Show & TEL 13th Dec 2022

Tuesday 13th December (9.30am – 12pm)
Link to Recording

Join us for the last time this year for openTEL Show & TEL seminar, chaired by Jessica Carr and presentations from Duygu Bektik, Francisco Iniesto, Jenna Mittelmeier, Maria Aristeidou, Fridolin Wild, Koula Charitonos, Andrew Brasher and Paul Astles.

All are welcome!


9:30am – 10:00am: Steering Group – OpenTEL ’23 onwards (Updates from the Chair Eileen Scanlon)

10:00am – 11:30am: Presentations

10:00am – 10:30am: Updates from previous and current OpenTEL fellows

10:30am – 11:30am: Updates from SIG leads and OpenTEL members on current and future projects

11:30am – 12:00pm: Goodbye from Eileen Scanlon


EdTech Forum
Fridolin Wild

Joint webinar series with UNHCR
Koula Charitonos

Module Maps
Andrew Brasher

The role of Learning Design at The Open University in supporting student retention and success
Paul Astles
This talk would begin by framing what the role of a learning designer is at The Open University (OU). We would then move to focus on the work that the OU Learning Design (LD) team have been doing around workload and retention. A brief overview of how real time student feedback is used within module presentation is followed by the impact that course workload, specifically overloaded or unbalanced content, has on student retention and how we use a specialised tool to map workload, activity types and constructive alignment.  We then will discuss the role LDs play in the identification and implication of design decisions, communicating outcomes with module teams and application of the ICEBERG model (this model is used at the OU as a rationale for our approach to impact student retention and success).

First time OU students commented on their own learning analytics data in a research study – how did it go?

These days following a progressive move of Higher Education Institutions towards blended and online environments, accelerated by COVID-19, many universities have access to a greater variety of student data than before. Learning Analytics (LA) provides means for collecting and analysing such evidence. In order to make this analysis useful to the end user, LA data are aggregated in the form of a Learning Analytics Dashboard (LAD). LADs visualise and predict students’ learning progress based on demographics, performance, and digital learning footprints data and have a strong potential to provide useful insights into how teaching and learning may be improved. Continue reading