Social dimension of higher education: definition, indicators, models

Authors

DOI:

https://doi.org/10.55056/cte.108

Keywords:

higher education, social dimension, education statistics, students training, EUROSTUDENT, social statistics, modeling, educational and migration backround of students, cloud technologies, R, NodeXL

Abstract

The article deals with the problem of strengthening the social dimension of higher education. It discusses the definition of social dimension, its indicators, models of student retention and student engagement. The article argues that students should act as active researchers of the topic of social dimension and present the ways to update the content of university courses for Sociology majors, such as "Mathematical and statistical methods of social information analysis", "Social statistics and demography", "Multivariate data analysis", "Structural equation modeling" and other courses for bachelors, master students, and PhDs in Sociology.

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References

Annex II Rome Ministerial Communiqué, Principles and Guidelines to Strengthen the Social Dimension of Higher Education in the EHEA, Produced by the BFUG Advisory Group 1 for Social Dimension. Edited by Co-Chairs Schmidt Scukanec Ninoslav (Croatia) and Napier Robert (ESU), 24 June 2020, 2020. Available from: http://www.ehea.info/Upload/BFUG_DE_UK_73_9_b_PaG_SD.pdf.

Berger, J.B., Blanco Ramírez, G. and Lyon, S., 2012. Past to present: A historical look at retention. In: A. Seidman, ed. College student retention: Formula for student success. Lanham, MD: Rowman & Littlefield, pp.7–34.

Bilousova, L., Kolgatin, O. and Kolgatina, L., 2019. Computer simulation as a method of learning research in computational mathematics. Ceur workshop proceedings, 2393, pp.880–894.

Burke, A., 2019. Student Retention Models in Higher Education: A Literature Review. College and university, 94(2), pp.12–21. Available from: https://www.aacrao.org/research-publications/quarterly-journals/college-university-journal/issue/c-u-vol.-94-no.-2-spring.

Demetriou, C. and Schmitz-Sciborski, A., 2011. Integration, motivation, strengths and optimism: Retention theories past, present and future. In: R. Hayes, ed. Proceedings of the 7th national symposium on student retention, 2011, charleston. Norman, OK: The University of Oklahoma, pp.300–312. Available from: http://web.archive.org/web/20130223222447/http://studentsuccess.unc.edu/files/2012/11/Demetriou-and-Schmitz-Sciborski.pdf.

EUROSTUDENT VII final conference (Hanover, Germany, 18-19 may 2021), 2021. Available from: https://eua.eu/partners-news/672-eurostudent-vii-final-conference,-18-%E2%80%93-19-may.html.

Eurydice (European Education and Culture Executive Agency), 2020. Chapter 4: Social dimension. The European Higher Education Area in 2020, Bologna Process Implementation Report. Luxembourg: Publications Office of European Union. Available from: https://doi.org/doi/10.2797/851121.

Hadjar, A., Haas, C. and Gewinner, I., 2021. The roles of individual characteristics and institutional support in students’ higher education dropout intention in Luxembourg. Presentation of report in Eurostudent VII conference, Hannover. Available from: https://wwwde.uni.lu/content/download/136307/1556785/file/Spady_Tinto_4May2021.pdf.

Hauschildt, K., Gwosć, C., Schirmer, H. and Wartenbergh-Cras, F., 2021. Social and Economic Conditions of Student Life in Europe: EUROSTUDENT VII Synopsis of Indicators 2018–2021. Bielefeld: wbv Media GmbH & Co. KG. Available from: https://doi.org/10.3278/6001920dw.

Hauschildt, K., Gwosć, C., Schirmer, H. and Cras, F., 2019. The social dimension of student life in the European higher education area in 2019. Selected indicators from Eurostudent VII. Available from: https://www.eurostudent.eu/download_files/documents/Eurostudent_brochure_WEB.pdf.

Hauschildt, K., Vogtle, E.M. and Gwosc, C., 2018. Social and Economic Conditions of Student Life in Europe: Eurostudent VI 2016-2018: Synopsis of indicators. Bielefeld: W. Bertelsmann Verlag. Available from: https://doi.org/10.3278/6001920cw.

Kerby, M.B., 2015. Toward a new predictive model of student retention in higher education: An application of classical sociological theory. Journal of college student retention: Research, theory & practice, 17(2), pp.138–161. Available from: https://doi.org/10.1177/1521025115578229. DOI: https://doi.org/10.1177/1521025115578229

Kricorian, K., Seu, M., Lopez, D., Ureta, E. and Equils, O., 2020. Factors influencing participation of underrepresented students in STEM fields: matched mentors and mindsets. International journal of stem education, 7, pp.1–24. Available from: https://doi.org/10.1186/s40594-020-00219-2. DOI: https://doi.org/10.1186/s40594-020-00219-2

London Communiqué (Towards a European Higher Education Area: Responding to the Challenges of Globalization): Communiqué of the Conference of European Ministers responsible for Higher Education; London, 16-19 May 2007, 2007. Available from: http://www.ehea.info/Upload/document/ministerial_declarations/2007_London_Communique_English_588697.pdf.

Mishra, S. and Diesner, J., 2019. Capturing signals of enthusiasm and support towards social issues from twitter. Proceedings of the 5th international workshop on social media world sensors. New York, NY, USA: Association for Computing Machinery, SIdEWayS’19, p.19–24. Available from: https://doi.org/10.1145/3345645.3351104. DOI: https://doi.org/10.1145/3345645.3351104

Panchenko, L., 2019. Methodology of using structural equation modeling in educational research. Ceur workshop proceedings, 2393, pp.895–904.

Panchenko, L., Korzhov, H., Kolomiiets, T. and Yenin, M., 2021. PhD student training: principles and implementation. Journal of physics: Conference series, 1840(1). Available from: https://doi.org/10.1088/1742-6596/1840/1/012056. DOI: https://doi.org/10.1088/1742-6596/1840/1/012056

Panchenko, L. and Samovilova, N., 2020. Secondary data analysis in educational research: opportunities for PhD students. Shs web of conferences, 75, p.04005. Available from: https://doi.org/10.1051/shsconf/20207504005. DOI: https://doi.org/10.1051/shsconf/20207504005

Panchenko, L.F., 2018. Training sociology students in computer analysis of demographic processes and structure. Information technologies and learning tools, 65(3), p.166–183. Available from: https://doi.org/10.33407/itlt.v65i3.2034. DOI: https://doi.org/10.33407/itlt.v65i3.2034

Panchenko, L.F. and Velychko, V.Y., 2022. Structural equation modeling in educational research: a case-study for PhD training. In: S. Semerikov, V. Osadchyi and O. Kuzminska, eds. Proceedings of the symposium on advances in educational technology, aet 2020. University of Educational Management, Kyiv: SciTePress. DOI: https://doi.org/10.5220/0010923900003364

Prague Communiqué (Towards a European Higher Education Area): Communiqué of the Meeting of European Ministers of Education, 2001. Available from: http://www.ehea.info/page-ministerial-conference-prague-2001.

Rome Ministerial Communiqué (Conference of Ministers of Higher Education of the European Higher Education Area ”Embrace the challenge, create new opportunities and cancel differences” (Rome, Italy, 19.11.2020), 2020. Available from: http://www.ehea.info/Upload/Rome_Ministerial_Communique.pdf.

Salmi, J., 2009. Violence, integrity and education. Global crime, 10(4), pp.396–415. Available from: https://doi.org/10.1080/17440570903248445. DOI: https://doi.org/10.1080/17440570903248445

Salmi, J., 2019. Social Dimension Within a Quality Oriented Higher Education System. In: A. Curaj, L. Deca and R. Pricopie, eds. European higher education area: The impact of past and future policies. Springer, Cham, pp.300–312. Available from: https://doi.org/10.1007/978-3-319-77407-7_10. DOI: https://doi.org/10.1007/978-3-319-77407-7_10

Semerikov, S., Teplytskyi, I., Yechkalo, Y., Markova, O., Soloviev, V. and Kiv, A., 2019. Computer simulation of neural networks using spreadsheets: Dr. Anderson, welcome back. Ceur workshop proceedings, 2393, pp.833–848. Available from: http://ceur-ws.org/Vol-2393/paper_348.pdf. DOI: https://doi.org/10.31812/123456789/3178

Soloviev, V., Moiseienko, N. and Tarasova, O., 2019. Modeling of cognitive process using complexity theory methods. Ceur workshop proceedings, 2393, pp.905–918. DOI: https://doi.org/10.31812/123456789/3609

Spady, W.G., 1970. Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), pp.64–85. Available from: https://doi.org/10.1007/BF02214313. DOI: https://doi.org/10.1007/BF02214313

Spady, W.G., 1971. Dropouts from higher education: Toward an empirical model. Interchange, 2(3), pp.38–62. Available from: https://doi.org/10.1007/BF02282469. DOI: https://doi.org/10.1007/BF02282469

Strategy for the development of higher education in Ukraine for 2021-2030, 2020. Available from: https://mon.gov.ua/storage/app/media/rizne/2020/09/25/rozvitku-vishchoi-osviti-v-ukraini-02-10-2020.pdf.

Tight, M., 2020. Student retention and engagement in higher education. Journal of further and higher education, 44(5), pp.689–704. Available from: https://doi.org/10.1080/0309877X.2019.1576860. DOI: https://doi.org/10.1080/0309877X.2019.1576860

Tinto, V., 1975. Dropout from higher education: A theoretical synthesis of recent research. Review of educational research, 45(1), pp.89–125. Available from: https://doi.org/10.3102/00346543045001089. DOI: https://doi.org/10.3102/00346543045001089

Tinto, V., 1994. Leaving college: Rethinking the causes and cures of student attrition. 2nd ed. Chicago: The University of Chicago Press. DOI: https://doi.org/10.7208/chicago/9780226922461.001.0001

Tinto, V., 1999. Taking Retention Seriously: Rethinking the First Year of College. Nacada journal, 19(2), pp.5–9. Available from: https://doi.org/10.12930/0271-9517-19.2.5. DOI: https://doi.org/10.12930/0271-9517-19.2.5

Tinto, V., 2000. Linking learning and leaving: Exploring the role of the college classroom in student departure. Nashville: Vanderbilt University Press, Reworking the student departure puzzle, pp.81–94. Available from: https://silo.tips/download/linking-learning-and-leaving-exploring-the-role-of-the-college-classroom-in-stud#. DOI: https://doi.org/10.2307/j.ctv176kvf4.8

Tinto, V., 2004. Student retention and graduation: Facing the truth, living with the consequences. (Occasional paper 1). Washington: The Pell Institute. Available from: http://www.pellinstitute.org/publications-Student_Retention_and_Graduation_July_2004.shtml.

Tinto, V., 2006. Research and practice of student retention: What next? Journal of college student retention: Research, theory & practice, 8(1), pp.1–19. Available from: https://doi.org/10.2190/4YNU-4TMB-22DJ-AN4W. DOI: https://doi.org/10.2190/4YNU-4TMB-22DJ-AN4W

Tishkovskaya, S. and Lancaster, G., 2010. Teaching strategies to promote statistical literacy: review and implementation. Icots 8 proceedings: International conference on teaching statistics 2010, ljubljana, slovenia. Auckland, New Zealand: International Association for Statistical Education. Available from: http://icots.info/8/cd/pdfs/contributed/ICOTS8_C193_TISHKOVSKAY.pdf.

Unger, M., 2021. What can EUROSTUDENT data tell us about the social dimension? Eurostudent vii final conference, 19.05.2021.

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Published

2022-03-21

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Section

Competency-Based Education Platforms and Social Analytics in Education

How to Cite

Panchenko, L.F., Korzhov, H.O., Khomiak, A.O., Velychko, V.Y. and Soloviev, V.N., 2022. Social dimension of higher education: definition, indicators, models. CTE Workshop Proceedings [Online], 9, pp.124–138. Available from: https://doi.org/10.55056/cte.108 [Accessed 24 July 2024].
Received 2021-06-28
Accepted 2021-12-17
Published 2022-03-21

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