Learning analytics: the way forward

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Learning analytics: the way ahead

Rebecca FergusonThe Open University

Nordic LASI, 2017

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Learning analytics

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.

Priority areas for education and training

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Open and innovative education and training, fully embracing the digital era.

Strong support for teachers, trainers, school leaders and other educational staff

Relevant and high-quality knowledge, skills and competences developed throughout lifelong learning

Focus on learning outcomes for employability, innovation, active citizenship and well-being and inclusive education, equality, equity, non-discrimination and the promotion of civic competences.

• Our researchers and students shall contribute

insight and communication of knowledge in

public discussions.

• We will challenge the knowledge front and

conventional notions through critical analysis

and knowledge made available to everyone.

• Through research and education, we shall

contribute towards challenging power

structures and promote a diversified and

sustainable society.

• We shall have the reputation of being a

national institution of culture and a crucible for

new ideas, innovation and new ways of

learning.

• We shall be a meeting place for staff, students

and society at large in an attractive arena for

lifelong learning.

• We shall have a strong and vibrant university

democracy characterized by generosity,

openness, diversity and dialogue.

Priority areas for education and training

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• Bringing together different sectors: higher education, schools & workplace learning• Building networks that have outlived the project’s funding period• Helping to develop learning analytics capability• Creating and sharing resources• Developing visions of the future and agreeing how to work towards them

http://www.laceproject.eu/

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http://careers2030.cst.org/jobs/

Preparing for the future

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(Just over) a decade of change

2012: ‘Year of the MOOC’

2007: Launch of the iphone

2006: First tweets

Provocations for visions

of the future

Full report

bit.ly/28X5tq7

Provocation 1: Learners are monitored by their learning environments

Learners are monitored by their learning environments

Just as in bioethics, there are

fundamental human factors at play here

Too much Big Brother vision to

be appealing

I think it is a promising line of work for learning analytics. I think there will be many advances in the use of sensors to

identify aspects that can be applied to

this vision

Provocation 2:Learners’ personal data are tracked

Learners’ personal data are tracked

Wearable sensors are already present, but in

the next future they must be improved,

especially for health purposes, such as

diabetes monitoring or cardiovascular diseases

prevention

Quantified self strongly builds on reflection and self-organisation. This is

good and feasible

A reliable evidence base for the effectiveness of

these measures and some sort of safety control to prevent irresponsible

recommendations

Provocation 3:

Analytics are

rarely used

Analytics are rarely used

There should be strong discussion about the ethical concerns that

apply to each approach to learning analytics

Focus less on analytics to automate, but rather to be

student-centred, to inform the learner and improve their

personal practice. Once analytics provides real, tangible value to the learner, you begin to both

alleviate concerns about privacy AND develop faith that if you can give control of data back to the

learners, they will opt-in to learning analytics.

The research community must concretely show the benefits of the use of data

to improve educational outcomes

Provocation 4:Learners control their own data

Learners control their own data

Absolutely essential.

Organisations must not rely on the data, but try

to build trust with their learners so that learners see

the benefit of sharing

Can we assume that ‘data

owners’ know what to do with

their data? Some people can’t even

manage the money in their

pockets

Users should be entitled to know how their data are

interrogated and used and for what reasons. This

should be made explicit and easy to understand

Greatly limits our ability to effectively use learning

analytics to improve learning for ALL students

Provocation 5:Open systems are widely adopted

Open systems are widely adopted

• Define standards for the information exchange

• Define standards to exploit the information stored

• Define guides about what information is useful

• Define and publish different models to represent the information

Align with corporate and stop thinking that academic is leading!

A multi-pronged approach involving IT services, national

organisations like JISC and ministry of education and

tertiary institutions would all need to be involved

Provocation 6:Learning analytics are essential tools

Learning analytics are essential tools

Very little credible research has demonstrated any real large-scale benefits to learners or institutions

Learning is not only about success is about learning from failure. So, yes, I think that it would be desirable that the

prediction rates are very high, however, I don't think it is the

final goal

What is needed is support for reflection, discussion and debate on the purpose of it

all, especially to curb the excesses of those that see

learning as something teachers do to students. We

need to nurture rich, reflective communities

Provocation 7:Analytics help learners make the right choices

Analytics help learners make the right choices

From bitter experience, I'm aware of the very

slow pace of institutional change

Companies will sell politicians on the

budget savings and lead us here

With learning there is an element that learning is a

process – reducing it to simply outcomes that can be

measured is dangerous

Limited as ignores learning through interactions with both peers and the wider

social environment

Provocation 8:Analytics have largely

replaced teachers

Analytics have largely replaced teachers

Autonomy begets engagement, motivation, persistence, relevance

The collective is as important as the

individual: it is not just about how ‘I’ learn but how ‘we’ learn

Self-directed learning can let students improve a lot according to their needs. But they also need the

instructors to guide them when they are confusing and frustrated during

the learning process

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Learning analytics for European educational policy

LAEP

•What is the current state of the art?

• What are the prospects for the implementation of learning analytics?

• What is the potential for European policy to be used to guide and support the take-up and adaptation of learning analytics to enhance education in Europe?

Action for analytics

Strategy

Research and development

Infrastructure

Context

Standards

Skills

Outreach

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Strategy

Example of a framework for learning analytics: Siemens, G., Gašević, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., Ferguson, R., Duval, E., Verbert, K. & Baker, R.S.J.d. (2011). Open Learning Analytics: An Integrated and

Modularized Platform (Concept Paper). Download from solaresearch.org

• Align work on learning analytics with strategic objectives and priority areas for education and training

• Develop a roadmap for learning analytics

• Assign responsibility for development of learning analytics

• Identify and build on work inrelated areas and other countries

• Build on learning analytics work to develop new priorities

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http://evidence.laceproject.eu

Infrastructure

● Increase data-handling capability

●Create organisational structures to support use of learning analytics

●Use Evidence Hub to identify areas for development

●Develop methods of sharing experience and good practice

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Context

●Align learning analytics work with different sectors of education

●Develop practices that are appropriate to different contexts

● Identify successful financial models

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Standards

●Adapt and employ interoperability standards

●Develop and employ ethical standards, including data protection

●Align analytics with assessment practices

●Develop a robust quality assurance process

●Develop evaluation frameworks

http://www.laceproject.eu/deliverables/d7-1-interoperability-studies/

http://www.open.ac.uk/students/charter/essential-documents/ethical-use-student-data-learning-analytics-policy

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Skills

● Identify the skills required in different areas

●Train and support educators to use analytics to support achievement

●Train and support researchers and developers to work in this field

●Develop and support educational leaders to implement these changes

●Educate learners to use analytics to support their own achievement

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Outreach

●Engage stakeholders throughout the learning analytics process

●Support collaboration between academics and commercial organisations

●Promote awareness of learning analytics

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Research and development

Develop the evidence base

• Develop pedagogy that makes good use of analytics

• Develop analytics that address strategic objectives and priorities

• Develop technology that enables deployment of analytics

• Develop frameworks that enable development of analytics

34https://xkcd.com/1739/

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A problem that we can address

Very little hard evidence is currently available that is based on anything other than short-term studies.

Some positive work is cited in the LACE Evidence Hub but, at this stage, there is no overwhelming evidence that learning analytics have fostered more effective and efficient learning processes and organisations.

However, there is convincing evidence in the Inventory and Case Studies that companies and organisations believe they can do this in the future, and are prepared to invest time and resources in order to achieve this.

36Clow, LAK12, http://oro.open.ac.uk/34330/

http://evidence.laceproject.eu/

http://evidence.laceproject.eu/evidence/evidence-flow-map/

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Slides online at www.slideshare.net/R3beccaF

Rebecca Ferguson @R3beccaFhttp://r3beccaf.wordpress.com/