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A-LASI Getting started in learning analytics (Lockyer, Rogers and Dawson)
The document discusses the importance of learning analytics in higher education, detailing its role in collecting and analyzing data about learners to optimize learning experiences. It highlights current challenges such as fragmented data usage and the need for actionable insights to enhance teaching and student success. The text emphasizes that effective data utilization requires clear purposes, ownership, and contextual understanding to inform decision-making in educational settings.
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A-LASI Getting started in learning analytics (Lockyer, Rogers and Dawson)
- 1. Getting Started with LearningAnalytics Lori Lockyer Tim Rogers Shane Dawson
- 2. What about today? • • • • • • • Introductionsand background From base camp to summit Data – it seems important Analytics for teachers Wrap up Beer and cookies Questions, concerns or issues
- 3. Learning Analytics …is thecollection, collation, analysis and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning
- 4. Learning Analytics Ed theory,Ed practice, SNA, Data mining, Machine learning, semantic, data visualisations, sense-making, psychology (social, cognitive, organisational), learning sciences
- 5. Examine large datasets – trends/ patterns or anomalies. What do patterns indicate and what do changes in habit indicate?
- 6. Current State Higher education: •Lots of isolated work targeting attrition. Very few large enterprise egs. • Commercial – IBM, SAS, Hobsons, D2L Insight, BB analytics
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- 13. Current Focus Pass/Fail, Retention Conceptunderstanding Kentucky: 1.3% - 80 stds approx 400k
- 14. Where next? Beyond dashboards Predictiveand recommender states
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- 18. NLP – videoannotations
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- 20. First steps –the why, what and how of data Improving feedback in mass higher education
- 21. Data, data, everywhere… • Wheredata is accessible it is usually lagged, scattered, indecipherable, requires manipulating, lacks context… • Yes, there are BI reports, but they are mostly for the converted and don’t flag exceptions
- 22. …but not adigit of use • Currently, despite all the data, • Students often don’t know how they are going • Academics don’t know if their teaching is effective • Program/degree owners don’t know how their students navigate their way through • Management don’t know if the Uni is on track
- 23. Outline of data thinkingprocess • What is the purpose for the data? • What data is needed (and who ‘owns’ it) • How to work with the data? • How to make the data actionable?
- 24. Data for what purpose? •Student level support (success and retention) • Educator needs – improving teaching and learning • Program designer/owner needs – curriculum flows • Management/QA requirements – are courses/subjects meeting standards and improving?
- 25. Who owns thedata… • …aka where do you get it? IT, Business Intelligence, Admin? • And others, e.g. class rolls, library data, orientation attendance, in-class formative and summative assessments etc
- 26. Working with data •All data will need various degrees of extraction and transformation • All data needs contextualisation, and a decision about how fine-grained that needs to be • For example, is this a problem?…
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- 31. 42 Student Test score 10% Degree: /100 courseChemical mark Engineering
- 32. 42 Student Test score 10% Degree: Course: /100 courseChemical Shakesp mark Engineering eare and Society
- 33. 42 Student Test score 10% Degree: Course:Class /100 course Chemical Shakesp Position mark Engineering eare and : 1/36 Society
- 34. Making data actionable • Visualisingthe data for summary and exception highlighting • Trends, key junctures, cumulative risk • Tools for action, e.g. CRMs, and business processes
- 35. Visualising critical metrics The workof Stephen Few Context is everything
- 37. Data and analyticsto support learning design and implementation
- 38. Learning Analytics: … measurement,collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. (LAK11 - https://tekri.athabascau.ca/analytics/)
- 39. Where and howdoes learning occur in HE in Australia? • Within courses/units • which are designed predominately by teachers (not instructional designers) • who interact with students as they are learning • who can, may, may not, intervene in the learning process.
- 40. How might auniversity teacher use data and analytics? • Analytics to inform design decisions • Just-in-time analytics to understand learner activity and experience during implementation • Recommendations for learner action • Analytics for post-implementation reflection and revision • Support scholarship of teaching
- 41. What can datahelp us with? • Moe than… – retention/attrition – “… and they liked it” • To are they… – – – – – doing what you intended? understanding the task? on-task/off-task? motivated, engaged? actually learning anything?
- 42. Learning analytics canonly help us answer these questions if they are: - specific to the learning outcomes of the unit - related to how we think learning occurs for such outcomes, in the discipline… - relevant to the learning design we have put in place
- 43. In other words… …the teaching and learning context matters.
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- 45. Case Based LearningDesign adapted from Bennett (2002) available at http://needle.uow.edu.au/ldt/ld/4wpX5Bun.
- 46. LMS log: Studentlog in; access case Network diagram: even pattern of participation Network diagram: teachercentred pattern Network diagram: even pattern of participation Document sharing logs of contribution LMS log: access to teacher feedback LMS log: submission of reflection template Content analysis: depth of reflection Case Based Learning Design adapted from Bennett (2002) available at http://needle.uow.edu.au/ldt/ld/4wpX5Bun.
- 48. LMS log: Studentlog in; access case Network diagram: even pattern of participation Network diagram: teachercentred pattern Network diagram: even pattern of participation Document sharing logs of contribution LMS log: access to teacher feedback LMS log: submission of reflection template Content analysis: depth of reflection Case Based Learning Design adapted from Bennett (2002) available at http://needle.uow.edu.au/ldt/ld/4wpX5Bun.
- 50. LMS log: Studentlog in; access case Network diagram: even pattern of participation Network diagram: teachercentred pattern Network diagram: even pattern of participation Document sharing logs of contribution LMS log: access to teacher feedback LMS log: submission of reflection template Content analysis: depth of reflection Case Based Learning Design adapted from Bennett (2002) available at http://needle.uow.edu.au/ldt/ld/4wpX5Bun.
- 51. Now it isyour turn: Sample design or Your learning design? • • • • • • What are the learning outcomes? What does the design look like? Map it? What do you want to know? What data will inform these? What patterns do you anticipate? What can you do about it?
- 52. Summary • We arealready capturing a lot of data • There’s a lot of information we are not systematically capturing • Current or possible answer might answer our questions • First we have to have relevant questions and know what we are prepared to do with the answers