Simulating learning networks in a higher education blogosphere – at scale


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Blogging has become mainstream (even in HE), but building and sustaining dispersed cross-institutional learning networks is still difficult. Large and longitudinal validation trials are costly and resource intensive. A possible way out is introduced in this presentation: a simulation model of a Higher Education blogosphere. With this model I analyse the impact of a new educational intervention model and a new blog management component. The simulation predicts increased density and reciprocity.

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  • Autistic social software? Dana Boyd! => message = autistic software, not facilitating collaboration but individual publishing.
  • Simulating learning networks in a higher education blogosphere – at scale

    1. 1. Simulating learning networks in a higher education blogosphere – at scale Fridolin Wild 1) Steinn Sigurdarson 2) 1) KMi, The Open University 2) University of Iceland [email_address]
    2. 2. Simulating an educational blogosphere? <ul><li>Blogging has become mainstream (even in HE) </li></ul><ul><li>Dispersed cross-institutional learning networks: still difficult </li></ul><ul><li>Large & longitudinal validation trials: too costly and resource intensive </li></ul><ul><li>Way out: simulation model of HE blogosphere </li></ul><ul><li>Analyse impact of new educational intervention model and new blog management facilities </li></ul><ul><li>Simulation predicts increased density & reciprocity </li></ul>
    3. 3. Structure of this talk
    4. 4. The Blogosphere <ul><li>Difficult to assess with statistics and demographics </li></ul><ul><li>Early studies: bursty evolution (Kumar et al., 2003): in scale, community structures, and connectedness </li></ul><ul><li>More recent years: stagnation in growth or even decline, due to rise of social media alternatives (Arnold, 2009; Economist, 2010) </li></ul><ul><li>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) </li></ul>
    5. 5. The Educational Blogosphere <ul><li>Even more difficult to assess </li></ul><ul><li>Contradicting reports: </li></ul><ul><ul><li>Technorati: of the 7,200 surveyed (professional) bloggers: 2-7% students (Sobel, 2010) </li></ul></ul><ul><ul><li>Other studies: higher share of learners, although often not blogging for learning purposes </li></ul></ul><ul><ul><ul><li>57.5% of authors are pupils & students (Herring et al., 2004) </li></ul></ul></ul><ul><ul><ul><li>70.4% personal journals , clearly less for filtering & knowledge sharing (Herring et al., 2004) </li></ul></ul></ul><ul><ul><ul><li>Schmidt & Mayer (2007): todays k-loggers are workers , rarely pupils & students </li></ul></ul></ul>
    6. 6. Problems in Blogging for Learning <ul><li>Notorious fragmentation of conversations </li></ul><ul><li>Long response times (good? bad?) </li></ul><ul><li>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 </li></ul><ul><li>Low number of comments: 0.3 in average, majority none </li></ul><ul><li>Multimodality puzzles: comments as comment, comment in own blog, … </li></ul><ul><li>Fuzziness of the audience </li></ul>(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
    7. 7. 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)
    8. 8. FeedBack: Management Component Wild (Ed.), 2008 offer notify accept
    9. 9. 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.
    10. 10. Validation: Promising, but… <ul><li>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 . </li></ul>
    11. 11. The Model: Subscriptions
    12. 12. The Model: Posts
    13. 13. The Model: Posting <ul><li>In each iteration (1 day = 1 tick), the number of postings assumed for each vertex would be created, following his average post quotient. </li></ul><ul><li>The postings then would be immediately transferred to the subscribing vertices. </li></ul><ul><li>Old postings would be removed from the feed, when they would be older than t-postingdeath (per default set to 105 days=ticks). </li></ul>
    14. 14. The Model: (Un-)Subscribing <ul><li>Add subscriptions: </li></ul><ul><ul><li>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!) </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><li>Remove subscriptions: </li></ul><ul><ul><li>Subscriptions randomly die out with the frequency p-subscriptiondeath. </li></ul></ul><ul><ul><li>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. </li></ul></ul>
    15. 15. Results of Four Simulations <ul><li>10 universities, 10-20k students, 10-20 disciplines = 10.000 students / discipline </li></ul><ul><li>3% bloggers = 300 individuals </li></ul><ul><li>Period of one year </li></ul><ul><li>SIM 1 > typical Higher Education blogosphere </li></ul><ul><li>SIM 2 > increased awareness and disburdened mutual subscription, as made possible with the FeedBack management component </li></ul><ul><li>SIM 3 > adds modelling of courses: establishment of weak ties among a smaller number of individuals </li></ul><ul><li>SIM 4 > counter check: courses, no FeedBack </li></ul><ul><li>Last one: not fully realistic: needs additional efforts of the facilitators or learners, particularly for monitoring. </li></ul>
    16. 16. 1 > Educational Blogosphere
    17. 17. 2 > Reciprocity-enabled Add subscription method based on p_reciprocity=0.03
    18. 18. 3 > Reciprocity-enabled + courses Reciprocity subscription and 1 week course injection every 12 weeks With 3 facilitators scaffolding 16 students each to subscribe to themselves and to mutually subscribe to each other in groups of 4 Additionally: 1 week of higher p_reciprocity of 0.3, then decreased p_reciprocity of 0.01
    19. 19. 4> Courses, no reciprocity support
    20. 20. Summary <ul><li>Simulation model seems realistic (cf. actual smaller trials) </li></ul><ul><li>If realistic </li></ul><ul><li>Then it shows that </li></ul><ul><ul><li>supporting social networking via blogs </li></ul></ul><ul><ul><li>along courses with improved management facilities </li></ul></ul><ul><ul><li>has positive impact on the network density and connectedness </li></ul></ul><ul><ul><li>even when applied to larger numbers of people </li></ul></ul><ul><ul><li>(and: connecting across organisations works, trials already showed that) </li></ul></ul><ul><li>Possible to study effects of new technology & practice before developing & conducting trials </li></ul>
    21. 21. Outlook <ul><li>Extend to cover other social media? </li></ul><ul><li>Test all assumptions more thoroughly </li></ul><ul><li>Implement reading quotient for link death </li></ul><ul><li>Test new practices? New technologies? </li></ul><ul><li>Netlogo code is available as open source </li></ul>
    22. 22. <ul><li>Beware. The end is near. </li></ul>