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Costa, Stella F.; Diniz, Michael M. – Education and Information Technologies, 2022
The large rates of students' failure is a very frequent problem in undergraduate courses, being even more evident in exact sciences. Pointing out the reasons of such problem is a paramount research topic, though not an easy task. An alternative is to use Educational Data Mining techniques (EDM), which enables one to convert data from educational…
Descriptors: Prediction, Undergraduate Students, Mathematics Education, Models
Roslan, Muhammad Haziq Bin; Chen, Chwen Jen – Education and Information Technologies, 2023
This study attempts to predict secondary school students' performance in English and Mathematics subjects using data mining (DM) techniques. It aims to provide insights into predictors of students' performance in English and Mathematics, characteristics of students with different levels of performance, the most effective DM technique for students'…
Descriptors: Foreign Countries, Secondary School Students, Academic Achievement, English Instruction
Naseem, Mohammed; Chaudhary, Kaylash; Sharma, Bibhya – Education and Information Technologies, 2022
The need for a knowledge-based society has perpetuated an increasing demand for higher education around the globe. Recently, there has been an increase in the demand for Computer Science professionals due to the rise in the use of ICT in the business, health and education sector. The enrollment numbers in Computer Science undergraduate programmes…
Descriptors: College Freshmen, Student Attrition, School Holding Power, Dropout Prevention
Camille Gasaway Pace – ProQuest LLC, 2021
Even with extensive retention research dating from the 1960s, community colleges still struggle to identify the reasons why students do not return to college. Data mining has allowed these retention models to evolve to identify new patterns among student populations and variables. The purpose of this study was to create a predictive model for…
Descriptors: Community Colleges, School Holding Power, College Freshmen, Information Retrieval
CannistrĂ , 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
Trakunphutthirak, Ruangsak; Lee, Vincent C. S. – Journal of Educational Computing Research, 2022
Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…
Descriptors: Information Retrieval, Pattern Recognition, Data Analysis, Information Technology
Suh, Suhyun; Suh, Jingyo – Educational Research Quarterly, 2011
There has been a general decline in the dropout rate and an increase in the high school completion rate over the last three decades. This research investigates causes for the decline in the dropout rate over the periods using decomposition analysis. Traditional cross-section analysis was inadequate to perform this task. Using the two cohort…
Descriptors: Dropout Rate, Dropouts, Dropout Research, Time Perspective
Niemi, David; Gitin, Elena – International Association for Development of the Information Society, 2012
An underlying theme of this paper is that it can be easier and more efficient to conduct valid and effective research studies in online environments than in traditional classrooms. Taking advantage of the "big data" available in an online university, we conducted a study in which a massive online database was used to predict student…
Descriptors: Higher Education, Online Courses, Academic Persistence, Identification
Pitts, Kristy L. – ProQuest LLC, 2010
Since the passage of the No Child Left Behind Act (NCLB) in 2001, public school systems have been engaged in a system of educational reform fueled by a level of accountability that includes not only the performance of the students, but also the performance of the teachers and the administrators. Recent studies have found that student achievement…
Descriptors: Teacher Attendance, Employee Absenteeism, Teaching (Occupation), Pattern Recognition
Faulkner, Robert; Davidson, Jane W.; McPherson, Gary E. – International Journal of Music Education, 2010
The use of data mining for the analysis of data collected in natural settings is increasingly recognized as a legitimate mode of enquiry. This rule-inductive paradigm is an effective means of discovering relationships within large datasets--especially in research that has limited experimental design--and for the subsequent formulation of…
Descriptors: Foreign Countries, Data Processing, Pattern Recognition, Data Analysis
Hung, Jui-Long; Hsu, Yu-Chang; Rice, Kerry – Educational Technology & Society, 2012
This study investigated an innovative approach of program evaluation through analyses of student learning logs, demographic data, and end-of-course evaluation surveys in an online K-12 supplemental program. The results support the development of a program evaluation model for decision making on teaching and learning at the K-12 level. A case study…
Descriptors: Web Based Instruction, Databases, Virtual Classrooms, Decision Support Systems
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