Analytics, 2018
Lecture videos are amongst the most widely used instructional methods within present Massive Open Online Courses (MOOCs) and other digital educational platforms. As the main form of instruction, student engagement behaviour, including interaction with videos, directly impacts the student success or failure and accordingly, in-video dropouts…
Descriptors: Lecture Method, Video Technology, Online Courses, Mass Instruction
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Ye, Cheng; Biswas, Gautam – Journal of Learning Analytics, 2014
Our project is motivated by the early dropout and low completion rate problem in MOOCs. We have extended traditional features for MOOC analysis with richer and higher granularity information to make more accurate predictions of dropout and performance. The results show that finer-grained temporal information increases the predictive power in the…
Descriptors: Large Group Instruction, Online Courses, Educational Technology, Technology Uses in Education
Jones, LaTonya S. – ProQuest LLC, 2011
Compared to other races, the college achievement gap is largest between Black men and women where females earn twice as many degrees as their male counterparts (National Center for Education Statistics, 2010, Status and trends in the education of racial and ethnic minorities). Many Black men attempt college and eventually drop out forfeiting their…
Descriptors: Achievement Gap, Colleges, Academic Degrees, Phenomenology
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Chen, Rong; DesJardins, Stephen L. – Journal of Higher Education, 2010
This study focuses on the differences in college student dropout behavior among racial/ethnic groups. We employ event history methods and data from the Beginning Postsecondary Students (BPS) and National Postsecondary Student Aid Study (NPSAS) surveys to investigate how financial aid may differentially influence dropout risks among these student…
Descriptors: Access to Education, Higher Education, College Students, At Risk Students
Carson, Cristi, Ed. – Online Submission, 2012
The NEAIR (North East Association for Institutional Research) 2012 Conference Proceedings is a compilation of papers presented at the Bethesda, Maryland conference. Papers in this document include: (1) Can a Marketing Campaign Increase Response Rates to Online Course Evaluations? (Kimberly Puhala); (2) Developing Community College Peer…
Descriptors: Institutional Research, Marketing, Course Evaluation, Response Rates (Questionnaires)
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Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
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Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Gilbert, Charles C.; Lueck, Lowell A. – 1976
Several traditional approaches to studying the student dropout are outlined. They include data collection on reasons given for dropping out and characteristics of dropouts, as well as follow-up study. The use of the National Center for Higher Education Management Systems (MCHEMS) Student Flow Model (SFM) is described to show how students could be…
Descriptors: College Freshmen, College Students, Data Analysis, Dropout Characteristics
Lawrence, Arul A. S. – Online Submission, 2015
The XX IDEA annual conference has been focused and reflected on different ways and means of meeting various kinds of methodological challenges, new technologies and multi-media developments, newly emerging partnerships and collaboration between emerging sectors on one hand and between the institutions functioning with similar objectives and…
Descriptors: Foreign Countries, Open Education, Distance Education, Barriers
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