Although social network analysis and its educational antecedents date back to the early 1900s, the popularity of social networking sites like Twitter and Facebook have raised awareness of and renewed interests in networks and their influence. As the use of digital resources continues expand in education, data collected by these educational technologies has also greatly facilitated the application of network analysis to teaching and learning. This introductory course is designed to prepare education researchers and practitioners to apply network analysis in order to better understand and improve student learning and the contexts in which learning occurs. This course will provide students with an overview of social network theory, examples of network analysis in educational contexts, and applied experience with widely adopted tools and techniques. As participants gain experience in the collection, analysis, and reporting of data throughout the course, they will be better prepared help educational organizations understand and improve both online and blended learning environments.

This course is part of the Graduate Certificate in Learning Analytics Program and is open to all Masters and Doctoral students. ECI 586 Introduction to Learning Analytics and/or prior experience with R and RStudio is recommended for students but not required. For those new to R and RStudio, however, tutorials will be provided.

Number of Credits: 3

Name: Dr. Shaun Kellogg

Title: Senior Director, Program Evaluation and Education Research

Email: [email protected]

Website: https://www.fi.ncsu.edu/people/sbkellog

Location: Friday Institute for Educational Innovation (Office 223)

Phone: (919) 513-8563

Zoom/Calendly/Twitter/GitHub: sbkellogg

Meeting Time: This distance education course is entirely asynchronous. Online tools are utilized throughout the course for communication and interaction. In addition, we may use Zoom for real-time web conferencing, virtual office hours, or whole class discussions. Occasional live sessions will be held as a time agreeable to most students and otherwise recorded for playback by anyone unable to attend.

Virtual Class Locations: This course will be taught online through NC State's Moodle course management platform. Access http://wolfware.ncsu.edu/ and log-in with your Unity ID and password. After logging-in, locate and click on ECI 589 to access the course site.

Office Hours: Any weekday by appointment at calendly.com/sbkellogg/analytics

Course goals for ECI 589: Analyzing Learning Networks are guided by the North Carolina State University motto: Think and Do. Specifically, goals for this course are twofold:

1. Think. Students will deepen their understanding of Social Network Analysis as an emerging approach within the field of Learning Analytics and education research, including its application in a wide range of educational settings. 

2. Do. Scholars will develop proficiency with the processes, tools, and techniques necessary to efficiently, effectively, and ethically apply network analysis to understand and improve learning and the contexts in which learning occurs.

The following learning objectives are aligned with the overarching learning objectives of the Graduate Certificate in Learning Analytics program and are embedded in each unit of the course: 

1. Conceptual Foundations: Describe social network theory (e.g. history, concepts, ethics, etc.) and how it has been applied to address important problems, questions, and issues in education;

2. Data Sources & Measures: Identify and appropriately use network data sources (e.g. social media, online discussion forums, etc.) and associated measures (e.g. centrality, degree, etc.);

3. Tool Proficiency: Efficiently and effectively apply up-to-date software and tools (i.e. R, Quarto, GitHub) to implement LA workflows for preparing, analyzing, and sharing data;

4. Processes & Techniques: Understand and apply analytic processes and network analysis techniques (e.g. sociograms, clustering, ERGMs) in order to understand and improve learning and the contexts in which learning occurs; and, 

5. Communication: Clearly communicate methods, analyses, findings, and recommendations that can provide actionable insight into learning contexts for a range of education stakeholders.

This course is divided into three-week units focused on common techniques associated with social network theory and analysis. Week 1 of each unit consists of course Readings and Discussion designed to introduce key terminology, core concepts, and applications of SNA in Education. The second week of each unit is guided by a network Case Study and focuses on applying network analysis to gain insight into a network data set. During the final week of each unit, students will continue to develop technical skills through either an Independent Analysis for those comfortable students who have completed ECI 586, or R tutorials available through Posit Cloud.

The Social Network Perspective is a gentle introduction to social network theory, analysis and applications in education. The course Readings and Discussion in this unit will help students gain a general understanding of key social network theory, concepts, and applications in education, as well how researchers manage and represent network data. Our unit Case Study: Who's Friends with Who in Middle School is guided by the work of Pittinsky and Carolan (2008) and compares teacher perceptions and student reports of classroom middle school friendship. Finally, the Independent Study provides an opportunity to further explore our middle school datasets and create your own data product that highlights the knowledge and skills gained in Unit 1. Students new to R are required to complete an alternative assignment consisting of R tutorials available through Posit Cloud.

Network Methods and Measures moves beyond basic concepts of network analysis and takes a closer look at the collection, management, and measurement of network data. Our course Readings and Discussion examine the different levels at which social networks can be analyzed, as well as common network measures for describing properties of complete networks. Our unit Case Study: A Tale of Two MOOCs compares discussion networks from two courses using an open educational dataset prepared by Kellogg and Edelman (2015) as part of the Friday Institute's work around Massively Open Online Courses for Educators (MOOC-Eds). Finally, the Independent Analysis provides an opportunity to further explore our MOOC-Ed dataset and create your own data product that highlights the knowledge and skills gained in this. Students new to R are required to complete an alternative assignment consisting of R tutorials available through Posit Cloud.

Groups, Positions and Egocentric Analysis shifts the focus from complete network analysis and zooms in on methods and measures for analyzing groups, positions, and individual actors. Our course Readings and Discussion and case study explore both "top-down" and "bottom-up" approaches to identify a network's groups and extend measures introduced in the previous lab to identify individuals central to the network. Our unit Case Study: Components, Cliques, & Key Actors is inspired by the work of Supovitz et al. who examined groups and key actors that emerged during the intense Twitter debate surrounding the Common Core State Standards. You can learn more about their work on the expansive and interactive website for the #COMMONCORE Project. Finally, the Independent Analysis provides an opportunity to further explore our Twitter dataset and create your own data product that highlights the knowledge and skills gained in this. Students new to R are required to complete an alternative assignment consisting of R tutorials available through Posit Cloud.

Statistical Inference and Network Selection wraps up our work with SNA and examines recent advances in inferential statistics that can be used to make predictions from social network data and test hypotheses we have about a network of interest. Through our course Readings and Discussion, we'll learn about different techniques that make use of simulations to model network data and how these statistical models are used to address questions that more completely reflect the complexity of educational settings. For example, our unit Case Study: Birds of a Feather Lead Together is inspired by the work of Daly and Finnigan (2016) makes use of Exponential Random Graph Models (ERGMs) to examine social processes (e.g. reciprocity and homophily) that might explain how school and district-level leaders select peers for collaboration or confidential exchanges. Finally, the Independent Analysis provides an opportunity to further explore our school leadership datasets and create your own data product that highlights the knowledge and skills gained in this. Students new to R are required to complete an alternative assignment consisting of R tutorials available through Posit Cloud.

1. Housekeeping (4 pts): Students will review Syllabus, access necessary software and post a brief introduction of themselves and respond to their peers. Student who are not in the Learning Analytics Certificate program or have not completed ECI 586: Intro to Learning Analytics will also be required to complete The Basics R Primer accessible through R Studio Cloud. 

2. Readings & Discussion (24 pts): Each unit begins unit introduces terminology, core concepts, and applications of an analytical approach through readings, course videos, and discussion. Students will log in throughout week and engage in informal discussion with other members of our learning community. To help guide discussions, students are provided a set of essential questions to address and are also encouraged to explore their own areas of interest. Provided readings are embedded in each discussion topic and are intended to help address guiding questions and foster a deeper understanding SNA and its application in educational contexts. 

3. Case Studies (24 pts): During the second week of each unit, we will complete a case study to illustrate how Learning Analytics methods and techniques can be applied to address research questions of interest, create useful data products, and conduct reproducible research. Each case study is structured around a basic research workflow modeled after the Data-Intensive Research Workflow from Learning Analytics Goes to School.

4. Data Products (24 pts; Certificate Track): In the final week of each unit, certificate-track students will prepare a brief report or data product on an independent analysis they conducted using an instructor provided dataset or a data source of their choosing. The data product will include key findings and insights gained from the analysis. Students are asked to use an instructor provided rubric to reflect on and self-assess on their data product and will be provided exemplar products from prior course offerings.

5. R Tutorials (24 pts; Non-Certificate Track): In the final week of each unit, non-certificate track students will gain hands-on experience with tools and processes using R. Tutorials and resources will introduce students to importing and wrangling data for analysis, exploring data for interesting patterns or trends, and reporting data in a reproducible way. Full credit will be provided for successful completion of tutorials. 

6. Final Project (24 pts): In lieu of a final exam, students will conduct an independent analysis and develop a "data product" that addresses a research question using some of the SNA approaches we've learned this semester and applied to a dataset of their choosing.

Course grades are based on the following 100 point scale:

A+ (97-100), A (94-96), A- (90-93), 

B+ (87-89), B (84-86), B- (80-83),

C+ (77-79), C (74-76), C- (70-73), 

D+ (67-69), D (64-66), D- (60-63), 

F (59 or less)

Late work is accepted but may be penalized at 15% per week it is late. Assignments submitted by the due date, however, may be revised and resubmitted for a higher grade by the following week.

Course Feedback Expectations: Please contact your instructor via email ([email protected]) with any questions about the course project or other assignments. Your instructor will strive to answer any emails within 24 hours (M-F) and 48 hours on the weekend, and grade submitted assignments within 5-7 days of the due date. In addition, students will be provided ongoing opportunities, and are strongly encourage, to provide course feedback for to help improve the design of current and future courses.

Students must have Internet access and access to a Web browser (e.g., Safari, Firefox, chrome) to participate in this course. The Moodle course site and Web-based software required for completing course projects may only be accessed online. It is strongly recommended that students have high-speed Internet access (e.g., cable modem). 

This course requires R and R Studio or Gephi (linked below) that will be used to provide hands-on experience with the concepts and skills addressed in course readings. R, R studio, and Gephi can be installed on computers running Windows, MacOS, Chrome, and Linux operating systems. 

Finally, students should feel comfortable installing new software programs and navigating unfamiliar graphical user interfaces. It is also recommended that students in this class have some background knowledge of online learning environments (e.g. LMS, MOOCs, etc.) and an understanding of basic descriptive statistics (e.g. distribution, frequencies, mean, variability, etc.).

1. R (https://www.r-project.org) is an open-source language and computing environment for data manipulation, analysis, and visualization. Installation files for Windows, Mac, and Linux can be found at the website for the Comprehensive R Archive Network (CRAN), http://cran.r-project.org/. 

2. R Studio Cloud (https://rstudio.cloud) is an online integrated development environment (IDE) for R which includes an R console, syntax-highlighting editor, and tools for plotting, packages, and workspace management. The R Desktop application can also be downloaded from https://www.rstudio.com/products/RStudio/#Desktop. 

1. Gephi (https://gephi.org) is the leading GUI-based visualization and exploration software for all kinds of graphs and networks. Gephi is open-source and free and runs on Windows, Mac OS X and Linux.

2. DataQuest (https://www.dataquest.io) offers interactive R, Python, Sheets, and SQL courses and tutorials on topics in data science, statistics and machine learning. An email will be sent providing free access to our DataQuest site, full catalogue of courses and resources for 6 months.

3. LinkedIn Learning (https://www.linkedin.com/learning)){.uri} offers tutorials and training courses on R, R Studio, and Tableau. LinkedIn Learning is available at no charge to students.

4. GitHub is a web-based hosting service for version control using Git. You can create an account here: https://github.com 

5. Git is a free and open source distributed version control system. Jenny Bryan's very thorough installation and R Studio set up process for Mac and Windows can be found here: http://happygitwithr.com.

Server Space: NC State is a Google Apps for Education institution. Your Google Drive provides "an infinitely large, ultra-secure, and entirely free bookbag for the 21st century."  This space may be useful for your project work, or you may use a third-party Internet service provider to place your data files and projects online (e.g., Github, Dropbox). In addition, Moodle provides space for storing private files.

1. Carolan, B. V. (2013). Social network analysis and education: Theory, methods & applications. Sage Publications. Digital copy available through the NCSU Library. 

2. R for Data Science is available free online at https://r4ds.had.co.nz 

3. Researcher and Practitioner Articles (see References for potential readings). Provided through NCSU Course.   

1. Daly, A. J. (2010). Social Network Theory and Educational Change. Cambridge, MA: Harvard Education Press. Physical copy available through the NCSU Library.

2. Froehlich, D., Rehm, M., & Rienties, B. (2019). Mixed methods social network analysis: Theories and methodologies in learning and education. Routledge. Digital copy available through the NCSU Library.EG02.20.1.php

• Borgatti, S., Mehra, A., Brass, D. D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(April), 892--5. http://doi.org/10.1126/science.1165821

• Daly, A. J. (2012). Data, dyads , and dynamics : Exploring data use and social networks in educational improvement. Teachers College Record, 114(November 2012), 1--38.

• Christakis, N. (2010). The hidden influence of social networks. Retrieved February 12, 2017, from https://www.youtube.com/watch?v=2U-tOghblfE

• Christakis, N. (2012). The sociological science behind social networks and social influence. Retrieved from https://www.youtube.com/watch?v=wadBvDPeE4E

• Coburn, C. E., & Russell, J. L. (2008). District policy and teachers' social networks. Educational Evaluation and Policy Analysis, 30(3), 203--235. http://doi.org/10.3102/0162373708321829

• Ennis, G., & West, D. (2013). Using social network analysis in community development practice and research: A case study. Community Development Journal, 48, 40--57. http://doi.org/10.1093/cdj/bss013

• Ferguson, R., & Buckingham Shum, S. (2012). Social learning analytics: Five Approaches. In 2nd International Conference on Learning Analytics & Knowledge (pp. 23--33). Vancouver, BC. http://doi.org/10.1145/2330601.2330616

• Foton Labs. (2014). Network Theory: An introduction. Retrieved February 16, 2015, from https://www.youtube.com/watch?v=-ckaLBsCoxo&spfreload=10

• Foton Labs. (2014). Social network analysis: An overview. Retrieved February 16, 2015, from https://www.youtube.com/watch?v=fgr_g1q2ikA&spfreload=10

• 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. Cell Biology Education, 13, 167--178. http://doi.org/10.1187/cbe.13-08-0162

• Golbeck, J. (2013). Visual analysis of social networks. Retrieved from https://www.youtube.com/watch?v=nznUgnrPvTM&spfreload=10

• Golbeck, J. (2013). Network structure. Retrieved from https://www.youtube.com/watch?v=89mxOdwPfxA&spfreload=10

• Hanneman, R., & Riddle, M. (2005).Introduction to social network methods. Retrieved from  

• Hansen, D. L., Schneiderman, B., & Smith, M., (2011). Analyzing social media networks with NodeXL: Insights from a connected world. Burlington, MA: Morgan Kaufmann,. Retrieved from http://catalog.lib.ncsu.edu/record/NCSU3745682

• Kellogg, S., & Edelmann, A. (2015). Massively Open Online Course for Educators (MOOC-Ed) network dataset. British Journal of Educational Technology, 46(5), 977--983. http://doi.org/10.1111/bjet.12312

• Kellogg, S. (2013). A SNA cheat sheet. Handout from and SNA Workshop conducted at the Friday Institute.

• Kellogg, S. (2014). Social learning theories and education: A review of the literature. 

• Kellogg, S. B., Booth, S., & Oliver, K. M. (2014). A social network perspective on peer support learning in MOOCs for educators. International Review of Research in Open and Distance Learning, 15(5), 263--289.

• McFarland, D. A., Diehl, D., & Rawlings, C. (2011). Methodological transactionalism and the sociology of education. In M. T. Hallinan (Ed.), Frontiers in Sociology of Education (pp. 87--109). Dordrecht: Springer Netherlands. http://doi.org/10.1007/978-94-007-1576-9

• Penuel, W. R., Sussex, W., Korbak, C., & Hoadley, C. (2006). Investigating the potential of using social network analysis in educational evaluation. American Journal of Evaluation, 27(4), 437--451. http://doi.org/10.1177/1098214006294307

• Smith, M., et al. (2009). Analyzing (social media) networks with NodeXL. In Proceedings of the fourth international conference on Communities and technologies - C&T '09 (p. 255). New York, New York, USA: ACM Press. http://doi.org/10.1145/1556460.1556497

• Supovitz, J. Daly, A., & Del Fresno, M. (2015, Feb 23). #Commoncore Project. Retrieved from: http://www.hashtagcommoncore.com/

Academic Integrity: Students are bound by the academic integrity policy as stated in the code of student conduct. Therefore, students are required to uphold the university pledge of honor and exercise honesty in completing any assignment. See the website for a full explanation:

http://www.ncsu.edu/policies/student_services/student_discipline/POL11.35.1.php 

N.C. State University Polices, Regulations, and Rules (PRR): Students are responsible for reviewing the PRRs which pertain to their course rights and responsibilities. These include: http://policies.ncsu.edu/policy/pol-04-25-05 (Equal Opportunity and Non-Discrimination Policy Statement), http://oied.ncsu.edu/oied/policies.php (Office for Institutional Equity and Diversity), http://policies.ncsu.edu/policy/pol-1135-01 (Code of Student Conduct), and http://policies.ncsu.edu/regulation/reg-02-50-03 (Grades and Grade Point Average).

University Non-Discrimination Policies: It is the policy of the State of North Carolina to provide equality of opportunity in education and employment for all students and employees. Accordingly, the university does not practice or condone unlawful discrimination in any form against students, employees or applicants on the grounds of race, color, religion, creed, sex, national origin, age, disability, or veteran status. In addition, North Carolina State University regards discrimination based on sexual orientation to be inconsistent with its goal of providing a welcoming environment in which all its students, faculty, and staff may learn and work up to their full potential.

Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with the Disability Resource Office at Holmes Hall, Suite 304, Campus Box 7509, 919-515-7653. For more information on NC State's policy on working with students with disabilities, please see the Academic Accommodations for Students with Disabilities Regulation (REG02.20.01)

For more information on NC State's policy on working with students with disabilities, please see http://www.ncsu.edu/policies/academic_affairs/courses_undergrad/REG02.20.1.php