Keynote address The power of learning analytics: a need to move - - PowerPoint PPT Presentation

Keynote address The power of learning analytics: a need to move towards new methodologies in education? VU Amsterdam, 13 October 2016 @DrBartRienties Reader in Learning Analytics

A special thanks to Avinash Boroowa, Aida Azadegan, Shi-Min Chua, Simon Cross, Rebecca Ferguson, Lee Farrington-Flint, Christothea Herodotou, Martin Hlosta, Wayne Holmes, Garron Hillaire, Simon Knight, Nai Li, Vicky Marsh, Kevin Mayles, Jenna Mittelmeier, Vicky Murphy, Quan Nguygen, Tom Olney, Lynda Prescott, John Richardson, Jekaterina Rogaten, Matt Schencks, Mike Sharples, Dirk Tempelaar, Lisette Toetenel, Thomas Ullmann, Denise Whitelock, John Woodthorpe, Zdenek Zdrahal, and others…A special thanks to Prof Belinda Tynan for her continuous support on analytics at the OU UK

What is learning analytics?

http://bcomposes.wordpress.com/

(Social) Learning Analytics

“LA 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” (LAK 2011) Social LA “focuses on how learners build knowledge together in their cultural and social settings” (Ferguson & Buckingham Shum, 2012)

Dyckhoff, A. L., Zielke, D., Bültmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and Implementation of a Learning Analytics Toolkit for Teachers. Journal of Educational Technology & Society, 15(3), 58-76.

Agenda? You choose 

1. The power of 151 Learning Designs on 113K+ students at the OU? 2. Analytics4Action: evidence-based interventions? 3. OU Analyse: predictive analytics with automated student recommender? 4. Key drivers for 100K+ student satisfaction? 5. Opportunities of learning analytics/elearning for educational research, teaching practice, and wider policy implications.

So what does the OU do in terms of interventions on learning analytics?

The OU is developing its capabilities in 10 key areas that build the underpinning strengths required for the effective deployment of analytics

Strategic approach

9

11

Analytics4Action framework

Implementation/testing methodologies

• Randomised control trials
• A/B testing
• Quasi-experimental
• Apply to all

Community

• f inquiry

framework: underpinning typology Menu of response actions Methods of gathering data Evaluation Plans Evidence hub Key metrics and drill downs Deep dive analysis and strategic insight

14

Menu of actions

Learning design (before start) In-action interventions (during module) Cognitive Presence

• Redesign learning materials
• Redesign assignments
• Audio feedback on assignments
• Bootcamp before exam

Social Presence

• Introduce graded discussion forum activities
• Group-based wiki assignment
• Assign groups based upon learning analytics

metrics

• Emotional questionnaire to gauge students’

emotions

• Introduce buddy system
• Organise additional videoconference sessions
• One-to-one conversations
• Cafe forum contributions
• Support emails when making progress

Teaching Presence

• Introduce bi-weekly online videoconference

sessions

• Podcasts of key learning elements in the module
• Screencasts of “how to survive the first two weeks”
• Organise additional videoconference sessions
• Call/text/skype student-at-risk
• Organise catch-up sessions on specific topics that

students struggle with

Selected important VLE activities

• Forum (F), Subpage (S), Resource (R), OU_content (O), No

activity (N)

• Possible activity combinations in a week: F, FS, N, O, OF, OFS,

OR, ORF, ORFS, ORS, OS, R, RF, RFS, RS, S

FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS

Start FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS Pass Fail No submit

TMA-1 time

VLE opens Start

Activity space

FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS

FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS Start FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS Pass Fail No submit

TMA-1 time

VLE opens Start

VLE trail: successful student

FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS

FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS Start FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS FS F RFS OFS ORF N O S RF R OF OR ORS ORFS OS RS Pass Fail No submit

TMA-1 time

VLE opens Start

VLE trail: student who did not submit

Probabilistic model: all students

time TMA1 VLE start

Module VLE Fingerprint

Prediction table

Dashboard: Module view

Dashboard: Student view

VLE activities, TMA results, time machine

Nearest neighbours, Predictions with real scores, Personalised recommender

Dashboard: Student view