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Deho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
Prinsloo, Paul; Kaliisa, Rogers – British Journal of Educational Technology, 2022
Whilst learning analytics is still nascent in most African higher education institutions, many African higher education institutions use learning platforms and analytic services from providers "outside" of the African continent. A critical consideration of the protection of data privacy on the African continent and its implications for…
Descriptors: Foreign Countries, Information Security, Privacy, Data
Aom Perkash; Qaisar Shaheen; Robina Saleem; Furqan Rustam; Monica Gracia Villar; Eduardo Silva Alvarado; Isabel de la Torre Diez; Imran Ashraf – Education and Information Technologies, 2024
Developing tools to support students, educators, intuitions, and government in the educational environment has become an important task to improve the quality of education and learning outcomes. Information and communication technology (ICT) is adopted by educational institutions; one such instance is video interaction in flipped teaching.…
Descriptors: Academic Achievement, Colleges, Artificial Intelligence, Predictor Variables
Sun, Jeffrey C. – British Journal of Educational Technology, 2023
Technology integration and learning analytics offer insights to improve educational experiences and outcomes. In advancing these efforts, laws and policies govern these environments placing protections, standards, and developmental opportunities for higher education, students, faculty, and even the nation-state. Nonetheless, evidence of…
Descriptors: Technology Integration, Privacy, Student Rights, Laws
Bowers, Alex J.; Zhao, Yihan; Ho, Eric – High School Journal, 2022
Research on data use and school Early Warning Systems (EWS) notes a central practice of researchers and practitioners is to search for patterns in student data to predict outcomes so schools can support success when students experience challenges. Yet, the domain lacks a means to visualize the rich longitudinal data that schools collect. Here, we…
Descriptors: Learning Analytics, Visual Aids, Student Records, Longitudinal Studies
Xu, Yinuo; Pardos, Zachary A. – International Educational Data Mining Society, 2023
In studies that generate course recommendations based on similarity, the typical enrollment data used for model training consists only of one record per student-course pair. In this study, we explore and quantify the additional signal present in course transaction data, which includes a more granular account of student administrative interactions…
Descriptors: Semantics, Enrollment Trends, Learning Analytics, STEM Education
Travis, Tiffini A.; Ramirez, Christian – portal: Libraries and the Academy, 2020
Libraries remain one of the last places on campus where the purging of usage data is encouraged and "tracking" is a dirty word. While some libraries have demonstrated the usefulness of analytics, opponents bring up issues of privacy and debate the feasibility of student-generated library data for planning and assessment. Using a study…
Descriptors: Academic Libraries, Data Collection, Learning Analytics, Ethics
Chinsook, Kittipong; Khajonmote, Withamon; Klintawon, Sununta; Sakulthai, Chaiyan; Leamsakul, Wicha; Jantakoon, Thada – Higher Education Studies, 2022
Big data is an important part of innovation that has recently attracted a lot of interest from academics and practitioners alike. Given the importance of the education industry, there is a growing trend to investigate the role of big data in this field. Much research has been undertaken to date in order to better understand the use of big data in…
Descriptors: Student Behavior, Learning Analytics, Computer Software, Rating Scales
Zualkernan, Imran – International Association for Development of the Information Society, 2021
A significant amount of research has gone into predicting student performance and many studies have been conducted to predict why students drop out. A variety of data including digital footprints, socio-economic data, financial data, and psychological aspects have been used to predict student performance at the test, course, or program level.…
Descriptors: Prediction, Engineering Education, Academic Achievement, Dropouts
Gupta, Anika; Garg, Deepak; Kumar, Parteek – IEEE Transactions on Learning Technologies, 2022
With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…
Descriptors: Markov Processes, Online Courses, Learning Management Systems, Learning Analytics
Kisling, Reid; Peterson, Andrew; Nisbet, Robert – Strategic Enrollment Management Quarterly, 2021
Data analytics is undergoing an evolution through effective data use to support both operational and learning analytics models. However, this evolution will require that institutional leaders transform their data systems to best support the needs of application modeling and use their intuition to help drive the development of better analytical…
Descriptors: Higher Education, Learning Analytics, Models, Instructional Leadership
Holloway, Kristine – Journal of Electronic Resources Librarianship, 2020
The legal and ethical use of Big Data and Learning Analytics in academic libraries has been widely debated. Analyzing large data sets has tremendous potential for libraries to implement changes that help students and prove the library's value to the university. The librarian's role in safeguarding patron privacy in a university setting where…
Descriptors: Compliance (Legal), Ethics, Learning Analytics, Data Use
Broos, Tom; Pinxten, Maarten; Delporte, Margaux; Verbert, Katrien; De Laet, Tinne – Assessment & Evaluation in Higher Education, 2020
In this study, we present a case study involving two self-service dashboards providing feedback on learning and study skills and on academic achievement. These dashboards were offered to first-year university students in several study programmes in Flanders, Belgium. Data for this study were collected using usage tracking (N = 2875) and a survey…
Descriptors: Data Analysis, Learning Analytics, Dropout Prevention, Student Experience
Nasrollahian Mojarad, Sara; Cruz, Laura – To Improve the Academy, 2022
MegaSoTL projects are scholarship of teaching and learning (SoTL) projects that generate evidence of learning from multiple institutions. While being increasingly practiced, MegaSoTL projects and their potential contribution to improve higher education pedagogy remain understudied in higher education literature. In this article, we introduce…
Descriptors: Scholarship, Teaching Methods, Learning Processes, Higher Education
Williamson, Ben – British Journal of Educational Technology, 2019
Digital data are transforming higher education (HE) to be more student-focused and metrics-centred. In the UK, capturing detailed data about students has become a government priority, with an emphasis on using student data to measure, compare and assess university performance. The purpose of this paper is to examine the governmental and commercial…
Descriptors: Foreign Countries, Higher Education, Technology Uses in Education, Data Analysis
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