9.1 Week 9 Activities

  • Read, Examine & Share, due by Saturday March 30, 5PM
  • Comment, due by Monday April 1, 5PM

9.1.1 Read, Examine & Share

Choose: A research article that is/seems highly relevant to your project idea (a list is offered below if you cannot identify one by yourself). Record its bibliographic information so that you can share with the class. Ideally this article could serve as a model text for your ‘Project Final Artifact’, especially if you plan to develop something publishable from this course.

Examine this article closely, focusing on the following areas: 1) research purposes served by SNA in the study; 2) specific SNA techniques applied; 3) how SNA is combined with other methodology.

Share your thoughts as a Slack Post on the assignments channel. In this post, add key take-aways from this article that may inform your own research project. This Slack post is due by Saturday March 30, 5PM.

9.1.2 Comment

Spend time reading each others’ posts. When you’re reading, try your best to ‘borrow’ ideas from articles introduced by others to inform your own project. Make sure you credit a colleague’s contribution to your thinking by commenting on her post.

Please try to comment on at least 2 classmates’ posts. Comments are due by Monday March 27, 11:59PM.

9.1.3 Lists of articles to choose from

Note: Ideally you will choose a reading by yourself that is pertinent to your project idea.

Examples from the LAK conference

  • Qiao, C., & Hu, X. (2019). Measuring Knowledge Gaps in Student Responses by Mining Networked Representations of Texts. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 275–279). New York, NY, USA: ACM. https://doi.org/10.1145/3303772.3303822
  • Wise, A. F., & Cui, Y. (2019). Top Concept Networks of Professional Education Reflections. In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (pp. 260–264). New York, NY, USA: ACM. https://doi.org/10.1145/3303772.3303840
  • Gardner, J., & Brooks, C. (2018). Coenrollment Networks and Their Relationship to Grades in Undergraduate Education. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 295–304). New York, NY, USA: ACM. https://doi.org/10.1145/3170358.3170373
  • Boroujeni, M. S., Hecking, T., Hoppe, H. U., & Dillenbourg, P. (2017). Dynamics of MOOC discussion forums. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference on - LAK ’17 (pp. 128–137). New York, New York, USA: ACM Press. https://doi.org/10.1145/3027385.3027391
  • Poquet, O., Dawson, S., & Dowell, N. (2017). How Effective is Your Facilitation?: Group-level Analytics of MOOC Forums. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 208–217). New York, NY, USA: ACM. https://doi.org/10.1145/3027385.3027404
  • Wise, A. F., Cui, Y., & Jin, W. Q. (2017). Honing in on social learning networks in MOOC forums: examining critical network definition decisions. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference (pp. 383–392). ACM. https://doi.org/10.1145/3027385.3027446

List of articles (most of them are from Week 3)

  • Chen, B., & Huang, T. (2019). It is about timing: Network prestige in asynchronous online discussions. https://osf.io/preprints/socarxiv/wvjpd/
  • Chen, X., Vorvoreanu, M., & Madhavan, K. (2014). Mining Social Media Data for Understanding Students’ Learning Experiences. IEEE Transactions on Learning Technologies, 7(3), 246–259. https://doi.org/10.1109/TLT.2013.2296520
  • 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.
  • Baker-Doyle, K. (2010). Beyond the Labor Market Paradigm: A Social Network Perspective on Teacher Recruitment and Retention. Education Policy Analysis Archives, 18, 26.
  • Heck, R. h., Price, C. l., & Thomas, S. l. (2004). Tracks as Emergent Structures: A Network Analysis of Student Differentiation in a High School. American Journal of Education , 110(4), 321–353.
  • González Canché, M. S., & Rios-Aguilar, C. (2015). Critical Social Network Analysis in Community Colleges: Peer Effects and Credit Attainment. New Directions for Institutional Research, 2014(163), 75–91. https://doi.org/10.1002/ir.20087
  • Hill, M. (2002). Network Assessments and Diagrams: A Flexible Friend for Social Work Practice and Education. Journal of Social Work , 2(2), 233–254.
  • Christley, R. M. (2005). Infection in Social Networks: Using Network Analysis to Identify High-Risk Individuals. American Journal of Epidemiology, 162(10), 1024–1031. http://doi.org/10.1093/aje/kwi308
  • Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357(4), 370–379.
  • Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technology, 27(6), 924–942.
  • Honeycutt, T. (2009). Making Connections: Using Social Network Analysis for Program Evaluation. Mathematica Policy Research, (1), 1–4.
  • Rienties, B., Héliot, Y., & Jindal-Snape, D. (2013). Understanding social learning relations of international students in a large classroom using social network analysis. Higher Education, 66(4), 489–504. http://doi.org/10.1007/s10734-013-9617-9
  • Martinez, A., Dimitriadis, Y., Rubia, B., Gómez, E., & de la Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers & Education, 41(4), 353–368.
  • Roberson, Q. M., & Colquitt, J. A. (2005). Shared and Configural Justice: A Social Network Model of Justice in Teams. Academy of Management Review. Academy of Management, 30(3), 595–607.