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Learning Analytics (LA’s), Student Engagement and Retention

Discover the power of learning analytics in promoting student engagement and improving retention rates. Explore the collection, analysis, and reporting of data for optimizing learning environments. Join us for an informative session on how to leverage learning analytics for enhanced student outcomes.

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Learning Analytics (LA’s), Student Engagement and Retention

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  1. Learning Analytics (LA’s), Student Engagement and Retention

  2. Overview of session • Lisa – LAs, engagement & retention (15 mins) • Brett – LA project at Deakin (15 mins) • Questions? (5 mins) • Your turn! (25 mins)

  3. The connections…LAs, engagement & retention LAs, like any data, tell us nothing on their own… What LA’s do we collect, to answer what questions? For what purposes?

  4. LAs a part of strategic response to retention ‘Retention, completion and success: what do we know?’ (Deakin- 2018) The 20 Course Retention Project (2017) http://dteach.deakin.edu.au/wp-content/uploads/sites/103/2019/05/Retention-What-do-we-know-2018.pdf?_ga=2.161257213.1978603766.1558911225-2073488728.1538950765

  5. Deakin’s current context Retention steady for 5 years (79-81%) Cloud campus rate significantly lower Deakin University CRICOS Provider Code: 00113B

  6. Engagement & retention Early engagement (first 6 weeks) is key to retention Timely, targeted with supports embedded in curriculum Best approach – coordinated whole-of institution response

  7. Measurement & metrics Is engagement time on task?, reflecting on task (cognitive), investment in the task (affective)? Is it a product or a process? Measured at Unit, Course, uni level?

  8. Measuring types of individual student engagement

  9. Academic Analytics • “Academic Analytics reflects the role of data analysis at an institutional level, whereas learning analytics centers on the learning process (which includes analyzing the relationship between learner, content, institution, and educator)” • – (Long, Siemans et al. 2011) Deakin University CRICOS Provider Code: 00113B

  10. Learning Analytics • “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs” – (Long, Siemans et al. 2011) More critically, in todays context, it enables users of learning analytics (students, academics, administrators) to take action based on the data – as the learning occurs. Deakin University CRICOS Provider Code: 00113B

  11. Three Levels of Analytics • Student • The Student is categorised as an end user. Their focus is on how LA dashboard can enable them to study and engage better. The dashboard provides insight not only to their own actions but also the broader cohort and cross unit interactions. • Undergraduate/Postgraduate • Full/Part Time • Part of an equity/diversity cohort • Cloud/Located • Domestic /International • Teaching Academic • The Teaching Academic is at the coal face. They are delivering one or more unit. Their focus is on how LA can be used to deliver interventions or gain insight in how their students are engaging with their units and to take action. • Delivering multiple units • High Volume of students • Is Sessional or Contract staff member • Managing Sessional or Contract staff • Part of an equity/diversity cohort • Cloud/Located • Administration • The Administration layer is looking at more than one unit and multiple staff. This role is more interested in a organisational view/trends rather than direct interactions with single or groups of students. • Educational/Instructional Designers • Course Directors • Unit chair leading multiple staff in one or more units • Exploring trends • Managing moderation of assessments Deakin University CRICOS Provider Code: 00113B

  12. If I had asked people what they wanted, they would have said faster horses. • apocryphally attributed to Henry Ford Eadweard Muybridge: TheHorse in Motion 1878 Deakin University CRICOS Provider Code: 00113B

  13. Design and Consistency • Context is Critical while data does not lie it does rely heavily on context to move from a generated data point to actionable insight. • Consistency is Key for learning analytics to pull and action meaningful outputs it must have a consistency of approach within the LMS and related systems. Consistency does not mean the same and LA should not stifle innovation. • Clear Documented Design Processes are crucial as they provide not only a means to interrogate the data coming out of the system but a tool to map and align outcomes (unit, course and graduate) to assessment and regulatory standards. • Dashboards provide a single location to access high level data, drill down to more granular levels, customise user reports and interventions. Deakin University CRICOS Provider Code: 00113B

  14. Over to you! Deakin University CRICOS Provider Code: 00113B

  15. Feedback on todays session & next steps • Provide feedback on the project now or email: Brett.McLennan@deakin.edu.au Deakin University CRICOS Provider Code: 00113B

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