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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
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Heidi Taveter; Marina Lepp – Informatics in Education, 2025
Learning programming has become increasingly popular, with learners from diverse backgrounds and experiences requiring different support. Programming-process analysis helps to identify solver types and needs for assistance. The study examined students' behavior patterns in programming among beginners and non-beginners to identify solver types,…
Descriptors: Behavior Patterns, Novices, Expertise, Programming
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Kui Xie; Vanessa W. Vongkulluksn; Benjamin C. Heddy; Zilu Jiang – Educational Technology Research and Development, 2024
Engagement has been recognized as one of the most important factors of learning and achievement in academic settings. Research on engagement has been gearing toward a "person-in-context" orientation, where both personal characteristics and contextual features in relation to students' engagement are considered. This orientation allows a…
Descriptors: Learner Engagement, Environment, Student Characteristics, Research Methodology
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Giorgio Di Pietro – European Education, 2023
We use Eurobarometer data to examine barriers to international student mobility. Multivariate analysis is employed to study how individual characteristics are related to the obstacles preventing higher education students from participating in activities in another EU country. The results suggest that several demographic factors including area of…
Descriptors: Student Characteristics, Barriers, Student Mobility, Foreign Countries
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Juanjuan Niu – International Journal of Web-Based Learning and Teaching Technologies, 2024
The internet, which is constantly advancing in technology, together with the rapidly changing internet communication technology terminals, has formed a new internet media, which has penetrated into all fields of human material life and spiritual life. This article proposes a design scheme for optimizing the impact of internet environment health on…
Descriptors: Influence of Technology, Internet, College Students, Ethical Instruction
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Tetzlaff, Leonard; Edelsbrunner, Peter; Schmitterer, Alexandra; Hartmann, Ulrike; Brod, Garvin – Educational Psychology Review, 2023
Demonstrating the differential effectiveness of instructional approaches for learners is difficult because learners differ on multiple dimensions. The present study tests a person-centered approach to investigating differential effectiveness, in this case of reading instruction. In N = 517 German third-grade students, latent profile analysis…
Descriptors: Reading Instruction, Intervention, Grade 3, Elementary School Students
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Betsy Wolf – Society for Research on Educational Effectiveness, 2024
Introduction: The What Works Clearinghouse (WWC) reviews rigorous research on educational interventions with a goal of identifying "what works" and making that information accessible to educators and policymakers. The WWC has historically prioritized internal validity over external validity in rating the quality of research. One critique…
Descriptors: Educational Assessment, Educational Research, Validity, Research Utilization
Ashley Hannah Majzun – ProQuest LLC, 2023
Meta-analytic Structural Equation Modeling (MASEM) is the combination of meta-analysis (MA) and structural equation modeling (SEM). With new MASEM methodologies developed over the past few years, there is an opportunity to compare the past approaches with the new ones. The purpose of this dissertation is two-fold. First, the parameter estimates,…
Descriptors: Meta Analysis, Structural Equation Models, College Students, Academic Persistence
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Alan Peslak; Lisa Kovalchick; Wenli Wang; Paul Kovacs – Information Systems Education Journal, 2023
Are students who prefer online education different from those who prefer on-ground education, and how? This is an important question because educational institutions need to better understand student segmentations. This research examined 251 survey responses from students enrolled in Computer Information Systems courses at three universities over…
Descriptors: Higher Education, Information Systems, Courses, Electronic Learning
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Siu-Cheung Kong; Wei Shen – Interactive Learning Environments, 2024
Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…
Descriptors: Student Characteristics, Predictor Variables, Comprehension, Computation
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Mayworm, Ashley M.; Sharkey, Jill D.; Nylund-Gibson, Karen – Contemporary School Psychology, 2023
The authoritative school climate construct, or the degree to which schools demonstrate student support and disciplinary structure, predicts several important student outcomes (e.g., racial suspension gap, student disengagement). To better understand this construct, we used multilevel latent class analysis to identify latent classes of student…
Descriptors: Educational Environment, Student School Relationship, Discipline, Power Structure
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Capp, Gordon P.; Sullivan, Kathrine S.; Park, Yangjin – Oxford Review of Education, 2023
Few studies holistically examine how students experience the multiple dimensions of school climate and resilience promoting characteristics, or how these two constructs may be interrelated. This study utilised a sample of 78,550 7th, 9th, and 11th grade students in California. Roughly half of the participants were female (52%), and roughly half…
Descriptors: Educational Environment, Resilience (Psychology), Student Characteristics, Grade 7
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Jackson, Dennis L.; McLellan, Chelsea; Frey, Marc P.; Rauti, Carolyn M. – Canadian Journal of Education, 2020
Academic entitlement (AE), which includes some students' tendencies to express deservingness of academic outcomes, not based on achievement, may have serious implications, such as academic dishonesty and classroom incivility. Some researchers have suggested that there may be different types of students with regard to AE, implying that motives for…
Descriptors: Student Behavior, Personality Traits, Classification, Undergraduate Students
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Tanvir, Hasan; Chounta, Irene-Angelica – International Educational Data Mining Society, 2021
The aim of this work is to provide data-driven insights regarding the factors behind dropouts in Higher Education and their impact over time. To this end, we analyzed students' data collected by a Higher Education Institute over the last 11 years and we explored how socio-economic and academic changes may have impacted student dropouts and how…
Descriptors: Dropouts, College Students, Predictor Variables, Socioeconomic Status
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Patel, Rahul S.; Walker, Tanesha; Weber, Zachary T.; Kelley, Sarah D.; Hansen, Ryan – Journal of American College Health, 2022
Objective: Our pilot study tests whether university counseling centers (UCC) can apply the concept of cluster analysis, and geospatial analysis to identify clusters of "hot spots". Participants: Study participants were university students who received services from a large mid-western UCC between August 2015 and July 2016. The study was…
Descriptors: Counseling Services, Higher Education, Multivariate Analysis, College Students
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