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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
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
Cantwell, Robert H.; Bourke, Sid F.; Scevak, Jill J.; Holbrook, Allyson P.; Budd, Janene – Studies in Higher Education, 2017
A national cohort of doctoral students (n = 1390) completed a suite of metacognitive questionnaires indicating management of affective, intellectual and contingency demands in learning. Responses to the questionnaires were analysed for evidence of individual differences in reported metacognitive behaviours. Three patterns of metacognitive response…
Descriptors: Individual Differences, Doctoral Programs, Graduate Students, Metacognition
Ellis, Robert A. – Active Learning in Higher Education, 2016
There is variation in the university student experience of learning. Prior research has shown that factors that shape this include student characteristics, the learning context, student perceptions of that context and approaches to learning and their learning outcomes. In blended contexts, there is a need to identify variables which can explain…
Descriptors: Student Experience, Educational Environment, Inquiry, Higher Education
Papi, Mostafa; Teimouri, Yasser – Language Learning, 2014
The study aimed to identify different second language (L2) learner motivational types drawing on the framework of the L2 motivational self system. A total of 1,278 secondary school students learning English in Iran completed a questionnaire survey. Cluster analysis yielded five different groups based on the strength of different variables within…
Descriptors: Multivariate Analysis, Second Language Learning, Second Language Instruction, Motivation
Smith, Russell K. – Research in Higher Education Journal, 2014
A segmentation study is used to partition college students into groups that are more or less likely to adopt tablet technology as a learning tool. Because the college population chosen for study presently relies upon laptop computers as their primary learning device, tablet technology represents a "next step" in technology. Student…
Descriptors: College Students, Cluster Grouping, Student Attitudes, Laptop Computers
Evan, Aimee J.; Burden, Frances F.; Gheen, Margaret H.; Smerdon, Becky A. – Career and Technical Education Research, 2013
Career academies have been effective in reducing the high school dropout rates and increasing academic course taking and course credit accumulation among students (Kemple & Willner, 2008; Kemple & Snipes, 2000). However, not all students have access to career academy programs as they are not universally implemented across the state of…
Descriptors: High School Students, Career Academies, Access to Education, Geographic Location
Saenz, Victor B.; Hatch, Deryl; Bukoski, Beth E.; Kim, Suyun; Lee, Kye-hyoung; Valdez, Patrick – Community College Review, 2011
This study employs survey data from the Center for Community College Student Engagement to examine the similarities and differences that exist across student-level domains in terms of student engagement in community colleges. In total, the sample used in the analysis pools data from 663 community colleges and includes more than 320,000 students.…
Descriptors: Learner Engagement, Community Colleges, Classification, Multivariate Analysis
Shaw, Emily J.; Kobrin, Jennifer L.; Packman, Sheryl F.; Schmidt, Amy Elizabeth – Journal of Advanced Academics, 2009
The media communicates the existence of two distinct types of college applicants: the frenzied, overachieving, anxious student who applies to many institutions, and the underprepared, less advantaged student whose parents are not at all familiar with the application process. The purpose of this study is to more realistically describe distinct…
Descriptors: College Preparation, College Choice, College Applicants, Enrollment Management
Luan, Jing – Online Submission, 2004
This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…
Descriptors: Educational Strategies, Evaluation Methods, Student Behavior, College Students