Reviewing Mixed Methods Approaches Using Social Network Analysis for Learning and Education

  • Dominik Froehlich
  • Martin Rehm
  • Bart RientiesEmail author
Part of the Lecture Notes in Social Networks book series (LNSN)


Across the globe researchers are using social network analysis (SNA) to better understand the visible and invisible relations between people. While substantial progress has been made in the last 20 years in terms of quantitative modelling and processing techniques of SNA, there is an increased call for SNA researchers to embrace and mix methods developed in qualitative research to understand the what, how, and why questions of social network relations. In this chapter, we will reflect on our experiences with our latest edited book called Mixed Methods Approaches to Social Network Analysis for Learning and Education, which contained contributions from 20+ authors. We will first review the empirical literature of mixed methods social network analysis (MMSNA) by conducting a systematic literature review. Secondly, by using two case studies from our own practice, we will critically reflect on how we have used MMSNA approaches. Finally, we will discuss the potential limitations of MMSNA approaches, in particular given the complexities of mastering two ontologically different methods.


Social network analysis Mixed method MMSNA Systematic review 



Academic development


Amotivated students


Cognitive Evaluation Theory


Computer-supported collaborative learning


External motivation to external regulation


External motivation to identified regulation


External motivation to introjected regulation


Intrinsic motivation to experience stimulation


Intrinsic motivation to accomplish


Intrinsic motivation to know


Mixed methods


Mixed methods social network analysis


Multiple regressions quadratic assignment procedure


Problem-based learning


Self-Determination Theory


Social network analysis


Virtual learning environment


  1. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. Paper presented at the Third international AAAI conference on weblogs and social media.Google Scholar
  2. Bevelander, D., & Page, M. J. (2011). Ms. Trust: Gender, networks and trust—Implications for management and education. Academy of Management Learning & Education, 10(4), 623–642. Scholar
  3. Bogler, R., Caspi, A., & Roccas, S. (2013). Transformational and passive leadership: An initial investigation of university instructors as leaders in a virtual learning environment. Educational Management Administration & Leadership, 41(3), 372–392. Scholar
  4. Borgatti, S. P. (2002). NetDraw: Graph visualization software.Google Scholar
  5. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Harvard, MA: Analytic Technologies.Google Scholar
  6. Borgatti, S. P., & Halgin, D. (2011). On network theory. Organization Science, 22(5), 1168–1181. Scholar
  7. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892–895. Scholar
  8. Borgatti, S. P., & Molina, J. L. (2003). Ethical and strategic issues in organizational social network analysis. The Journal of Applied Behavioral Science, 39(3), 337–349. Scholar
  9. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. Scholar
  10. Bråten, I., Strømsø, H. I., & Salmerón, L. (2011). Trust and mistrust when students read multiple information sources about climate change. Learning and Instruction, 21(2), 180–192. Scholar
  11. Bryman, A. (2006). Integrating quantitative and qualitative research: how is it done? Qualitative Research, 6(1), 97–13. Scholar
  12. Buchanan, E. A. (2011). Internet research ethics: Past, present, and future. In M. Consalvo, C. Ess, & R. Burnett (Eds.), The handbook of internet studies (pp. 83–108). Somerset: Wiley.CrossRefGoogle Scholar
  13. Cárdenas-Robledo, L. A., & Peña-Ayala, A. (2018). Ubiquitous learning: A systematic review. Telematics and Informatics, 35(5), 1097–1132. Scholar
  14. Carpenter, M. A., Li, M., & Jiang, H. (2012). Social network research in organizational contexts: A systematic review of methodological issues and choices. Journal of Management, 38(4), 1328–1361. Scholar
  15. Cela, K. L., Sicilia, M. Á., & Sánchez, S. (2015). Social network analysis in E-learning environments: A preliminary systematic review. Educational Psychology Review, 21(1), 219–246. Scholar
  16. Clough, G., Jones, A. C., McAndrew, P., & Scanlon, E. (2008). Informal learning with PDAs and smartphones. Journal of Computer Assisted Learning, 24(5), 359–371. Scholar
  17. Coburn, C. E., & Russell, J. L. (2008). District policy and teachers’ social networks. Educational Evaluation and Policy Analysis, 30(3), 203–235. Scholar
  18. Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. Journal of Communication, 64(2), 317–332. Scholar
  19. Conde, M. A., García, F., Rodríguez-Conde, M. J., Alier, M., & García-Holgado, A. (2014). Perceived openness of Learning Management Systems by students and teachers in education and technology courses. Computers in Human Behavior, 31, 517–526. Scholar
  20. Conway, S. (2014). A cautionary note on data inputs and visual outputs in social network analysis. British Journal of Management, 25(1), 102–117. Scholar
  21. Crossley, N. (2010). The social world of the network. Combining qualitative and quantitative elements in social network analysis. Sociologica, 4(1), 1–34. Scholar
  22. Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.Google Scholar
  23. de Lima, J. Á. (2007). Teachers’ professional development in departmentalised, loosely coupled organisations: Lessons for school improvement from a case study of two curriculum departments. School Effectiveness and School Improvement, 18(3), 273–301. Scholar
  24. Dado, M., & Bodemer, D. (2017). A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, 22(Supplement C), 159–180. Scholar
  25. Daly, A. J., & Finnigan, K. S. (2010). A bridge between worlds: Understanding network structure to understand change strategy. Journal of Educational Change, 11(2), 111–138. Scholar
  26. Daly, A. J., & Finnigan, K. S. (2011). The ebb and flow of social network ties between district leaders under high-stakes accountability. American Educational Research Journal, 48(1), 39–79. Scholar
  27. Daly, A. J., Moolenaar, N. M., Bolivar, J. M., & Burke, P. (2010). Relationships in reform: The role of teachers’ social networks. Journal of Educational Administration, 48(3), 359–391. Scholar
  28. De Laat, M., & Lally, V. (2004). It’s not so easy: Researching the complexity of emergent participant roles and awareness in asynchronous networked learning discussions. Journal of Computer Assisted Learning, 20(3), 165–171. Scholar
  29. De Laat, M., Lally, V., Lipponen, L., & Simons, R.-J. (2007). Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis. International Journal of Computer-Supported Collaborative Learning, 2, 87–103. Scholar
  30. De Laat, M., & Schreurs, B. (2013). Visualizing informal professional development networks: Building a case for learning analytics in the workplace. American Behavioral Scientist, 57(10), 1421–1438. Scholar
  31. De Wever, B., Van Keer, H., Schellens, T., & Valcke, M. (2011). Assessing collaboration in a wiki: The reliability of university students’ peer assessment. The Internet and Higher Education, 14(4), 201–206. Scholar
  32. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behaviour. New York: Plenum.CrossRefGoogle Scholar
  33. Dillenbourg, P., Sharples, M., Fisher, F., Kollar, I., Tchounikine, P., Dimitriadis, Y., et al. (2011). Trends in orchestration. Second research & technology scouting report. Retrieved from:
  34. Duffy, P. (2008). Engaging the YouTube Google-eyed generation: Strategies for using web 2.0 in teaching and learning. Electronic Journal of E-learning, 6(2), 119–130. Scholar
  35. Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 1143–1168.CrossRefGoogle Scholar
  36. Ferguson, R., Barzilai, S., Ben-Zvi, D., Chinn, C. A., Herodotou, C., Hod, Y., et al. (2017). Innovating pedagogy 2017: Open University innovation report 6. Milton Keynes: The Open University.Google Scholar
  37. Ferguson, R., Brasher, A., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., et al. (2016). Research evidence of the use of learning analytics; implications for education policy. Retrieved from Luxembourg:
  38. Freeman, L. (2003). The development of social network analysis. Vancouver: Empirical Press.Google Scholar
  39. Frels, R. K., Newman, I., & Newman, C. (2015). Mentoring the next generation in mixed methods research. In S. Hesse-Biber & R. B. Johnson (Eds.), The Oxford handbook of multimethod and mixed methods research inquiry (pp. 333–353). Oxford: Oxford Internet Institute.Google Scholar
  40. Froehlich, D. (2019). Mapping mixed methods approaches to social network analysis in learning and education. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  41. Froehlich, D., & Bohle Carbonell, K. (2020). Social influences on team learning. In D. Gijbels, C. Harteis, & E. Kyndt (Eds.), Research approaches to workplace learning. Heidelberg: Springer.Google Scholar
  42. Froehlich, D., & Brouwer, J. (Forthcoming). Social network analysis as mixed analysis. In A. J. Onwuegbuzie & R. B. Johnson (Eds.), Reviewer’s guide for mixed methods research analysis. London: Routledge.Google Scholar
  43. Froehlich, D., & Gegenfurtner, A. (2019). Social support in transitioning from training to the workplace: A social network perspective. In H. Fasching (Ed.), Beziehungen in pädagogischen Arbeitsfeldern [Relations in pedagogical work]. Bad Heilbrunn: Klinkhardt.Google Scholar
  44. Froehlich, D., Mamas, X., & Schneider, Y. (2019). Mapping mixed methods approaches to social network analysis in learning and education. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  45. Froehlich, D., Rehm, M., & Rienties, B. (2019). Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  46. Gemmell, I., & Harrison, R. (2017). A comparison between national and transnational students’ access of online learning support materials and experience of technical difficulties on a fully online distance learning master of public health programme. Open Learning: The Journal of Open, Distance and e-Learning, 32(1), 66–80. Scholar
  47. Giesbers, B., Rienties, B., Tempelaar, D. T., & Gijselaers, W. H. (2013). Investigating the relations between motivation, tool use, participation, and performance in an e-learning course using web-videoconferencing. Computers in Human Behavior, 29(1), 285–292. Scholar
  48. Giesbers, B., Rienties, B., Tempelaar, D. T., & Gijselaers, W. H. (2014). A dynamic analysis of the interplay between asynchronous and synchronous communication in online learning: The impact of motivation. Journal of Computer Assisted Learning, 30(1), 30–50. Scholar
  49. Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2014). Technologies for foreign language learning: A review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1), 70–105. Scholar
  50. Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California.Google Scholar
  51. Hartrey, L., Denieffe, S., & Wells, J. S. G. (2017). A systematic review of barriers and supports to the participation of students with mental health difficulties in higher education. Mental Health & Prevention, 6, 26–43. Scholar
  52. Hesse-Biber, S. (2010). Qualitative approaches to mixed methods practice. Qualitative Inquiry, 16(6), 455–468. Scholar
  53. Hiltz-Hymes, C. E. (2011). The role of emotional contagion and flooding in the group process of children exposed to domestic violence. Barbara, CA: Fielding Graduate University.CrossRefGoogle Scholar
  54. Hoel, T., Griffiths, D., & Chen, W. (2017). The influence of data protection and privacy frameworks on the design of learning analytics systems. Paper presented at the Proceedings of the Seventh International Learning Analytics & Knowledge Conference, Vancouver, Canada.Google Scholar
  55. Holmes, V., Clark, W., Burt, P., & Rienties, B. (2013). Engaging teachers (and students) with media streaming technology, the case of Box of Broadcasts. In L. Wankel & P. Blessinger (Eds.), Increasing student engagement and retention using mobile applications: Smartphones, Skype and texting technologies (Vol. 6D, pp. 211–240). Bingley, UK: Emerald Publishing Group.Google Scholar
  56. Hommes, J., Arah, O. A., de Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2014). Medical students perceive better group learning processes when large classes are made to seem small. PLoS One, 9(4), e93328. Scholar
  57. Hommes, J., Rienties, B., de Grave, W., Bos, G., Schuwirth, L., & Scherpbier, A. (2012). Visualising the invisible: A network approach to reveal the informal social side of student learning. Advances in Health Sciences Education, 17(5), 743–757. Scholar
  58. Jindal-Snape, D., & Rienties, B. (Eds.). (2016). Multi-dimensional transitions of international students to higher education. London: Routledge.Google Scholar
  59. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition. Retrieved from Austin, Texas:
  60. Jones, E., Samra, R., & Lucassen, M. (2018). The world at their fingertips? The mental wellbeing of online distance-based law students. The Law Teacher, 1–21. Scholar
  61. Jones, T., & Cuthrell, K. (2011). YouTube: Educational potentials and pitfalls. Computers in the Schools, 28(1), 75–85. Scholar
  62. Kadushin, C. (2005). Who benefits from network analysis: Ethics of social network research. Social Networks, 27(2), 139–153. Scholar
  63. Kirschner, P. A. (2015). Facebook as learning platform: Argumentation superhighway or dead-end street? Computers in Human Behavior, 53, 621–625. Scholar
  64. Kirschner, P. A., & Erkens, G. (2013). Toward a framework for CSCL research. Educational Psychologist, 48(1), 1–8. Scholar
  65. Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237–1245. Scholar
  66. Klein, M., & Konieczny, P. (2015). Wikipedia in the world of global gender inequality indices: What the biography gender gap is measuring. Paper presented at the Proceedings of the 11th International Symposium on Open Collaboration, San Francisco, CA.Google Scholar
  67. Knight, S., Rienties, B., Littleton, K., Tempelaar, D. T., Mitsui, M., & Shah, C. (2017). The orchestration of a collaborative information seeking learning task. Information Retrieval Journal, 20(5), 480–505. Scholar
  68. Korir, M., Mittelmeier, J., & Rienties, B. (2019). Is mixed methods social network analysis ethical? In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  69. Lally, V., Sharples, M., Tracy, F., Bertram, N., & Masters, S. (2012). Researching the ethical dimensions of mobile, ubiquitous and immersive technology enhanced learning (MUITEL): A thematic review and dialogue. Interactive Learning Environments, 20(3), 217–238. Scholar
  70. Law, P., & Jelfs, A. (2016). Ten years of open practice: A reflection on the impact of OpenLearn. Open Praxis, 8(2), 7. Scholar
  71. Madge, C., Breines, M., Beatrice Dalu, M. T., Gunter, A., Mittelmeier, J., Prinsloo, P., et al. (2019). WhatsApp use among African international distance education (IDE) students: Transferring, translating and transforming educational experiences. Learning, Media and Technology, 44(3), 267–282.CrossRefGoogle Scholar
  72. Madge, C., Meek, J., Wellens, J., & Hooley, T. (2009). Facebook, social integration and informal learning at university: “It is more for socialising and talking to friends about work than for actually doing work”. Learning, Media and Technology, 34(2), 141–155. Scholar
  73. Manca, S., & Ranieri, M. (2013). Is it a tool suitable for learning? A critical review of the literature on Facebook as a technology-enhanced learning environment. Journal of Computer Assisted Learning, 29(6), 487–504. Scholar
  74. McCallum, A., Wang, X., & Corrada-Emmanuel, A. (2007). Topic and role discovery in social networks with experiments on enron and academic email. Journal of Artificial Intelligence Research, 30, 249–272.CrossRefGoogle Scholar
  75. McConnell, D., Hodgson, V., & Dirckinck-Holmfeld, L. (2012). Networked learning: A brief history and new trends. In L. Dirckinck-Holmfeld, V. Hodgson, & D. McConnell (Eds.), Exploring the theory, pedagogy and practice of networked learning (pp. 3–24). New York: Springer.CrossRefGoogle Scholar
  76. Moolenaar, N. M. (2012). A social network perspective on teacher collaboration in schools: Theory, methodology, and applications. American Journal of Education, 119(1), 7–39. Scholar
  77. Moolenaar, N. M., Sleegers, P. J. C., & Daly, A. J. (2012). Teaming up: Linking collaboration networks, collective efficacy, and student achievement. Teaching and Teacher Education, 28(2), 251–262. Scholar
  78. Neal, J. W. (2008). “Kracking” the missing data problem: Applying Krackhardt’s cognitive social structures to school-based social networks. Sociology of Education, 81(2), 140–162. Scholar
  79. Okada, A., & Moreira, P. (2008). Enhancing informal learning through videoconferencing and knowledge maps. Paper presented at the EDEN 2008, Universidade Aberta, Lisbon.Google Scholar
  80. Open University UK. (2014). Ethical use of student data for learning analytics policy. Retrieved from:
  81. Parsons, D., Hill, I., Holland, J., & Willis, D. (2012). Impact of teaching development programmes in higher education. Retrieved from York:
  82. Peña-Ayala, A. (2018). Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(3), e1243. Scholar
  83. Peña-Ayala, A., Cárdenas-Robledo, L. A., & Sossa, H. (2017). A landscape of learning analytics: An exercise to highlight the nature of an emergent field. In A. Peña-Ayala (Ed.), Learning analytics: Fundaments, applications, and trends: A view of the current state of the art to enhance e-learning (pp. 65–112). Cham: Springer International Publishing.CrossRefGoogle Scholar
  84. Pifer, M. J. (2011). Intersectionality in context: A mixed-methods approach to researching the faculty experience. New Directions for Institutional Research, 2011(151), 27–44. Scholar
  85. Piper, R., & Emmanuel, T. (2019). Co-producing mental health strategies with students: A guide for the higher education sector. Retrieved from London:
  86. Prinsloo, P., & Slade, S. (2017). Ethics and learning analytics: Charting the (un)charted. In C. Lang, G. Siemens, A. F. Wise, & D. Gasevic (Eds.), Handbook of learning analytics (pp. 49–57). Society for Learning Analytics Research.
  87. R Core Team. (2014). R: A language and environment for statistical computing. Retrieved from:
  88. Rehm, M., & Cornelissen, F. (2019). Power to the people?! Twitter discussions on (educational) policy processes. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  89. Rehm, M., Gijselaers, W., & Segers, M. (2015). The impact of hierarchical positions on communities of learning. International Journal of Computer-Supported Collaborative Learning, 10(2), 117–138. Scholar
  90. Rehm, M., Littlejohn, A., & Rienties, B. (2018). Does a formal wiki event contribute to the formation of a network of practice? A social capital perspective on the potential for informal learning. Interactive Learning Environments, 26(3), 308–319. Scholar
  91. Rehm, M., & Notten, A. (2016). Twitter as an informal learning space for teachers!? The role of social capital in Twitter conversations among teachers. Teaching and Teacher Education, 60, 215–223. Scholar
  92. Rienties, B. (2010). Understanding social interaction in Computer-Supported Collaborative learning: the role of motivation on social interaction (Ph.D. manuscript, Océ Business Services, Maastricht) (978-90-5681-328-4, 841).Google Scholar
  93. Rienties, B. (2019). Powers and limitations of MMSNA: Experiences from the field of education. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  94. Rienties, B., Giesbers, B., Tempelaar, D. T., Lygo-Baker, S., Segers, M., & Gijselaers, W. H. (2012). The role of scaffolding and motivation in CSCL. Computers & Education, 59(3), 893–906. Scholar
  95. Rienties, B., & Hosein, A. (2015). Unpacking (in)formal learning in an academic development programme: A mixed method social network perspective. International Journal of Academic Development, 20(2), 163–177. Scholar
  96. Rienties, B., Johan, N., & Jindal-Snape, D. (2015). Bridge building potential in cross-cultural learning: A mixed method study. Asia Pacific Education Review, 16, 37–48. Scholar
  97. Rienties, B., & Kinchin, I. M. (2014). Understanding (in)formal learning in an academic development programme: A social network perspective. Teaching and Teacher Education, 39, 123–135. Scholar
  98. Rienties, B., & Tempelaar, D. T. (2018). Turning groups inside out: A social network perspective. Journal of the Learning Sciences, 27(4), 550–579. Scholar
  99. Rienties, B., Tempelaar, D. T., Giesbers, B., Segers, M., & Gijselaers, W. H. (2014). A dynamic analysis of social interaction in Computer Mediated Communication; a preference for autonomous learning. Interactive Learning Environments, 22(5), 631–648. Scholar
  100. Rienties, B., Tempelaar, D. T., Pinckaers, M., Giesbers, B., & Lichel, L. (2012). The diverging effects of social network sites on receiving job information for students and professionals. In E. Coakes (Ed.), Technological change and societal growth: Analyzing the future (pp. 202–217). Hershey, PA: IGI Global.CrossRefGoogle Scholar
  101. Rienties, B., Tempelaar, D. T., Van den Bossche, P., Gijselaers, W. H., & Segers, M. (2009). The role of academic motivation in Computer-Supported Collaborative Learning. Computers in Human Behavior, 25(6), 1195–1206. Scholar
  102. Rienties, B., Tempelaar, D. T., Waterval, D., Rehm, M., & Gijselaers, W. H. (2006). Remedial online teaching on a summer course. Industry and Higher Education, 20(5), 327–336. Scholar
  103. Roberts, C., Daly, M., Held, F., & Lyle, D. (2017). Social learning in a longitudinal integrated clinical placement. Advances in Health Sciences Education, 22(4), 1011–1029. Scholar
  104. Roxå, T., & Mårtensson, K. (2009). Significant conversations and significant networks – exploring the backstage of the teaching arena. Studies in Higher Education, 34(5), 547–559. Scholar
  105. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67.CrossRefGoogle Scholar
  106. Sarazin, M. (2019). Ethnographic mixed methods social network analysis studies: Opportunities and challenges. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  107. Schellens, T., & Valcke, M. (2005). Collaborative learning in asynchronous discussion groups: What about the impact on cognitive processing? Computers in Human Behavior, 21(6), 957–975.CrossRefGoogle Scholar
  108. Schoonenboom, J., Johnson, R. B., & Froehlich, D. (2018). Combining multiple purposes of mixing within a mixed methods research design. International Journal of Multiple Research Approaches, 10(1), 271–282.CrossRefGoogle Scholar
  109. Scott, J. P. (2012). Social network analysis: A handbook (3rd ed.). London: Sage Publications Ltd..Google Scholar
  110. Sharples, M., Adams, A., Alozie, N., Ferguson, F., FitzGerald, E., Gaved, M., et al. (2015). Innovating pedagogy 2015. Retrieved from Milton Keynes:
  111. Sharples, M., Adams, A., Ferguson, R., Gaved, M., McAndrew, P., Rienties, B., et al. (2014). Innovating pedagogy 2014. Retrieved from Milton Keynes:
  112. Sharples, M., de Roock, R., Ferguson, R., Gaved, M., Herodotou, C., Koh, E., et al. (2016). Innovating pedagogy 2016: Open University innovation report 5. Retrieved from Milton Keynes:
  113. Sharples, M., McAndrew, P., Weller, M., Ferguson, R., FitzGerald, E., Hirst, T., & Gaved, M. (2013). Innovating pedagogy 2013. Retrieved from Milton Keynes.
  114. Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167. Scholar
  115. Tempelaar, D. T., Rienties, B., Kaper, W., Giesbers, B., Van Gastel, L., Van de Vrie, E., et al. (2011). Effectiviteit van facultatief aansluitonderwijs wiskunde in de transitie van voortgezet naar hoger onderwijs (effectiveness of voluntary remedial education in mathematics to facilitate the transition from secondary to higher education). Pedagogische Studiën, 88(4), 231–248.Google Scholar
  116. Tempelaar, D. T., Rienties, B., Mittelmeier, J., & Nguyen, Q. (2018). Student profiling in a dispositional learning analytics application using formative assessment. Computers in Human Behavior, 78, 408–420. Scholar
  117. Thomas, L., Tuytens, M., Devos, G., Kelchtermans, G., & Vanderlinde, R. (2019). Unpacking beginning teachers’ collegial network structure: A mixed-method social network study. In D. Froehlich, M. Rehm, & B. Rienties (Eds.), Mixed methods approaches to social network analysis. London: Routledge.Google Scholar
  118. Vallerand, R. J., Pelletier, L. G., Blais, M. R., Brière, N. M., Senécal, C., & Vallières, E. F. (1992). The academic motivation scale: A measure of intrinsic, extrinsic, and amotivation in education. Educational and Psychological Measurement, 52, 1003–1017.CrossRefGoogle Scholar
  119. Van den Bossche, P., & Segers, M. (2013). Transfer of training: Adding insight through social network analysis. Educational Research Review, 8, 37–47. Scholar
  120. Van Waes, S., De Maeyer, S., Moolenaar, N. M., Van Petegem, P., & Van den Bossche, P. (2018). Strengthening networks: A social network intervention among higher education teachers. Learning and Instruction, 53, 34–49. Scholar
  121. Van Waes, S., Van de Bossche, P., Moolenaar, N. M., De Maeyer, S., & Van Petegem, P. (2015). Know-who? Linking faculty’s networks to stages of instructional development. Higher Education, 70(5), 807–826. Scholar
  122. Veerman, A. L., & Veldhuis-Diermanse, E. (2001). Collaborative learning through computer-mediated communication in academic education. In P. Dillenbourg, A. Eurelings, & K. Hakkarainen (Eds.), European perspectives on computer-supported collaborative learning: Proceedings of the 1st European conference on computer-supported collaborative learning (pp. 625–632). Maastricht: University of Maastricht.Google Scholar
  123. Vera, E. R., & Schupp, T. (2006). Network analysis in comparative social sciences. Comparative Education, 42(3), 405–429. Scholar
  124. Wassermann, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  125. Watson, W. R., Watson, S. L., & Reigeluth, C. M. (2015). Education 3.0: Breaking the mold with technology. Interactive Learning Environments, 23(3), 323–343. Scholar
  126. Zhang, M. (2015). Internet use that reproduces educational inequalities: Evidence from big data. Computers & Education, 86, 212–223. Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of ViennaViennaAustria
  2. 2.Pädagogische Hochschule WeingartenWeingartenGermany
  3. 3.Open UniversityMilton KeynesUK

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