Small is beautiful: an antidote to big data

Small is beautiful: an antidote to Big Data Stephen Powell & Sheila MacNeill

About this workshop

An opportunity to consider analytics, in particular achievable

ambition to improve and enhance the practice and management

of education:

• Unpacking Analytics (15 mins – pres)

• Raising Questions and Concerns (10 mins - plenary)

• Case Studies & Survey of Analytics in Practice (10 mins – pres)

• Discussing Achievable Ambition (20 mins - round table)

Cetis Analytic Series

http://publications.cetis.ac.uk/c/analytics

• Case Study, Acting on Assessment Analytics

• Case Study, Engaging with Analytics

• Infrastructure and Tools for Analytics

• The impact of analytics in Higher Education on academic practice

• A Brief History of Analytics

• Institutional Readiness for Analytics

• A Framework of Characteristics for Analytics

• Analytics for Understanding Research

• What is Analytics? Definition and Essential Characteristics

• Legal, Risk and Ethical Aspects of Analytics in Higher Education




1. Unpacking analytics

“Analytics is the process of developing actionableinsights through problem definition and the

application of statistical models and analysis againstexisting and/or simulated future data.”


What interest?

• Business Intelligence

• Learning, Teaching and Assessment

However, this is not entirely new…

• Exam boards

• HESA returns

• Extensive use of data in school sector


Suggested purposes across the educational system:

• individual learners

• predictors of students requiring extra support

• functional groups within an institution

• institutional administrators

• enhanced regulation and accountability

• methods and tools to help teachers


Some institutional considerations:

• Why does your institution collect data

• What data is collected

• Where is data collected and stored

• Who has access to data

• When is it available

Provision of data, interpretation and visualisation, taking action…

Ethical and Legal Issues

Stakeholder motivations:

• In assuring educational benefits

• As businesses

• To satisfy expectations

Guiding principles:

• Clarity

• Comfort and care

• Choice and consent

• Consequence and complaint

2. Raising questions and concerns (10 mins)

What do we imagine an educationallymeaningful analytics initiative would look like?

3. Case Study 1: Engaging with Analytics

A richer of understanding of the student journey to scope aSupport system for staff and students. The team beganwith a list of questions:• What is actually happening to students, how can we find out?• What are the touch points with between students and the institution?• What are the institutional "digital footprints" of our students?• What really matters to our students?

(Sheila MacNeill [CETIS] & Jean Mutton [University of Derby])

3. Case Study 1: Engaging with Analytics

Engagement analytics have allowed the team to look "beyond the classroom" and help identify patterns of behavior,both academic and non-academic, that might lead to studentwithdrawal. This has led to insights into how the withdrawal processcould be redeveloped to offer better support to “at risk” students.

3. Case Study 2: Acting on Assessment Analytics

…e-submission and e-marking tools allows the collection of andaccess to far more detailed levels of assessment data…

…using collective data from previous cohorts it is possible to visualizecommon errors and their impact on final marks…

…once the assignment is completed and marked, a follow upworkshop provides a collective view of group performance…

…this opportunity for students to see common mistakes and contextualizetheir own performance within a cohort is proving to be very motivating…

(Sheila MacNeill [CETIS] and Dr Cath Ellis [University of Huddersfield])

3. State of Analytics Survey, UK HE & FE 2013

4. Achievable Ambition (20 mins)

Small group discussion followed by plenary feedback will develop and share improved thinking on practical waysforward for enhancing student experience and outcomes at aninstitutional or sectoral level.

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