Learning Analytics and Higher

Education: a brief introduction

Sharon Slade

Overview of session

• Brief background

• How learning analytics is being used in Higher Education

• What the OU is doing

• Things to think about

Our students leave information about themselves every time they interact with usWith no realization or understanding of what we do with that information

So, how do we use that data – and does it matter?

Learning analytics is the measurement, collection, analysis and reporting of data about learners to increase our understanding of them and their learning needs, and to use that understanding to influence their learning.

What do we mean by data about learners?

Background

Disability

Gender

Ethnicity

Learning behaviours

Study history

Learning style

Funding issues

Assignment/test scores

Websites visited

Hitting study milestones

Study goals

Age

Location

Working status

Family income

LanguageLog in frequency

Posting to forums - frequency

Frequency of contact with tutor

Posting to forums - content

What’s going on in learning analytics?• Many universities are using student data to trigger

interventions– Some are automated and direct to students– Others are delivered via the tutor or support staff– Most focus on online engagement, assessment and

demographics• Broader studies are looking at modifying student

learning as well as providing student support• Lots of newer work around social networking and how

students engage with each other (and how that impacts on their success)

Purdue’s Course signals• Uses a predictive model based on

– online activity and assessment scores– Previous academic history and demographic data

• Has created an ‘early warning’ system which– Identifies students ‘at risk’ of not completing a

course– Deploys an intervention to increase chances of

success• System automates the intervention process

– Student gets ‘traffic light’ alert via their online student page, and

– an email/message suggesting corrective action

Knewton (Arizona State Univ)• A continuously adaptive online learning

platform• Logs data about student behaviour and

performance (e.g. keystrokes, scores, speed, etc)

• Analyses behavioural and performance data, comparing it with similar students and assessing relevance of educational content to students

• Serves each individual student the most appropriate learning activity for them at a particular moment in time

University of Maryland’s ‘Check my activity’ Tool

• Allows students to compare their use of the VLE against that of other students

• Results indicate that students with lower usage score less well

• http://www.screencast.com/t/jmZzozpPRZiG

The OU and Student Support Teams

• From Feb 2014, supporting students by curriculum rather than geography

• Ensuring that students get proactive support based on their characteristics and study behaviours

• Underpinned by a standard ‘service level agreement’ to ensure equitable treatment and the maintenance of high standards of support

• The opportunity for development of expertise

The OU Student Support Tool

• A monitoring tool to be used by Student Support Teams

• Pulls in data relevant to the student• Aims to identify and track student progression against

key milestones across a curriculum area• Links to interventions direct to students or via other

staff

Select the SST, quals, pathways, modules, levels, regions of interest

Get a summary view of student numbers

Run a report to identify students who meet certain characteristics

Students aged 50+

Review list of students

Can sort and search each column

Send an intervention if needed

Can opt to exclude certain student types

Student Support Teams Pilots• This approach has been piloted across faculties for the

last few years • Has led to greater understanding of drivers of student

success• Support staff feel more knowledgeable• Improvements in student retention and progression, as

well as increased student satisfaction

So learning analytics can help us to really understand our students.

Sounds great, yes?

Most research has focused on data protection and privacy issues, but is there more to it than that?

What other issues might we be concerned about?

Privacy

Do students appreciate that information is being

gathered about them?

Are we explicit about what we might do with that

information?

Transparency and robustness

Who can see the data collected?

Who can see/influence the models?

How reliable and robust are the models?

Power

Who gets to decide what happens next?

Who can choose which students get more support?

Do teachers, learners, and administrators have the same authority/rights to determine what support is provided?

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Ownership issues

Who can mine our data for other purposes?

Can students opt out of having their information used?

… and what are the consequences of that?

How long is data kept for?

Responsibilities

Is there a shared responsibility to ensure that

information is accurate? Can students opt to

disguise themselves online?

Do we have a responsibility to ensure

equitable treatment of students based on what

we know? (or despite what we know)

Any questions?