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An Empirical Assessment of SMOTE Variants Techniques and Interpretation Methods in Improving the Accuracy and the Interpretability of Student Performance Models
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Hayat Sahlaoui; El Arbi Abdellaoui Alaoui; Said Agoujil; Anand Nayyar – Education and Information Technologies, 2024
Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…
Descriptors: Sampling, Classification, Algorithms, Prediction
Data-Mining Techniques in Detecting Factors Linked to Academic Achievement
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Martínez Abad, Fernando; Chaparro Caso López, Alicia A. – School Effectiveness and School Improvement, 2017
In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…
Descriptors: Foreign Countries, Data Collection, Statistical Analysis, Evaluation Methods
Confusion in the Ranks: How Good Are England's Schools?
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Smithers, Alan – Sutton Trust, 2013
Understanding how well English education performs compared with other countries is a valuable exercise, particularly because the information can help England and other countries learn from successful systems. The most recent international league tables of pupil performance differ considerably. England languishes well down the list in PISA 2009,…
Descriptors: Foreign Countries, School Effectiveness, National Competency Tests, Classification
A Practitioner's Guide to Growth Models
Castellano, Katherine E.; Ho, Andrew D. – Council of Chief State School Officers, 2013
This "Practitioner's Guide to Growth Models," commissioned by the Technical Issues in Large-Scale Assessment (TILSA) and Accountability Systems & Reporting (ASR), collaboratives of the "Council of Chief State School Officers," describes different ways to calculate student academic growth and to make judgments about the…
Descriptors: Guides, Models, Academic Achievement, Achievement Gains
Effect of Different Methods of Reporting and Reclassification on Trends in Test Scores for Students with Disabilities.
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Ysseldyke, Jim; Bielinski, John – Exceptional Children, 2002
A study compared the effects of different methods of analyzing trends to illustrate how failure to account for change in classification will lead to misinterpretation of data on the performance of students with disabilities. Data from five years of assessment in Texas is used to illustrate effects of classification changes. (Contains references.)…
Descriptors: Academic Achievement, Accountability, Classification, Data Collection
A Taxonomy of Questions, or, What the Computer Can Help You Learn about Your Students' Achievement, Attendance, Characteristics. Management of Instructional Information Systems Project.
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Bank, Adrianne; Williams, Richard C. – 1985
This document is divided into two parts: the first presents a taxonomy of questions designed for a hypothetical computer database; the second describes the rationale for a question oriented approach to data. This taxonomy is described as a list of questions an educator might use to find out more about the achievement, attendance, and…
Descriptors: Academic Achievement, Attendance Records, Classification, Computer Oriented Programs