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A2 DataDive 2012

A2 DataDive 2012. Project: African Health OER Network (AHON). g roup: content-focus. all content in this presentation is licensed under a Creative Commons Attribution CC:BY license. original sources:. RESEARCH QUESTIONS: How often are the resources being accessed? From where? When?

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A2 DataDive 2012

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  1. A2 DataDive 2012 Project: African Health OER Network (AHON) group: content-focus all content in this presentation is licensed under a Creative Commons Attribution CC:BY license.

  2. original sources: • RESEARCH QUESTIONS: • How often are the resources being accessed? From where? When? • Can we see geographically how the resources are being disbursed? • How actively engaged are our content creators? • Etc. • DATA • Google Analytics data • Youtube analytics/stats – geographic, text fields, engagement • CiviCRM data on material creators, events • Detailed word doc describing data

  3. two groups: & INDIVIDUALS (Gin Corden, Lettie Malan, Rodger Devine, MandarGokhale, Kathleen Omollo [client]) TOOLS USED: - R • Convert from CVS to Pajek • Pajek • GUESS • GEFI • Google Fusion CONTENT (Jude Yew, Brian Vickers, Derrick Lin, Whitney K, Lidia, Tawfig, Jackie Cohen, Kathleen Omollo [client]) TOOLS USED: • R • SAS • SPSS • Excel • Python

  4. several projects overall • word frequency • word frequency by video, content by video • top 10 Youtube Videos – engagement by country • site traffic trends • viewers’ gender and age trends

  5. tools used • R, and various R libraries • GraphViz • .csv files and text input • SAS • SPSS • Excel • Python • Wordle • …and various knowledge/ideas/energy

  6. specific output • Visualizations of word frequency in Youtube comments • Plots and boxplots of engagement types by country and continent • Charts of site traffic trends • KPI (Key Performance Indicator) charts • Beginnings of R and Python scripts to produce data that may be used for new visualizations and statistical analyses

  7. visualization of greatest word frequency in AHON Youtube video comments – from wordle.com

  8. video comment word frequency – stacked histogram (R, ggplot)

  9. engagement in top 10 youtube videos

  10. some take-away points • There may be different values attached to different forms of engagement in different areas of the world – meaning different takeaways from content analysis • AHON can look at trends of language in comments (for example) by video  Access to answers to questions like: what videos are people most outwardly grateful for? In what videos is the content being most discussed, and which content? • With access to scripts like these, AHON in turn has access to data which can more easily be displayed and analyzed

  11. questions for further research • What does the variety of engagement with video content by geography suggest? • Can site traffic and time depth information be measured more accurately or should it be measured differently? • Is there surprising data regarding gender, age, demographic information with respect to engagement with content? • How can AHON best use increased knowledge about network connections in combination with content engagement and views?

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