WEB SCIENCE

GUEST LECTURE

Using Web Science for Educational ResearchDr Christian Bokhove

About me

• Dr Christian Bokhove

• Lecturer 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.

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. Times Tables Rockstars. Other games.

E. Tools for data analysis

4

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

A. 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

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

http://is.gd/quadrilaterals

Example 2

• Online resources or a

textbook (non-edu:

tweets)

• Can we extract its

meaning?

• e.g. Latent semantic

analysis

USING R

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

• Web scraper

• Mass download

• Convert from pdf

• Stop-words, stemming

etc

• Sentiment analysis

• Topic modelling with

Latent Dirichlet

Allocation

link

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.”

Technical side

Education side

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.”

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

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

• The rest is 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.

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.

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

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)

Observation apps

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)

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

AAA games?

Can also be more simple

via http://is.gd/kes2017

E. Tools for data analysis

• There are a lot of data analysis tools out there

• 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.

Thank you

• Questions?

• Twitter: @cbokhove

• Blog: www.bokhove.net (blog sporadically ;-)