Using Learning Analytics to Provide Personalised Feedback to Online Learners

Chris Blackmore, University Teacher, ScHARR

01/07/2016 © The University of Sheffield

01/07/2016 © The University of Sheffield

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs

Call for Papers of the 1st International Conference on Learning Analytics & Knowledge (LAK 2011)

01/07/2016 © The University of Sheffield

Why use learning analytics? Data is already being collated

Can we use this productively, e.g. to enhance student experience?

Where is the added value, e.g. can it tell us things we otherwise would not know?

01/07/2016 © The University of Sheffield

01/07/2016 © The University of Sheffield

Understand the variety of data available

01/07/2016 © The University of Sheffield

01/07/2016 © The University of Sheffield

Harvest the data (and clean if necessary)

Analyze (at appropriate level)

01/07/2016 © The University of Sheffield

01/07/2016 © The University of Sheffield

Distribute personalized feedback to learners

01/07/2016 © The University of Sheffield

A worked example Pilot project in 2015-16

From a ScHARR PGT module on e-learning programme

10 students

Data to be analyzed – discussion forum postings

CloutAnalytical thinking

Authenticity

Emotional tone

01/07/2016 © The University of Sheffield

Analytical thinking - Captures the degree to which people use words that suggest formal, logical, and hierarchical thinking patterns

01/07/2016 © The University of Sheffield

Clout - refers to the relative social status, confidence, or leadership that people display through their writing or talking

01/07/2016 © The University of Sheffield

Authenticity - When people reveal themselves in an authentic or honest way, they are more personal, humble, and vulnerable

01/07/2016 © The University of Sheffield

Emotional tone - puts positive and negative emotions into a single summary variable. The higher the number, the more positive the tone. Numbers below 50 suggest a more negative emotional tone.

So what?

So what?Pilot project – data was not presented to students

No evaluation yet, e.g. how helpful was it to students and tutors?

Does it really make a difference to participation, engagement, results or satisfaction?

But…Only worth doing if it fits with learning objectives

VLE issues, e.g. work involved in harvesting, analyzing and distributing data

Type of learning will impact on nature and volume of data available, e.g. not all courses generate enough discussion forum postings for analysis

Ethics

c.m.blackmore@sheffield.ac.uk

@chrisblackmore

http://elearningemotions.blogspot.co.uk/

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01/07/2016 © The University of Sheffield

01/07/2016 © The University of Sheffield

01/07/2016 © The University of Sheffield