SOLAR - learning analytics, the state of the art


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On 3 May 2012, the Society for Learning Analytics Research (SoLAR) organised a learning analytics summit. The summit took place in Vancouver, Canada, following the second Learning Ananlytics and Knowledge conference (LAK12). This presentation summarised the state of the art in learning analytics at the time, identifying drivers, challenges, interest groups and future challenges.

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SOLAR - learning analytics, the state of the art

  1. 1. Learning Analytics 2012:Review and Future ChallengesDr Rebecca FergusonThe Open University, UK
  2. 2. Learning analyticsThe measurement, collection,analysis and reporting of dataabout learners and their contexts,for purposes ofunderstanding and optimising learningand the environments in which it occurs. LAK 11 Call for Papers
  3. 3. Driver 1: Big dataFormal: Blackboard, MoodleInformal: Facebook, OpenLearn•Interaction data•Personal data•Systems information•Academic information•Social information
  4. 4. Technical challengeHow can we extract value from thesebig sets of learning-related data?
  5. 5. Driver 2: Online learning Infographic:
  6. 6. Educational challengeHow can we optimise opportunitiesfor learning?
  7. 7. Driver 3: Political concerns The goal of creating an interconnected feedback system would be to ensure that key decisions about learning are informed by data and that data are aggregated and made accessible at all levels of the education system for continuous improvement. US Department of Education, 2010Table:,3746,en_2649_39263238_48634114_1_1_1_1,00.html
  8. 8. Political challengeHow can we substantially improvelearning opportunities and educationalresults at national or international levels?
  9. 9. Three main interest groups Learners Governments and teachers Schools and colleges
  10. 10. Data-driven analyticsUse of web mining techniques•Clustering•Classification•Outlier detection•Association rule mining•Sequential pattern mining•Text mining
  11. 11. Effective better learnersThe goal:Turn learners into effective better learnersFocus on:data mining and machine learning techniques…to enhance web-based learning environments forthe educator to better evaluate the learning process,as well as for the learners to help them in theirlearning endeavour Zaïane, 2001
  12. 12. Learning-focused perspectivesKnowledge is constructedthrough social negotiationLearning takes place innetworks and incommunities of practiceLearning can be scaffoldedby a more able other Dawson, McWilliam, Tan, 2008
  13. 13. Strategic perspectives – 2007/8by 2020 the overall portion of the U.S.workforce with a college degree willbe lower than it was in 2000analytics is emerging as a newtool that can address what seemlike intractable challengesanalytics has the potential toimprove teaching, learning, andstudent success
  14. 14. EDM and analytics split• extend geographical focus• make tools easier for educators to use• standardize methods and data across systems• integrate tools within e-learning environments• develop education-specific mining techniques
  15. 15. Disambiguation Phil Long, George Siemens (2011)
  16. 16. Dividing responsibilityEducational data miningHow can we extract value from these big sets oflearning-related data?Academic (and action) analyticsHow can we substantially improve learningopportunities and educational results at national orinternational levels?Learning analyticsHow can we optimise opportunities for learning?
  17. 17. Meeting the challengeHow can we optimise opportunities for learning?•Maintain focus on this challenge•Learn from previous work in all three fields•Integrate experience from different disciplines•Focus on learners and teachers
  18. 18. Fresh challenges• Widen range of theory-driven approaches• Develop methods of presenting analytics clearly• Adopt standards for the structure and export of data• Broaden focus from higher education• Broaden international focus• Address issues around ethics, privacy and data• Explore possibilities offered by new data sources