LAK12: Siemen’s Educause Presentation

Here are the slides:

Notes, Reflections and Thoughts

  • Academic Analytics: target organizational efficiency, strategy and decision making. (Campbell, Dianne ?)
  • Educational Data-mining: Reducing components and analyzing relationship.
  • Learning Analytics: Systems and wholes that include social components and cognitive elements.
  • Bottom Up: Data collected through traditional learning activities.
  • Top-Down: System wide data collected.
  • Due to the existence of large data, cognitive processes need to use tools to convert them into useable information.
  • Confidence is directly related to (academic) success.
  • Quantified Self: Tracking the self abilities and analysis its data.
  • Precise and accurate information leads to better performance overall all types of organization.
  • In learning, there are many data/methods that exist: EduCause, Student Success researches, Duval, Haythornthwaite, De Liddo & Buckingham Shum (automated vs manual, 70% accuracy), Social learning analytics, Clow & Makriyannis (icebox?)
  • ¬†Privacy and Ethics issues especially when you relate the data to none-learning layers will cause unease and we need manifestos to guide the privacy and ethics layer to minimize negative reaction. There is no research about P, E and analytics.
  • Gold Mine: Organizations [e.g Pearson, Stanford] offers open free learning courses because it offer them priceless free amount of learning data.
  • Learning organization collect massive data. We need (1) figure out a way to find them and (2) We need to relate the data together.
  • Data needs to include: data from outside LMS, from Library, classroom interaction. This required 3 layers to communicate together: Systems/enterprise level + Researchers + Educators.
  • 24% of learning organizations utilize deep analytics (Kron, 2011).
  • The process of handling data: Acquisition, Storage, Cleaning, Integration, Analysis, Representation [Myopic view]
  • Procedures for a systems: Strategy, Planning & Resources allocation, Metrics & Tools, Capacity Development, Systemic change (Click on image to enlarge, Siemens, @ 45:00 min)
  • Capacity Development: will require restructure and redevelopment capacity.
  • Resources found at: www.solaresearch.org
  • Cloud Based Analytic research: SAlgorithm should be open, Student should see what schools sees,
  • Conference in Vancouver: lak12.sites.olt.ubc.ca
  • Starting point for new born analyticians: Initiate the social practice. Other suggestions: use tools (statistics, SPSS, SNAP, …, low threshold tools), begin conversation in the institution to identify chunks of data, Sr. Admin track the procedural matrix, PD a team and the capacity (tab on Educause).
  • Initial questions: (1) Teacher level: why do students do what they do, what network structure contribute to student learning (2) Admin level: what is the impact of resources on learning success -> provide an edge.
  • Collective collaboration: relate and connect to others, organization,¬† state level, develop new tools.

References

Siemans, G. (2012). https://educause.adobeconnect.com/_a729300474/p4xmnq9p9rz/?launcher=false&fcsContent=true&pbMode=normal