Current State and Future Trends: A citation network analysis of the Learning Analytics Field

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Citation analysis of learning analytics field from conference proceedings and Journal special issues

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Current State and Future Trends: A citation network analysis of the Learning Analytics Field

  1. 1. S. Dawson D. Gasevic G. Siemens S. Joksimovic Current State and Future Trends: A citation network analysis of the Learning Analytics Field
  2. 2. Goal • Citation analysis and structural mapping to gain insight into the influence and impact within LA – a snapshot of LA through analysis of articles and citations (LAK conferences and special issues)
  3. 3. Context • Although much potential and excitement: – to date LA has served to identify a condition, but has not advanced to deal with the learning challenges in a more nuanced and integrated manner
  4. 4. Aim • Identify emergence of influential trends and hierarchies in the field • Commencement point (Leah): – a foundation for future work – identify promising areas of research – Identify under represented disciplines – Improve integration across disciplines and theory and practice
  5. 5. Context • Learning analytics: – has emerged as a field (maturation) – multi-disciplinary – often mis-represented and poorly understood • (Academic analytics; business intelligence; assessment analytics; social analytics; web analytics; educational data mining)
  6. 6. Approach • Bibliometrics measure the impact/influence of an author or article using various citation analyses • Garfield 1955 (Impact) Ding, Y. (2011). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks, Journal of Informetrics, 5(1), 187-203
  7. 7. Data • LAK11, 12, 13 • Special issues – ETS, JALN, ABS • Data analysis – Citation counts – Author/citation network analysis – Contribution type – Research methods – Author disciplinary background
  8. 8. Approach • Citation and author networks: – Identify the prominent research – Identify linkages between disciplines and authors – Identify diversity of research genres
  9. 9. Citation analysis • The use of citations long been used to measure impact – Core output of research is publications – As research grows, output (papers) further build on other associated works (citations) – “quality” as quantity of citations • Identify areas of prominent research activity
  10. 10. Citation analysis • Criticisms – Cronyism – Self citations – “Rich get richer”
  11. 11. By the way some great refs Gašević, D., Zouaq, A., & Janzen, R. (2013). “Choose Your Classmates, Your GPA Is at Stake!” The Association of Cross- Class Social Ties and Academic Performance. American Behavioral Scientist 57 (10), 1460-1479 Siemens, G. (2013). Learning Analytics The Emergence of a Discipline. American Behavioral Scientist 57 (10), 1380-1400 Dawson, S., Tan, J., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners' creative capacity. Australasian Journal of Educational Technology 27 (6), 924-942
  12. 12. Citation analysis
  13. 13. Citation analysis • Ok Some faults – broadly accepted that quality is linked to the number of citations
  14. 14. Citation analysis • Number of citations (Highly cited articles) – Predominately conceptual and opinion papers (e.g. Educause) – Methods (Wasserman and Faust) – Few empirical studies • Citation and author networks: – Illustrate linkages and disciplines
  15. 15. Example author networks Paper 1: Liz, Julie, Sara Sara JulieLiz
  16. 16. Example author networks Paper 1: Liz, Julie, Sara Sara JulieLiz 2 Degrees
  17. 17. Example citation network Paper 1: Liz, Julie, Sara Cites: Liam & Luka Sara Julie Liz Liam Luka
  18. 18. Citation network LAK conference Journals
  19. 19. Citation networks • Citation network moderate level of clustering – Consistent across LAK proceedings – Few strong connections? – Degrees low – indication of diverse and inconsistent literature sources – Degrees (increasing) from LAK11 to 13
  20. 20. Author networks (LAK)
  21. 21. Author networks (Journal)
  22. 22. Author networks • Author networks – small cliques with few highly connected nodes • For an interdisciplinary field still largely disciplinary clustered
  23. 23. Paper classification • Schema from Info Systems (6 categories) 1. Evaluation research – (e.g. case study empirical) 2. Validation research – (e.g. testing theory/ method/ solution empirical) Glass, R.L., et.al, 2002. Research in software engineering: an analysis of the literature. Information and Software Technology 44, 8, 491-506
  24. 24. Paper classification 3. Solution proposal (solution/ technique to address an issue) 4. Conceptual proposal (e.g. frameworks) 5. Opinion (well its my opinion/argument) 6. Experience (Let me tell you a story) 7. Panel/workshop
  25. 25. Author Background Extensive search – if home dept not listed in author details
  26. 26. Paper classification (LAK) • Dominated by evaluations of research (journals) • Proposal of solution dominates conference
  27. 27. Paper classification (Journal) • Dominated by evaluations of research (journals) • Proposal of solution dominates conference
  28. 28. Paper classification • Dominated by computer science (LAK) • Greater number of education researchers in journals – Reflection of special issues – Reflection of priority sites for publications • Largely conceptual and opinion publications
  29. 29. Methods classification • Qualitative • Quantitative • Mixed methods • Other
  30. 30. Methods classification (LAK) • Other dominates – reflects high number of opinion and conceptual papers
  31. 31. Methods classification (Journals)
  32. 32. Methods classification • Other dominates – reflects high number of opinion, experience and conceptual papers
  33. 33. Conclusions • The field is in its infancy – Citations still predominately opinion and definitional pieces – Clustering and degrees – Few number of empirical studies cited but this is growing • Mature fields greater examples of validation research and importantly critiques of studies
  34. 34. Conclusions • Computer scientists dominate LAK proceedings – Need to look at how other voices are heard • Education research dominates Journals – Reflection of broader priorities?
  35. 35. Conclusions • Early work need to extend • Structural mapping and citation analyses more common and more sophisticated. • Raise awareness – Inform practice – Build connections – Foster further empirical work
  36. 36. Conclusions • Understanding our field we can better advance our field. • Question: To what extent can we use these analyses to architect the development of the field?
  37. 37. Questions • Next steps: –Broader scope (extend network analyses) –Keyword clustering –Citation location –Incorporate multiple citations/ paper
  38. 38. Questions • To what extent can we use these analyses to architect the development of the field? • shane.dawson@unisa.edu.au • dgasevic@acm.org • gsiemens@gmail.com • sreckojoksimovic@gmail.com

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