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Jamal Kay B. Rogers; Tamara Cher R. Mercado; Ronald S. Decano – Journal of Education and Learning (EduLearn), 2025
Poor academic performance remains among the most concerning educational issues, especially in higher education and online learning. To address the concern, institutions like the University of Southeastern Philippines (USeP) leverage educational data mining (EDM) techniques to generate relevant information from learning management systems (LMS)…
Descriptors: Foreign Countries, Learning Management Systems, Academic Achievement, Data Analysis
Butuner, Resul; Calp, M. Hanefi – International Journal of Assessment Tools in Education, 2022
Many institutions in the field of education have been involved in distance education with the learning management system. In this context, there has been a rapid increase in data in the e-learning process as a result of the development of technology and the widespread use of the internet. This increase is in the size of large data. Today, big data…
Descriptors: Distance Education, Academic Achievement, Data Collection, Data Analysis
Ashima Kukkar; Rajni Mohana; Aman Sharma; Anand Nayyar – Education and Information Technologies, 2024
In the profession of education, predicting students' academic success is an essential responsibility. This study introduces a novel methodology for predicting students' pass or fail outcome in certain courses. The system utilises academic, demographic, emotional, and VLE sequence information of students. Traditional prediction methods often…
Descriptors: Predictor Variables, Academic Achievement, Pass Fail Grading, Long Term Memory
Önder Kethüda – Journal of Marketing for Higher Education, 2024
This study evaluates the influence of ranking reports on university brands' credibility and perceived differentiation. Signaling theory is applied to link ranking with credibility and perceived differentiation. An experimental approach was used to collect data and to test the hypothesis. Data was collected from 328 participants in the UK regarding…
Descriptors: Credibility, Reputation, Institutional Characteristics, Institutional Evaluation
Bharara, Sanyam; Sabitha, Sai; Bansal, Abhay – Education and Information Technologies, 2018
Learning Analytics (LA) is an emerging field in which sophisticated analytic tools are used to improve learning and education. It draws from, and is closely tied to, a series of other fields of study like business intelligence, web analytics, academic analytics, educational data mining, and action analytics. The main objective of this research…
Descriptors: Data Collection, Data Analysis, Correlation, Academic Achievement
Mariana Torres – ProQuest LLC, 2024
Retention, persistence, and academic achievement for first-generation students have become increasingly prevalent with increasing access to higher education. Beyond access, the most discernible challenge relates to their identity as a first-generation college student and the lack of mentorship from their parents in navigating the college…
Descriptors: First Generation College Students, Mentors, Educational Experience, Teacher Student Relationship
Strong, Kimberly Ann; Escamilla, Kathy – Bilingual Research Journal, 2020
Federal law requires states and school districts to institute accountability systems that report disaggregated student data to ensure that all children make academic progress. However, one of the mandated categories for disaggregation -- English Learner (EL) -- is reported as a single group despite representing students who are beginning,…
Descriptors: Language Proficiency, Accountability, Academic Achievement, English Language Learners
Gür, Tahir – Education Quarterly Reviews, 2021
The lecturing instruction method stands out as the most used education method in university classrooms. Students and researchers have developed study techniques to reduce the disadvantages of this method to increase success at the undergraduate level. The most important, common, and traditional of them is taking note. The verbatim note-taking,…
Descriptors: Notetaking, Handwriting, Keyboarding (Data Entry), Correlation
Montgomery, Amanda P.; Mousavi, Amin; Carbonaro, Michael; Hayward, Denyse V.; Dunn, William – British Journal of Educational Technology, 2019
Blended learning (BL) is a popular e-Learning model in higher education that has the potential to take advantage of learning analytics (LA) to support student learning. This study utilized LA to investigate fourth-year undergraduates' (n = 157) use of self-regulated learning (SRL) within the online components of a previously unexamined BL…
Descriptors: Blended Learning, Educational Technology, Higher Education, Undergraduate Students
Gaševic, Dragan; Jovanovic, Jelena; Pardo, Abelardo; Dawson, Shane – Journal of Learning Analytics, 2017
The use of analytic methods for extracting learning strategies from trace data has attracted considerable attention in the literature. However, there is a paucity of research examining any association between learning strategies extracted from trace data and responses to well-established self-report instruments and performance scores. This paper…
Descriptors: Foreign Countries, Undergraduate Students, Engineering Education, Educational Research
Corey Rosso – ProQuest LLC, 2022
This study sought to better understand present-day relationships between institutional and programmatic characteristics and their impact on student outcomes at postsecondary career and technical schools. This is critical given: (a) the existence of a significant middle-skills gap impacting the U.S. economy; (b) a lack of research and guidance for…
Descriptors: Institutional Characteristics, Outcomes of Education, Academic Achievement, Vocational Education
Guarcello, Maureen A.; Levine, Richard A.; Beemer, Joshua; Frazee, James P.; Laumakis, Mark A.; Schellenberg, Stephen A. – Technology, Knowledge and Learning, 2017
Supplemental Instruction (SI) is a voluntary, non-remedial, peer-facilitated, course-specific intervention that has been widely demonstrated to increase student success, yet concerns persist regarding the biasing effects of disproportionate participation by already higher-performing students. With a focus on maintaining access for all students, a…
Descriptors: Peer Teaching, Supplementary Education, College Students, Student Participation
Steele, George E. – New Directions for Higher Education, 2018
Two of the most important issues facing those in the field of academic advising are the use of technology and data analytics. There is no question that technology and data will shape the delivery and expectations for academic advising in higher education in the years to come. This chapter explores the intersections between advising, technology,…
Descriptors: Academic Achievement, Academic Advising, Data Analysis, Technology Uses in Education
Rafa, Alyssa – Education Commission of the States, 2017
Research shows that chronic absenteeism can affect academic performance in later grades and is a key early warning sign that a student is more likely to drop out of high school. Several states enacted legislation to address this issue, and many states are currently discussing the utility of chronic absenteeism as an indicator of school quality or…
Descriptors: Attendance Patterns, Academic Achievement, Educational Policy, At Risk Students
Benjamin W. Arold; M. Danish Shakeel – Annenberg Institute for School Reform at Brown University, 2021
From 2010 onwards, most US states have aligned their education standards by adopting the Common Core State Standards (CCSS) for math and English Language Arts. The CCSS did not target other subjects such as science and social studies. We estimate spillovers of the CCSS on student achievement in non-targeted subjects in models with state and year…
Descriptors: Common Core State Standards, Mathematics Education, Language Arts, Alignment (Education)