Simulating learning networks in a higher education

blogosphere – at scale

Fridolin Wild1) Steinn Sigurdarson2)

1) KMi, The Open University2) University of Iceland

f.wild@open.ac.uk

Simulating an educational blogosphere?

• Blogging has become mainstream (even in HE)• Dispersed cross-institutional learning networks:

still difficult• Large & longitudinal validation trials:

too costly and resource intensive• Way out: simulation model of HE blogosphere• Analyse impact of new educational intervention

model and new blog management facilities• Simulation predicts increased density &

reciprocity

Structure of this talk

The Blogosphere

• Difficult to assess with statistics and demographics• Early studies: bursty evolution (Kumar et al., 2003):

in scale, community structures, and connectedness• More recent years: stagnation in growth or even

decline, due to rise of social media alternatives (Arnold, 2009; Economist, 2010)

• Still: the German ARD/ZDF online study 2010 (Busemann & Gscheidle, 2010): 3% of the population of Germany to maintain or have maintained a blog (compared to 1% who posted on twitter)

The Educational Blogosphere

• Even more difficult to assess• Contradicting reports:– Technorati: of the 7,200 surveyed (professional)

bloggers: 2-7% students (Sobel, 2010)– Other studies: higher share of learners, although

often not blogging for learning purposes• 57.5% of authors are pupils & students

(Herring et al., 2004)• 70.4% personal journals, clearly less for

filtering & knowledge sharing (Herring et al., 2004)• Schmidt & Mayer (2007): todays

k-loggers are workers, rarely pupils & students

Problems in Blogging for Learning

• Notorious fragmentation of conversations• Long response times (good? bad?)• Low number of links: only 51.2% of all blogs link to other

blogs, only 53.7% to other websites, 30.5% not at all• Low number of comments: 0.3 in average, majority none• Multimodality puzzles: comments as comment, comment

in own blog, …• Fuzziness of the audience

(De Moor & Efimova, 2004; Herring et al., 2004; Krause, 2004; Gurzick & Lutters, 2006)

practice and technology support still fall short major obstacle for growth negative effects on the characteristic of educational blogosphere

Educational intervention model

Process view of iCamp approach (redrawn from Fiedler et al., 2009).

(Nguyen-Ngoc & Law, 2009; Law & Nguyen-Ngoc, 2008; Fiedler et al., 2009)

FeedBack: ManagementComponent

Wild (Ed.), 2008

offer

notify

accept

Blog Network Example

Resulting blog network as developed within the second iCamp trials: four universities were involved, bringing together 24 students and five facilitators (see Law & Nguyen-Ngoc, 2008). Within the three-month trial period, 68 offers were made, of which 49 were accepted. 94 requests for subscriptions had been made (see Law & Nguyen-Ngoc, 2008). The figure depicts the students and facilitators as the nodes. Offers, requests for subscriptions, each notification, and replies as directed links. Where two nodes are connected through multiple links, these are conflated and the link colour is darkened. The node size reflects the logarithmised calculated prestige score (Butts, 2009). R code is available from the first author.

Validation: Promising, but…

Even though the final validation trial included 76 students from 11 countries supported by 10 facilitators over a period of 14 weeks (Nguyen-Ngoc & Law, 2009), it remains unclear, what the effects of such approach and the used technology would be at scale.

The Model: Subscriptions

The Model: Posts

The Model: Posting

• In each iteration (1 day = 1 tick), the number of postings assumed for each vertex would be created, following his average post quotient.

• The postings then would be immediately transferred to the subscribing vertices. • Old postings would be removed from the feed, when they would be older than t-

postingdeath (per default set to 105 days=ticks).

The Model: (Un-)Subscribing• Add subscriptions:

– Random subscriptions with a very low probability of p-subscription, per default set to 0.012 (= 1 out of 83 vertices would randomly subscribe to another one). Models e.g. user finding a blog post in a search engine, decides to subscribe (iCamp trial: 0.05, but we have more subscription methods and a longer timeframe!)

– Authority subscriptions: Power law distributions of in-degrees (e.g. Karandikar, 2007)suggests those vertices with already high in-degrees attract more subscriptions, using also p-subscription.

• Remove subscriptions:– Subscriptions randomly die out with the frequency p-subscriptiondeath. – Subscriptions age with a random exponential distribution around the average t-

linkdeath: aging reduces link strength, postings increase it. When a subscription is not very active, i.e. not many postings come through this channel, its probability to die away is higher. Subscriptions that expose a link-strength below one are removed in this step.

Results of Four Simulations• 10 universities, 10-20k students, 10-20 disciplines

= 10.000 students / discipline• 3% bloggers = 300 individuals• Period of one year

SIM 1 > typical Higher Education blogosphereSIM 2 > increased awareness and disburdened mutual

subscription, as made possible with the FeedBack management component

SIM 3 > adds modelling of courses: establishment of weak ties among a smaller number of individuals

SIM 4 > counter check: courses, no FeedBackLast one: not fully realistic: needs additional efforts of the

facilitators or learners, particularly for monitoring.

1 > Educational Blogosphere

2 > Reciprocity-enabled

Add subscription method based on p_reciprocity=0.03

3 > Reciprocity-enabled + courses

Reciprocity subscription and 1 week course injection every 12 weeksWith 3 facilitators scaffolding 16 students each to subscribe to themselves and to mutually subscribe to each other in groups of 4Additionally: 1 week of higher p_reciprocity of 0.3, then decreased p_reciprocity of 0.01

4> Courses, no reciprocity support

Summary• Simulation model seems realistic (cf. actual

smaller trials)• If realistic• Then it shows that

– supporting social networking via blogs – along courses with improved management

facilities – has positive impact on the network density and

connectedness – even when applied to larger numbers of people– (and: connecting across organisations works,

trials already showed that)• Possible to study effects of new technology &

practice before developing & conducting trials

Outlook

• Extend to cover other social media?• Test all assumptions more thoroughly• Implement reading quotient for link death• Test new practices? New technologies?

• Netlogo code is available as open source

Beware. The end is near.