The explanatory power of Carnegie Classification in predicting engagement indicators: a multilevel analysis

Research output: Journal contributionsJournal articlesResearchpeer-review

Standard

The explanatory power of Carnegie Classification in predicting engagement indicators: a multilevel analysis. / Gök, Enes; Aydin, Burak.
In: Frontiers in Education, Vol. 8, 1305747, 08.01.2024.

Research output: Journal contributionsJournal articlesResearchpeer-review

Harvard

APA

Vancouver

Bibtex

@article{fcbf2de0aad64c4284862cb107167fcd,
title = "The explanatory power of Carnegie Classification in predicting engagement indicators: a multilevel analysis",
abstract = "The study aims to explore the effect of the type of higher education institution on students{\textquoteright} engagement. The meta-analyses of multilevel regression coefficients revealed significant relationships between the type of higher education institution and student engagement indicators across the years from 2013 to 2019. Comparing different types of higher education institutions with the base category, our findings revealed significant differences in effective teaching practices, discussion with diverse others, and student-faculty interaction consistent throughout the years. These findings are expected to provide insights for institutional administrators, policymakers, and researchers given that student engagement in higher education has become an indicator of quality all around the world.",
keywords = "assessment, Carnegie Classification, higher education, multilevel analysis, student engagement, Educational science",
author = "Enes G{\"o}k and Burak Aydin",
note = "Funding Information: The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Scientific and Technological Research Council of T{\"u}rkiye (Project ID: 1059B192000009). This publication was funded by the Open Access Publication Fund of the Leuphana University L{\"u}neburg. Publisher Copyright: Copyright {\textcopyright} 2024 G{\"o}k and Aydin.",
year = "2024",
month = jan,
day = "8",
doi = "10.3389/feduc.2023.1305747",
language = "English",
volume = "8",
journal = "Frontiers in Education",
issn = "2504-284X",
publisher = "Frontiers Media",

}

RIS

TY - JOUR

T1 - The explanatory power of Carnegie Classification in predicting engagement indicators

T2 - a multilevel analysis

AU - Gök, Enes

AU - Aydin, Burak

N1 - Funding Information: The authors declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Scientific and Technological Research Council of Türkiye (Project ID: 1059B192000009). This publication was funded by the Open Access Publication Fund of the Leuphana University Lüneburg. Publisher Copyright: Copyright © 2024 Gök and Aydin.

PY - 2024/1/8

Y1 - 2024/1/8

N2 - The study aims to explore the effect of the type of higher education institution on students’ engagement. The meta-analyses of multilevel regression coefficients revealed significant relationships between the type of higher education institution and student engagement indicators across the years from 2013 to 2019. Comparing different types of higher education institutions with the base category, our findings revealed significant differences in effective teaching practices, discussion with diverse others, and student-faculty interaction consistent throughout the years. These findings are expected to provide insights for institutional administrators, policymakers, and researchers given that student engagement in higher education has become an indicator of quality all around the world.

AB - The study aims to explore the effect of the type of higher education institution on students’ engagement. The meta-analyses of multilevel regression coefficients revealed significant relationships between the type of higher education institution and student engagement indicators across the years from 2013 to 2019. Comparing different types of higher education institutions with the base category, our findings revealed significant differences in effective teaching practices, discussion with diverse others, and student-faculty interaction consistent throughout the years. These findings are expected to provide insights for institutional administrators, policymakers, and researchers given that student engagement in higher education has become an indicator of quality all around the world.

KW - assessment

KW - Carnegie Classification

KW - higher education

KW - multilevel analysis

KW - student engagement

KW - Educational science

UR - http://www.scopus.com/inward/record.url?scp=85182665796&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/8111e9b6-7208-315f-b582-7f6e5ec54727/

U2 - 10.3389/feduc.2023.1305747

DO - 10.3389/feduc.2023.1305747

M3 - Journal articles

AN - SCOPUS:85182665796

VL - 8

JO - Frontiers in Education

JF - Frontiers in Education

SN - 2504-284X

M1 - 1305747

ER -

Recently viewed

Publications

  1. Back from the Deep
  2. Erratum zu
  3. Managing sustainable development with management control systems
  4. Competition between honey bees and wild bees and the role of nesting resources in a nature reserve
  5. Is the EnodePro® a Valid Tool to Determine the Bar Velocity in the Bench Press and Barbell Back Squat? A Comparative Analysis
  6. Explaining Age and Gender Differences in Employment Rates
  7. Differences in psychological strategies of failed and operational business owners in the Fiji Islands
  8. Making sense of sustainability transitions locally
  9. Induced Technological Change: Exploring its Implications for the Economics of Atmospheric Stabilization
  10. Cost of quality reports and value engineering
  11. The polarity field concept
  12. What Role for Public Participation in Implementing the EU Floods Directive? A comparison with the Water Framework Directive, early evidence from Germany, and a research agenda
  13. Optical part measuring inside a milling machine
  14. How to determine the pion cloud of the constituent quark
  15. Accuracy Improvement by Artificial Neural Networks in Technical Vision System
  16. Truth in labeling
  17. Forecasting Government Bond Yields with Neural Networks Considering Cointegration
  18. Der "fachdidaktische Code" der Lebenswelt- und/oder (?) Situationsorientierung
  19. Time and Income Poverty: An Interdependent Multidimensional Poverty Approach with German Time Use Diary Data
  20. Leverage points for reversing paddock tree loss in Upper Lachlan grazing landscapes: A workshop report.
  21. Digitized Evaluation of Academic Opportunities to Learn (OTLs) Concerning Linguistically Responsive Teaching (LRT)
  22. Soft Landing for electromagnetic actuators through adaptive pre-action combined with a slide surface to avoid electrical saturation