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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
Ashley Haigler – ProQuest LLC, 2021
The results of an industry research survey showed, understanding Dissertation Research categories has not been the focused on many researchers and institutions. This research expands on machine learning methodologies using two similar datasets to answer these three questions: 1. Is there a way to track the trends of Pace University's Doctor of…
Descriptors: Artificial Intelligence, Content Analysis, Cluster Grouping, Classification
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Crisp, Gloria; Horn, Catherine L.; Kuczynski, Margaret; Zhou, Qiong; Cook, Elizabeth – Review of Higher Education, 2019
Study of four-year broad access institutions (BAIs) is important given their influence on postsecondary educational opportunities and the continued importance of the bachelor's degree in earnings premiums and critical social and civic outcomes. Descriptive results add to current understanding regarding heterogeneity of four-year BAIs by…
Descriptors: Inclusion, Classification, Institutional Characteristics, Admission Criteria
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Ravinder, Handanhal; Misra, Ram B. – American Journal of Business Education, 2014
ABC analysis is a well-established categorization technique based on the Pareto Principle for determining which items should get priority in the management of a company's inventory. In discussing this topic, today's operations management and supply chain textbooks focus on dollar volume as the sole criterion for performing the categorization. The…
Descriptors: Facility Inventory, Evaluation Methods, Classification, Administration
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Jabnoun, Naceur – Quality Assurance in Education: An International Perspective, 2015
Purpose: This paper aims to explore the influence of wealth, transparency and democracy on the number of universities per million people ranked among the top 300 and 500. The highly ranked universities in the world tend to be concentrated in a few countries. Design/Methodology/Approach: ANOVA was used to test the differences between the two groups…
Descriptors: Universities, Classification, Influences, Fiscal Capacity
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Suranyi, Zsuzsanna; Hitchcock, David B.; Hittner, James B.; Vargha, Andras; Urban, Robert – International Journal of Behavioral Development, 2013
Previous research on sensation seeking (SS) was dominated by a variable-oriented approach indicating that SS level has a linear relation with a host of problem behaviors. Our aim was to provide a person-oriented methodology--a probabilistic clustering--that enables examination of both inter- and intra-individual differences in not only the level,…
Descriptors: Personality Traits, Behavior Problems, Conceptual Tempo, Individual Differences
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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
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Amershi, Saleema; Conati, Cristina – Journal of Educational Data Mining, 2009
In this paper, we present a data-based user modeling framework that uses both unsupervised and supervised classification to build student models for exploratory learning environments. We apply the framework to build student models for two different learning environments and using two different data sources (logged interface and eye-tracking data).…
Descriptors: Supervision, Classification, Models, Educational Environment