Websci 2018
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This document summarizes a guest lecture about using web science for educational research. It discusses (A) educational data mining of log files and textbooks to analyze learning behavior, (B) analyzing the educational blogosphere to understand topics of discussion over time, (C) applying social network analysis to study classroom interactions and teacher networks, (D) using gamification in online math practice, (E) intelligent tutoring systems to provide feedback based on student results, and (F) tools for data analysis including R, RapidMiner, and network analysis software. The talk provides examples and proposals for applying these methodologies to better understand learning from digital educational resources and social interactions.
Websci 2018
- 1. WEB SCIENCE GUEST LECTURE 2018 Using Web Science for Educational Research Dr Christian Bokhove
- 2. About me • Dr Christian Bokhove • Associate Professor in Mathematics Education at Southampton Education School • Background maths and computer science, teacher secondary school for years • Use of ICT to support learning • Use of technology for analysing learning • Research methodology. I especially like to integrate computer science with social science methodology.
- 3. Aim of this presentation Impression of various topics working on A. Educational datamining: mining log files, automatic text book analysis, inspection reports B. Analysing the edu blogosphere C. Social network analysis (classrooms, organizations) D. Gamification E. Intelligent Tutoring Systems (mathematics) F. Methods and tools for data analysis
- 4. 4 A. Educational Data Mining • “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.” • www.educationaldatamining.org
- 5. Educational datamining • Log files, electronic books, student actions • What can we say about their ‘learning behaviour’? Or about textbooks? • Learning Analytics/datamining: using algorithms to explore this • But I prefer to look at it somewhat broader
- 6. Example 1 • Log files with student activity (PhD student Chris Osborne) • Classification, predictions (e.g. bored, on-task, Ryan Baker has done a lot of work on this) • European project where Learning Analytics important role in maths digital textbooks USING RAPIDMINER
- 9. Example 2 • Online resources or a textbook (non-edu: tweets) • Can we extract its meaning? • e.g. Latent semantic analysis USING R
- 12. • Web scraper • Mass download • Convert from pdf • Stop-words, stemming etc
- 13. • Sentiment analysis • Topic modelling with Latent Dirichlet Allocation link
- 14. B. Analysing the edu blogosphere • Sarah Hewitt, PhD student (contents used with permission) • Blog at https://sarahhewittsblog.wordpress.com/ • “My corpus is a sample of 9,262 blog posts gathered last year.” • “to see what Edu-professionals talk about, and to see if the topics they discuss change over time.”
- 15. Technical side
- 16. Education side
- 18. Challenging at times “Teachers and other Edu-professionals, Gods-damn them, like to be creative and cryptic when it comes to titling their blogs, and they often draw on metaphors to explain the points they’re trying to make, all of which expose algorithms that reduce language to numbers as the miserable , soulless and devoid-of-any-real-intelligence things they are. How very dare they.”
- 19. Comments • A lot of digital resources are produced used in education; they create a lot of data (‘Big Data’) • What can we learn from their contents and usage? • Can be applied to multiple disciplines (For me: education) and types of data. • Highly interdisciplinary: on one hand need the skills to extract and analyse, on the other hand you need domain knowledge to interpret
- 20. C. Social Network Analysis • Started with social sciences (eg friendship networks) • Then Watts and Strogatz took it into physics • Now very multidisciplinary • See Freeman (2004) for the history • Dynamic Social Network Analysis: over time, statistical models, simulations, animations Freeman, L.C. (2004). The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical Press.
- 21. Example • Using tools o R o Gephi o nodeXL o UCInet o Pajek o …. McFarland, D.A. (2001). Student Resistance: How the Formal and Informal Organization of Classrooms Facilitate Everyday Forms of Student Defiance. American Journal of Sociology107(3), 612-78. Moody, J., McFarland, D.A., & Bender-deMoll, S. (2005). Dynamic Network Visualization: Methods for Meaning with Longitudinal Network Movies. American Journal of Sociology, 110, 1206-1241. Snijders, T.A.B. (2001). The Statistical Evaluation of Social Network Dynamics.Sociological Methodology, 31(1).361-395.
- 22. SNA for classroom interaction • Case to use SNA for classroom interaction • Making it dynamic • Classroom interaction (Moody, McFarland, & Bender-deMoll, 2005) • Technological and methodological advances • Observation apps • Video recording easier • Statistical techniques and packages to capture temporal aspects like Gephi, ERGMs, Rsiena, Statnet, Relevent
- 23. This project • Use dynamic social network analysis to describe classroom interaction • Data analysis and visualization software • Gephi 0.8.2 beta • R and Rstudio with the packages statnet (Handcock, Hunter, Butts, Goodreau, & Morris, 2008) and ndtv (Bender-deMoll, 2014)
- 24. Observation apps
- 25. Other examples • Longitudinal development of Maths and Science trainee teachers’ peer networks (with Dr Chris Downey). • Work by webscience PhD student Nic Fair on Personalised Learning Networks (PLN).
- 26. Comments • Use existing methodologies from Social Network Analysis (SNA) and apply them to social sciences (here: education) • Use existing metrics from SNA to explore (community) patterns and new metrics (for me: for educational effectiveness, for example) • Project proposal to apply SNA to educational context • Logical application might be: interactions between students in online environments, the role of social media in maintaining professional teacher networks, Personal Learning Networks (Nic Fair)
- 27. D. Gamification • Trying to use game principles for learning • Word of warning • Involved in project online Times Tables practice • Gamified environment • Competitive element • How does this influence times tables skills • Insight how times tables acquired at scale
- 28. Scoring serious educational games fairly • PhD student Clare Walsh
- 29. AAA games?
- 30. Can also be more simple via http://is.gd/kes2017
- 31. 31 E. Intelligent Tutoring Systems Store student results, and use these as a teacher to study misconceptions and for starting classroom discussions students
- 32. Feedback: worked examples and hints IDEAS feedback, webservice with Jeuring et al
- 34. F. Methods and tools for data analysis • There are a lot of data analysis tools out there • Open Science Framework • Replicability network • Although I use many, I try to focus on open source tools • Would also love to build capacity for all these tools we use o R o Rapidminer o nodeXL, Gephi, UCInet, Pajek o etc.
- 36. Thank you • Interested in any of these or other webscience topics related to education? • Questions? • Twitter: @cbokhove • Blog: www.bokhove.net (only blog sporadically ;-)