Presentation for webscience students

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

7. http://is.gd/quadrilaterals

9. Example 2
• Online resources or a
textbook (non-edu:
tweets)
• Can we extract its
meaning?
• e.g. Latent semantic
analysis
USING R

11. https://eprints.soton.ac.uk/393849/1/websci_bokhove_FINAL.pdf

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

33. http://is.gd/quadrilaterals

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 ;-)