learning elearning

 Attending the sessions for the CRADLE (Centre for assessment and digital learning) conference today and tomorrow. There is a two hour difference in time (NZ is 2 hours ahead) so It will be a late finish today 😊

The conference begins with a welcome from Professors Liz Johnson and David Boud, Deakin University. First CRADLE conference (over 900 participants!!) but the fifth year CRADLE has been running. Professor Boud provided background and rationale for the conference. Professor Johnson provided formal welcome to country and to the conference.

The first keynote (introduced by Phil Dawson) is with Associate Professor (of educational technology and learning science) Alyssa Wise from New York University on ‘The data intensive university in a post-digital world: New directions in teaching and learning.

https://steinhardt.nyu.edu/people/alyssa-wise

Defined learning analytics (LA) as the use of data science methods to generate insights into teaching and learning that lead to direct, impactful action. Important to use LA purposefully to inform how learning is designed, developed, supported, taught etc. Argued that data informed decisions are more important than ever given how online, blended, hybrid etc have moved to mainstream overnight, no longer digital vs traditional.

Shared recent survey across large number of universities. Unfortunately, pre-covid online better experienced than post-covid ‘remote learning’. However, experiences did improve (slightly) over time. Clear structure for learning important but needs to be balanced with flexibility. In general, videos of ‘lectures’ positive but online lectures themselves had mixed reviews, along with online discussions. Important to provide ‘active engagement’ for students.

How can learning analytics help? Begins with instructors design with intention to engage learners, who generate data, provide analytics, read as human insights and feed back into instructors design.

Data can be catergorised as activities, artifacts and associations. It is not only the data, but how is it analysed and applied? Sense making from analytics need to be supported with pedagogical response.

Provided a series of examples. Are my students preparing? When are my students preparing? Are my students engaging? Collected data on activity and used this to inform the  ongoing development of activities to increase engagement. Qualitative data also important – for instance to find out ‘who are my students engaging with? Where is the student spending the most time (asynchronously)? What does the student engage with – content related or non-content related discussion posts?

For instructors, important to find out how they are going. In general, professor responses which are to all levels of queries, providing coahing and supporting and social presence cues have greater engagement with professors who only respond to ‘top level queries which are straight forward.

Another example of how to find students who are struggling using diagnostic assessment with LA used to build a better model of how to identify and support students. However, relationship support (i.e. caring tutor) still most effective!

Interrupted by phone call!! So did not get to follow the last 5 minutes and the Q & A.