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Yiran Chen – Research in Higher Education, 2025
The "k"-means clustering method, while widely embraced in college student typology research, is often misunderstood and misapplied. Many researchers regard "k"-means as a near-universal solution for uncovering homogeneous student groups, believing its success hinges primarily on the selection of an appropriate "k."…
Descriptors: College Students, Classification, Educational Research, Research Methodology
Are We Pulling the Same Rope? Clustering Connotations of Digit(al)ization in the Educational Context
Zarnow, Stefanie; Off, Mona – AERA Online Paper Repository, 2023
Numerous activities and measures can be observed in the context of digitization. However, these are often not interrelated or sufficiently anchored institutionally and structurally with regard to overarching goals. The aim of this study is therefore to carry out a theory-based clustering of connotations with the concept of digit(al)ization in…
Descriptors: Technology Uses in Education, Theories, Adults, Attitudes
Palmer, Bryan – National Centre for Vocational Education Research (NCVER), 2022
This paper summarises the exploratory quantitative analysis undertaken to investigate how vocational education and training (VET) students cluster and segment in the Australian VET market. This analysis is outlined in three sections. The first section focuses on 'clustering' as a technique for grouping data and the three clustering algorithms…
Descriptors: Vocational Education, Foreign Countries, Labor Market, Multivariate Analysis
Salehudin, Imam; Alpert, Frank – Education & Training, 2022
Purpose: This study analyzed segment differences of student preference for video use in lecture classes and university use of video lecture classes. The authors then conducted novel gap analyses to identify gaps between student segments' preferences for videos versus their level of exposure to in-class videos. Multivariate analysis of variance…
Descriptors: Preferences, Video Technology, Class Activities, College Students
Nabizadeh, Amir Hossein; Goncalves, Daniel; Gama, Sandra; Jorge, Joaquim – IEEE Transactions on Learning Technologies, 2022
The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in…
Descriptors: College Students, Prediction, Grades (Scholastic), Game Based Learning
Tanja Kovacic; Cormac Forkan – Irish Educational Studies, 2024
Young people who are either at risk of disengaging or disengaged from mainstream education in Ireland are often supported by what is termed 'out-of-school' or the 'alternative education' sector. A recent review of out-of-school education provision (Department of Education. 2022. "Review of Out-of-School Educational Provision." Dublin:…
Descriptors: Foreign Countries, Nontraditional Education, Inclusion, Mainstreaming
Luke Keele; Matthew Lenard; Lindsay Page – Annenberg Institute for School Reform at Brown University, 2021
In education settings, treatments are often non-randomly assigned to clusters, such as schools or classrooms, while outcomes are measured for students. This research design is called the clustered observational study (COS). We examine the consequences of common support violations in the COS context. Common support violations occur when the…
Descriptors: Cluster Grouping, Educational Environment, Outcomes of Treatment, Compliance (Psychology)
Karagiannopoulou, Evangelia; Milienos, Fotios S.; Kamtsios, Spiridon; Rentzios, Christos – Educational Psychology, 2020
The study aims at investigating students' learning/defence profiles. It also explores students' profiles during different years of study. Participants comprised of 425 undergraduates. They completed the 'Approaches to Study and Skills Inventory' and the 'Defense Style Questionnaire'. The students' academic achievement was measured through grade…
Descriptors: Cognitive Style, Profiles, Measures (Individuals), Study Habits
Pouwels, J. Loes; Salmivalli, Christina; Saarento, Silja; van den Berg, Yvonne H. M.; Lansu, Tessa A. M.; Cillessen, Antonius H. N. – Child Development, 2018
The aim of this study was to determine how trajectory clusters of social status (social preference and perceived popularity) and behavior (direct aggression and prosocial behavior) from age 9 to age 14 predicted adolescents' bullying participant roles at age 16 and 17 (n = 266). Clusters were identified with multivariate growth mixture modeling…
Descriptors: Bullying, Adolescents, Student Participation, Predictive Validity
Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo – Journal of Learning Disabilities, 2018
The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the "Diagnostic and Statistical Manual of Mental Disorders." The…
Descriptors: Cognitive Development, Cluster Grouping, Learning Disabilities, Identification
Steiner, Peter M.; Kim, Jee-Seon – Society for Research on Educational Effectiveness, 2015
Despite the popularity of propensity score (PS) techniques they are not yet well studied for matching multilevel data where selection into treatment takes place among level-one units within clusters. This paper suggests a PS matching strategy that tries to avoid the disadvantages of within- and across-cluster matching. The idea is to first…
Descriptors: Computation, Outcomes of Treatment, Multivariate Analysis, Probability
Guasch, Marc; Haro, Juan; Boada, Roger – Psicologica: International Journal of Methodology and Experimental Psychology, 2017
With the increasing refinement of language processing models and the new discoveries about which variables can modulate these processes, stimuli selection for experiments with a factorial design is becoming a tough task. Selecting sets of words that differ in one variable, while matching these same words into dozens of other confounding variables…
Descriptors: Factor Analysis, Language Processing, Design, Cluster Grouping
Barata, Gabriel; Gama, Sandra; Jorge, Joaquim; Gonçalves, Daniel – IEEE Transactions on Learning Technologies, 2016
State of the art research shows that gamified learning can be used to engage students and help them perform better. However, most studies use a one-size-fits-all approach to gamification, where individual differences and needs are ignored. In a previous study, we identified four types of students attending a gamified college course, characterized…
Descriptors: Prediction, Performance, Profiles, Games
McNeish, Daniel M.; Stapleton, Laura M. – Educational Psychology Review, 2016
Multilevel models are an increasingly popular method to analyze data that originate from a clustered or hierarchical structure. To effectively utilize multilevel models, one must have an adequately large number of clusters; otherwise, some model parameters will be estimated with bias. The goals for this paper are to (1) raise awareness of the…
Descriptors: Hierarchical Linear Modeling, Statistical Analysis, Sample Size, Effect Size
Bowers, Alex J.; Blitz, Mark; Modeste, Marsha; Salisbury, Jason; Halverson, Richard R. – Teachers College Record, 2017
Background: Across the recent research on school leadership, leadership for learning has emerged as a strong framework for integrating current theories, such as instructional, transformational, and distributed leadership as well as effective human resource practices, instructional evaluation, and resource allocation. Yet, questions remain as to…
Descriptors: Classification, Teacher Response, Educational Practices, Leadership Effectiveness