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Direct linkAndrew Jaciw – Society for Research on Educational Effectiveness, 2024
Background: Rooted in problems of social justice, intersectionality addresses intragroup differences in impacts and outcomes and the compound discrimination at specific intersections of classification (Crenshaw,1991). It stresses that deficits/debts in outcomes often occur non-additively; for example, discriminatory hiring practices can be…
Descriptors: Intersectionality, Classification, Randomized Controlled Trials, Factor Analysis
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
Gonzalez, Oscar – Educational and Psychological Measurement, 2023
When scores are used to make decisions about respondents, it is of interest to estimate classification accuracy (CA), the probability of making a correct decision, and classification consistency (CC), the probability of making the same decision across two parallel administrations of the measure. Model-based estimates of CA and CC computed from the…
Descriptors: Classification, Accuracy, Intervals, Probability
Ransom, Keith J.; Perfors, Andrew; Hayes, Brett K.; Connor Desai, Saoirse – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2023
In describing how people generalize from observed samples of data to novel cases, theories of inductive inference have emphasized the learner's reliance on the contents of the sample. More recently, a growing body of literature suggests that different assumptions about how a data sample was generated can lead the learner to draw qualitatively…
Descriptors: Sampling, Generalization, Inferences, Logical Thinking
Meng Qiu; Ke-Hai Yuan – Structural Equation Modeling: A Multidisciplinary Journal, 2024
Latent class analysis (LCA) is a widely used technique for detecting unobserved population heterogeneity in cross-sectional data. Despite its popularity, the performance of LCA is not well understood. In this study, we evaluate the performance of LCA with binary data by examining classification accuracy, parameter estimation accuracy, and coverage…
Descriptors: Classification, Sample Size, Monte Carlo Methods, Social Science Research
Download full textMatthew Jannetti; Amy Carroll-Scott; Erikka Gilliam; Irene Headen; Maggie Beverly; Félice Lê-Scherban – Field Methods, 2023
Place-based initiatives often use resident surveys to inform and evaluate interventions. Sampling based on well-defined sampling frames is important but challenging for initiatives that target subpopulations. Databases that enumerate total population counts can produce overinclusive sampling frames, resulting in costly outreach to ineligible…
Descriptors: Sampling, Probability, Definitions, Prediction
Magooda, Ahmed; Elaraby, Mohamed; Litman, Diane – Grantee Submission, 2021
This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target…
Descriptors: Data Analysis, Synthesis, Documentation, Training
Mauer, Victoria; Savell, Shannon; Davis, Alida; Wilson, Melvin N.; Shaw, Daniel S.; Lemery-Chalfant, Kathryn – Journal of Early Adolescence, 2021
This study examined caregivers' longitudinal reports of adolescent multiracial categorization across the ages of 9.5, 10.5, and 14 years, and adolescents' reports of their own multiracial categorization at the age of 14 years. A portion of caregivers' reports of adolescent multiracial status were inconsistent across the years of the study; some…
Descriptors: Adolescents, Multiracial Persons, Classification, Identification
Jopke, Nikolaus; Gerrits, Lasse – International Journal of Social Research Methodology, 2019
There is a need to improve the ways in which Qualitative Comparative Analysis (QCA) handles qualitative data. To this end, we propose to include ideas and routines from Grounded Theory (GT) in QCA. We will first argue that there is a natural fit between the two on the ontological level. On the methodological level, we will demonstrate in what ways…
Descriptors: Qualitative Research, Comparative Analysis, Grounded Theory, Sampling
National Centre for Vocational Education Research (NCVER), 2022
"Apprentice and Trainee Outcomes 2021" provides a summary of the outcomes of apprentices and trainees who completed an apprenticeship or traineeship during 2020, with the data collected in mid-2021. The figures are derived from apprentices' and trainees' responses to the National Student Outcomes Survey (SOS), which is an annual survey…
Descriptors: Foreign Countries, Outcomes of Education, Apprenticeships, Trainees
Ellison, George T. H. – Journal of Statistics and Data Science Education, 2021
Temporality-driven covariate classification had limited impact on: the specification of directed acyclic graphs (DAGs) by 85 novice analysts (medical undergraduates); or the risk of bias in DAG-informed multivariable models designed to generate causal inference from observational data. Only 71 students (83.5%) managed to complete the…
Descriptors: Statistics Education, Medical Education, Undergraduate Students, Graphs
Yamaguchi, Kazuhiro – Journal of Educational and Behavioral Statistics, 2023
Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB)…
Descriptors: Bayesian Statistics, Classification, Statistical Inference, Sampling
Aydogdu, Seyhmus – Turkish Online Journal of Distance Education, 2020
The purpose of this research is a comprehensive review of studies towards educational data mining (EDM) in Turkey. For the purpose of this study, graduate theses and articles conducted in Turkey were examined in detail. As a result of the literature review, 48 studies were analyzed in the context of the data mining purpose, the technique used in…
Descriptors: Foreign Countries, Information Retrieval, Data Analysis, Academic Achievement
Harrison, Colin D.; Nguyen, Tiffy A.; Seidel, Shannon B.; Escobedo, Alycia M.; Hartman, Courtney; Lam, Katie; Liang, Kristen S.; Martens, Miranda; Acker, Gigi N.; Akana, Susan F.; Balukjian, Brad; Benton, Hilary P.; Blair, J. R.; Boaz, Segal M.; Boyer, Katharyn E.; Bram, Jason B.; Burrus, Laura W.; Byrd, Dana T.; Caporale, Natalia; Carpenter, Edward J.; Chan, Yee-Hung M.; Chen, Lily; Chovnick, Amy; Chu, Diana S.; Clarkson, Bryan K.; Cooper, Sara E.; Creech, Catherine J.; de la Torre, José R.; Denetclaw, Wilfred F.; Duncan, Kathleen; Edwards, Amelia S.; Erickson, Karen; Fuse, Megumi; Gorga, Joseph J.; Govindan, Brinda; Green, L. Jeanette; Hankamp, Paul Z.; Harris, Holly E.; He, Zheng-Hui; Ingalls, Stephen B.; Ingmire, Peter D.; Jacobs, J. Rebecca; Kamakea, Mark; Kimpo, Rhea R.; Knight, Jonathan D.; Krause, Sara K.; Krueger, Lori E.; Light, Terrye L.; Lund, Lance; Márquez-Magaña, Leticia M.; McCarthy, Briana K.; McPheron, Linda; Miller-Sims, Vanessa C.; Moffatt, Cristopher A.; Muick, Pamela C.; Nagami, Paul H.; Nusse, Gloria; Okimura, K. M.; Pasion, Sally G.; Patterson, Robert; Pennings, Pleuni S.; Riggs, Blake; Romeo, Joseph M.; Roy, Scott W.; Russo-Tait, Tatiane; Schultheis, Lisa M.; Sengupta, Lakshmikanta; Spicer, Greg S.; Swei, Andrea; Wade, Jennifer M.; Willsie, Julia K.; Kelley, Loretta A.; Owens, Melinda T.; Trujillo, Gloriana; Domingo, Carmen; Schinske, Jeffrey N.; Tanner, Kimberly D. – CBE - Life Sciences Education, 2019
Instructor Talk--noncontent language used by instructors in classrooms--is a recently defined and promising variable for better understanding classroom dynamics. Having previously characterized the Instructor Talk framework within the context of a single course, we present here our results surrounding the applicability of the Instructor Talk…
Descriptors: Classroom Communication, Language Usage, Novelty (Stimulus Dimension), Models
Moeller, Julia; Viljaranta, Jaana; Kracke, Bärbel; Dietrich, Julia – Frontline Learning Research, 2020
This article proposes a study design developed to disentangle the objective characteristics of a learning situation from individuals' subjective perceptions of that situation. The term objective characteristics refers to the agreement across students, whereas subjective perceptions refers to inter-individual heterogeneity. We describe a novel…
Descriptors: Student Attitudes, College Students, Lecture Method, Student Interests
