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Year 2023, Volume: 6 Issue: 4 - ICETOL 2023 Special Issue, 1128 - 1145, 31.12.2023

Akça Okan Yüksel Sibel Somyürek

https://doi.org/10.31681/jetol.1334985

Abstract

References

  • Alonso, F., Manrique, D., Martínez, L., & Viñes, J. M. (2015). Study of the influence of social relationships among students on knowledge building using a moderately constructivist learning model. Journal of Educational Computing Research, 51(4), 417-439.
  • Baki, A., ve Gökçek, T. (2012). Karma Yöntem Araştırmalarına Genel Bir Bakış. Electronic Journal of Social Sciences, 11(42).
  • Biancani, S., & McFarland, D. A. (2013). Social networks research in higher education. In Higher education: Handbook of theory and research (pp. 151-215). Springer, Dordrecht.
  • Bruun, J., & Brewe, E. (2013). Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores. Physical Review Special Topics-Physics Education Research, 9(2), 020109.
  • Büyüköztürk, Ş., Akgün, Ö. E., Demirel, F., Karadeniz, Ş., & Çakmak, E. K. (2015). Bilimsel araştırma yöntemleri (14.baskı). Ankara: Pegem Akademi.
  • Carolan, B. V. (2014). Social network analysis and education: Theory, methods & applications. Thousand Oaks, CA: SAGE.
  • Cela, K. L., Sicilia, M. Á., & Sánchez, S. (2015). Social network analysis in e-learning environments: A preliminary systematic review. Educational Psychology Review, 27(1), 219-246.
  • Chan, J. W., & Pow, J. W. (2020). The role of social annotation in facilitating collaborative inquiry-based learning. Computers & Education, 147, 103787.
  • Chen, B., & Huang, T. (2019). It is about timing: Network prestige in asynchronous online discussions. Journal of Computer Assisted Learning, 35(4), 503-515.
  • Cohen, L. M., Manion, L., & Morrison, K. (2007). Research methods in education. New York: Routledge.
  • Cross, R., Parker, A., & Borgatti, S. P. (2002). A bird’s-eye view: Using social network analysis to improve knowledge creation and sharing. In IBM Institute for Business Value (Vol. 2). Somers, NY: IBM Corporation.
  • Çalık, M., & Sözbilir, M. (2014). İçerik analizinin parametreleri. Eğitim ve Bilim, 39(174), 33-38.
  • Dawson, S. (2010). ‘Seeing’the learning community: An exploration of the development of a resource for monitoring online student networking. British journal of educational technology, 41(5), 736-752.
  • Dinçer, S. (2018). Eğitim bilimleri araştırmalarında içerik analizi: Meta-analiz, meta sentez, betimsel içerik analizi. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 7(1), 176-190.
  • Ellis, R. A., Han, F., & Pardo, A. (2019). When does collaboration lead to deeper learning? Renewed definitions of collaboration for engineering students. IEEE Transactions on Learning Technologies, 12(1), 123-132.
  • Freeman, L.C. (2006). The development of social network analysis: A study in the socıology of scıence. Vancouver: ΣP Empirical Press.
  • Froehlich, D. E., Van Waes, S., & Schäfer, H. (2020). Linking quantitative and qualitative network approaches: A review of mixed methods social network analysis in education research. Review of Research in Education, 44(1), 244-268.
  • Gillani, N., & Eynon, R. (2014). Communication patterns in massively open online courses. The Internet and Higher Education, 23, 18-26.
  • Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE—Life Sciences Education, 13(2), 167-178.
  • Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of educational computing research, 17(4), 397-431.
  • Heo, H., Lim, K. Y., & Kim, Y. (2010). Exploratory study on the patterns of online interaction and knowledge co-construction in project-based learning. Computers & Education, 55(3), 1383-1392.
  • Jan, S. K., Vlachopoulos, P., & Parsell, M. (2019). Social Network Analysis and Learning Communities in Higher Education Online Learning: A Systematic Literature Review. Online Learning, 23(1).
  • Jo, I., Park, Y., & Lee, H. (2017). Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison. Journal of Computer Assisted Learning, 33(2), 106-122.
  • Gerlach, J. M. (1994). Is this collaboration? In Bosworth, K. & Hamilton, S. J. (Eds.), Collaborative Learning: Underlying Processes and Effective Techniques, New Directions for Teaching and Learning, No. 59. (pp.5-14). San Francisco: USA, Jossey-Bass.
  • Gewerc, A., Montero, L., & Lama, M. (2014). Collaboration and social networking in higher Education. Comunicar, 42, 55-62.
  • Hew, K. F., Cheung, W. S., & Ng, C. S. L. (2010). Student contribution in asynchronous online discussion: A review of the research and empirical exploration. Instructional science, 38, 571-606.
  • Kim, D., Park, Y., Yoon, M., & Jo, I. H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. The Internet and Higher Education, 30, 30-43.
  • Liu, X., Magjuka, R. J., Bonk, C. J., & Lee, S. (2007). Does sense of community matter? The Quarterly Review of Distance Education, 8(1), 9–24.
  • McLaughlin, M. W. & Talbert, J. E. (2006). Building school-based teacher learning communities: Professional strategies to improve student achievement (Vol. 45). Teachers College Press.
  • McGloin, J. M., & Kirk, D. S. (2014). An overview of social network analysis. Advancing Quantitative Methods in Criminology and Criminal Justice, 67-79.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Moolenaar, N.M. (2012). A social network perspective on teacher collaboration in schools: Theory, methodology and applications. American Journal of Education, 119, 7-39.
  • Morgan, D. L. (1998). Practical strategies for combining qualitative and quantitative methods: Applications to health research. Qualitative health research, 8(3), 362-376.
  • Moskal, B. M., & Leydens, J. A. (2000). Scoring rubric development: Validity and reliability. Practical assessment, research, and evaluation, 7(1), 10.
  • Scott, J. (2000). Social network analysis: A handbook. London: Sage.
  • Senemoğlu, N. (2007). Gelişim öğrenme ve öğretim kuramdan uygulamaya. Ankara: Pegem.
  • Somyürek, S. ve Güyer, T. (2020). Social Network Analysis. Educational Data Mining and Learning Analytics, Güyer, T., Yurdugül, H., & Yıldırım, S. (Editor), Anı Publications ISBN: 978-605-170-379-4.
  • Sun, Z., Liu, R., Luo, L., Wu, M., & Shi, C. (2017). Exploring collaborative learning effect in blended learning environments. Journal of computer assisted learning, 33(6), 575-587.
  • Tabassum, S., Pereira, F. S., Fernandes, S., & Gama, J. (2018). Social network analysis: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), e1256.
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University.
  • We are Social (2020). Digital in 2020. Retrieved from https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media
  • Wu, D., & Hiltz, S. R. (2004). Predicting learning from asynchronous online discussions. Journal of asynchronous learning networks, 8(2), 139-152.

The use of social network analysis in educational sciences studies

Year 2023, Volume: 6 Issue: 4 - ICETOL 2023 Special Issue, 1128 - 1145, 31.12.2023

Akça Okan Yüksel Sibel Somyürek

https://doi.org/10.31681/jetol.1334985

Abstract

Since social networks analysis in education offers valuable insights into social structures and social dynamics that shapes individuals behaviors and information storage and transmission, it has become a hot topic in educational science studies. The aim of this study is to examine the educational sciences studies conducted at higher education level in which social network analysis is used. The studies were analyzed based on journals, years, author countries, number of citations, models, theories, and concepts, research methods and target audience. Content analysis method was used in the study. The reliability of inter-coder agreement was calculated as .88. The findings were categorized under certain themes according to the research questions. According to the results, Internet and Higher Education (n=6) and Computers and Education (n=5) were the journals with the most publications, while 2019 was the year that the most studies (n=12) were conducted. The studies were mostly conducted by authors in the USA. "Seeing' the learning community: An exploration of the development of a resource for monitoring online student networking" was the most cited article. When the underlying models, theories and concepts in the studies were analyzed, six themes emerged: social paradigm, learning environments/tools, learning approaches/methods, feedback/assessment, informal approaches to teaching and individual characteristics. The most frequently used method was quantitative research, and the target group was undergraduate students. The target group size was mostly between 30-60, and convenience sampling was primarily employed for the target group selection. According to the findings and results of the study, suggestions for the use of social network analysis in the field of educational sciences were presented.

Keywords

Social Network Analysis Higher Education Educational Sciences Content Analysis

References

  • Alonso, F., Manrique, D., Martínez, L., & Viñes, J. M. (2015). Study of the influence of social relationships among students on knowledge building using a moderately constructivist learning model. Journal of Educational Computing Research, 51(4), 417-439.
  • Baki, A., ve Gökçek, T. (2012). Karma Yöntem Araştırmalarına Genel Bir Bakış. Electronic Journal of Social Sciences, 11(42).
  • Biancani, S., & McFarland, D. A. (2013). Social networks research in higher education. In Higher education: Handbook of theory and research (pp. 151-215). Springer, Dordrecht.
  • Bruun, J., & Brewe, E. (2013). Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores. Physical Review Special Topics-Physics Education Research, 9(2), 020109.
  • Büyüköztürk, Ş., Akgün, Ö. E., Demirel, F., Karadeniz, Ş., & Çakmak, E. K. (2015). Bilimsel araştırma yöntemleri (14.baskı). Ankara: Pegem Akademi.
  • Carolan, B. V. (2014). Social network analysis and education: Theory, methods & applications. Thousand Oaks, CA: SAGE.
  • Cela, K. L., Sicilia, M. Á., & Sánchez, S. (2015). Social network analysis in e-learning environments: A preliminary systematic review. Educational Psychology Review, 27(1), 219-246.
  • Chan, J. W., & Pow, J. W. (2020). The role of social annotation in facilitating collaborative inquiry-based learning. Computers & Education, 147, 103787.
  • Chen, B., & Huang, T. (2019). It is about timing: Network prestige in asynchronous online discussions. Journal of Computer Assisted Learning, 35(4), 503-515.
  • Cohen, L. M., Manion, L., & Morrison, K. (2007). Research methods in education. New York: Routledge.
  • Cross, R., Parker, A., & Borgatti, S. P. (2002). A bird’s-eye view: Using social network analysis to improve knowledge creation and sharing. In IBM Institute for Business Value (Vol. 2). Somers, NY: IBM Corporation.
  • Çalık, M., & Sözbilir, M. (2014). İçerik analizinin parametreleri. Eğitim ve Bilim, 39(174), 33-38.
  • Dawson, S. (2010). ‘Seeing’the learning community: An exploration of the development of a resource for monitoring online student networking. British journal of educational technology, 41(5), 736-752.
  • Dinçer, S. (2018). Eğitim bilimleri araştırmalarında içerik analizi: Meta-analiz, meta sentez, betimsel içerik analizi. Bartın Üniversitesi Eğitim Fakültesi Dergisi, 7(1), 176-190.
  • Ellis, R. A., Han, F., & Pardo, A. (2019). When does collaboration lead to deeper learning? Renewed definitions of collaboration for engineering students. IEEE Transactions on Learning Technologies, 12(1), 123-132.
  • Freeman, L.C. (2006). The development of social network analysis: A study in the socıology of scıence. Vancouver: ΣP Empirical Press.
  • Froehlich, D. E., Van Waes, S., & Schäfer, H. (2020). Linking quantitative and qualitative network approaches: A review of mixed methods social network analysis in education research. Review of Research in Education, 44(1), 244-268.
  • Gillani, N., & Eynon, R. (2014). Communication patterns in massively open online courses. The Internet and Higher Education, 23, 18-26.
  • Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE—Life Sciences Education, 13(2), 167-178.
  • Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of educational computing research, 17(4), 397-431.
  • Heo, H., Lim, K. Y., & Kim, Y. (2010). Exploratory study on the patterns of online interaction and knowledge co-construction in project-based learning. Computers & Education, 55(3), 1383-1392.
  • Jan, S. K., Vlachopoulos, P., & Parsell, M. (2019). Social Network Analysis and Learning Communities in Higher Education Online Learning: A Systematic Literature Review. Online Learning, 23(1).
  • Jo, I., Park, Y., & Lee, H. (2017). Three interaction patterns on asynchronous online discussion behaviours: A methodological comparison. Journal of Computer Assisted Learning, 33(2), 106-122.
  • Gerlach, J. M. (1994). Is this collaboration? In Bosworth, K. & Hamilton, S. J. (Eds.), Collaborative Learning: Underlying Processes and Effective Techniques, New Directions for Teaching and Learning, No. 59. (pp.5-14). San Francisco: USA, Jossey-Bass.
  • Gewerc, A., Montero, L., & Lama, M. (2014). Collaboration and social networking in higher Education. Comunicar, 42, 55-62.
  • Hew, K. F., Cheung, W. S., & Ng, C. S. L. (2010). Student contribution in asynchronous online discussion: A review of the research and empirical exploration. Instructional science, 38, 571-606.
  • Kim, D., Park, Y., Yoon, M., & Jo, I. H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. The Internet and Higher Education, 30, 30-43.
  • Liu, X., Magjuka, R. J., Bonk, C. J., & Lee, S. (2007). Does sense of community matter? The Quarterly Review of Distance Education, 8(1), 9–24.
  • McLaughlin, M. W. & Talbert, J. E. (2006). Building school-based teacher learning communities: Professional strategies to improve student achievement (Vol. 45). Teachers College Press.
  • McGloin, J. M., & Kirk, D. S. (2014). An overview of social network analysis. Advancing Quantitative Methods in Criminology and Criminal Justice, 67-79.
  • Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.
  • Moolenaar, N.M. (2012). A social network perspective on teacher collaboration in schools: Theory, methodology and applications. American Journal of Education, 119, 7-39.
  • Morgan, D. L. (1998). Practical strategies for combining qualitative and quantitative methods: Applications to health research. Qualitative health research, 8(3), 362-376.
  • Moskal, B. M., & Leydens, J. A. (2000). Scoring rubric development: Validity and reliability. Practical assessment, research, and evaluation, 7(1), 10.
  • Scott, J. (2000). Social network analysis: A handbook. London: Sage.
  • Senemoğlu, N. (2007). Gelişim öğrenme ve öğretim kuramdan uygulamaya. Ankara: Pegem.
  • Somyürek, S. ve Güyer, T. (2020). Social Network Analysis. Educational Data Mining and Learning Analytics, Güyer, T., Yurdugül, H., & Yıldırım, S. (Editor), Anı Publications ISBN: 978-605-170-379-4.
  • Sun, Z., Liu, R., Luo, L., Wu, M., & Shi, C. (2017). Exploring collaborative learning effect in blended learning environments. Journal of computer assisted learning, 33(6), 575-587.
  • Tabassum, S., Pereira, F. S., Fernandes, S., & Gama, J. (2018). Social network analysis: An overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(5), e1256.
  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University.
  • We are Social (2020). Digital in 2020. Retrieved from https://wearesocial.com/blog/2020/01/digital-2020-3-8-billion-people-use-social-media
  • Wu, D., & Hiltz, S. R. (2004). Predicting learning from asynchronous online discussions. Journal of asynchronous learning networks, 8(2), 139-152.
There are 42 citations in total.

Details

Primary Language English
Subjects Instructional Technologies
Journal Section Articles
Authors

Akça Okan Yüksel 0000-0002-5430-0821

Sibel Somyürek 0000-0001-7803-1438

Publication Date December 31, 2023
Published in Issue Year 2023 Volume: 6 Issue: 4 - ICETOL 2023 Special Issue

Cite

APA Yüksel, A. O., & Somyürek, S. (2023). The use of social network analysis in educational sciences studies. Journal of Educational Technology and Online Learning, 6(4), 1128-1145. https://doi.org/10.31681/jetol.1334985
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