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ADEPTing to Learning Analytics – Q&A with Dr. David Wilson

The ADEPT Professional Development program provides staff with the opportunity to further develop their capabilities, underpinning knowledge to create and deliver contemporary, student-driven and collaborative learning environments.

We spoke with Dr. David Wilson about the recent ADEPT learning analytics module available for staff:

Q: How will the ADEPT learning analytics module help staff?

Learning analytics can be simple, did a student submit an assignment on time or more complex, did a student read the learning content engaging with their practical – are they coming to the learning opportunity prepared to apply their new found knowledge?

There is a plethora of approaches to teaching, student learning and collection of student learning data; it gets complicated. The ADEPT module provides a well-structured guide of how to gather the data and most importantly some guidance regarding interpretation of the data; what it means and how to use it.  Importantly all examples are authentic.

Q: How will this module enhance course design for academic staff at the University?

Completion of the module will help you better understand when, what, where, why and how your students engage in the diversity of course material you provide.

This can help you tailor your approach to the delivery and release of course content. Importantly knowing how and when your students learn can help you design assessments to encourage positive learning outcomes.

Q: In what ways can this module help the ethical use of student data?

I am up-front with students about all the learning analytics data that I can see but am clear that I only use it to support their learning. For example, knowing that a student engaged with a large module of learning 3 hours prior to the exam – enables me to point out that engaging with content earlier is associated with improved short and long term learning outcomes.

Q: How does this module support the University’s Digital Capabilities framework?

Historically we often made use of the one or two gregarious high achieving students as our in class sounding board, however, these days some of our best students prefer to learn online. Structuring the course to support online learning seems to be the modern way forward that as graduates our students will use in the workplace. The ADEPT module enables one to identify in advance what learning analytics can be measured, and consequently one can design the course with this in mind to enable teachers to identify the effectiveness of teaching and learning approaches.

Q: What are the benefits of this module for academic staff who are interested?

  1. To enable you to better understand your student audience e.g., their concurrent courses, gender mix, domestic/international status, and program of study
  2. How and when students receive learning content you provide, i.e., do they engage is spaced and paced learning or do they cram
  3. To enable you to identify learning content that students find challenging, e.g., this often manifests as repeated interaction with learning content you provide; watching parts of lectures repeatedly or multiple attempts at formative quizzes.
  4. To enable you to stratify data on student learning approach and outcome and advise/ acknowledge specific students regarding effective (and less effective) learning strategies.

If you would like to know how you can use the ADEPT Learning Analytics Module, please contact MyUni Support or (08) 8313 3000.

Dr David Wilson
Is a Cardiovascular research scientist and teaching academic, he has long been interested in patient heterogeneity and personalised/precision medicine. As a teacher he became interested in using data to identify approaches to effective learning and teaching. This led him to become the lead facilitator of the University of Adelaide communities of practice in learning analytics. He invites you to contact him or members of the communities of practice with any questions you might have regarding how to collect and action student data to improve student learning outcome.

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