Learning Analytics bij de Rijksuniversiteit Groningen

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centrum voor informatie technologie

ews

08-11-2016

Active Learning at UG SURF Onderwijsdagen

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Agenda › slides in English

› educational vision UG

characteristics

short video on EWS

› project Early Warning Signals (EWS)

freshmen

Law, Theology, Science, Sociology

±1000 students

› example from course Calculus

https://youtu.be/3L3f2KpAlzU

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Educational vision UG › 2010-2015 Key elements:

inspire each other (academic knowledge) get best out of each other (ambitions) collaborate and contribute (academic community)

› Successful pilots with educational concepts: research-driven education communities of learners international classroom e-learning

› Results: improved student success (more students more ECTS more

diplomas less time) increased student satisfaction 100% compliant to "prestatieafspraken" top 100 University

http://www.rug.nl/about-us/who-are-we/strategic-plan/strategic-goals/education

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2016-2020 › Innovative education to educate innovators

› Key elements:

Active Learning

Inclusion

› Meaning for students:

Every student prepared for every class

Students become involved, committed and responsible for their own learners path

University cares

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Learning Analytics

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Short video on EWS at UG

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Early Warning Signals

+ signals

+ interventions

Early Warning Signals

1. Academic preparation

• prior education, student characteristics

2. Student performance

• student workload, attempts, grading

3. Student effort

• activity in learning environment

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Project › The gem cannot be polished without friction,

nor man perfected without trials (ZWGG)

› Challenges & Goals to be addressed

› Who are the Stakeholders

› What data are you providing them with

› Stakeholder Actions – what do you expect them to do with the data

› Unexpected Challenges

› Where next for the University of Groningen – other uses of the data?

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Enable to become active › stakeholders (3 out of 7)

student: find out about performance and behaviour compared with peers

instructor: real-time insights into online behaviour

study advisor: identify at-risk students earlier

› infrastructure (data warehouse)

BB Learn + SIS

Blackboard Analytics for Learn (A4L)

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› A Packaged Analytics Application

Transformation

Layer

Best Practices

Defined Metrics

Business Rules

Derived Information

Data

Warehouse

OLAP

Star Schema

Pre-Built

Reports &

Dashboards

Pre-Built Data

Integration

Bb

Learn

Other

ERP

Blackboard Approach A4L

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Calculus › freshmen (Math, Physics, Astronomy)

› ± 300 students (differ in GPA, prior education)

› block of 10 weeks

› start entrée test

› invisible students

› pass rate final test max. 75%

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Student Portal

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Course

2 1

1

2

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Instructor – Grade Center

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Activity & Grade Matrix

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Calculus als docent

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Personal course report

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Calculus als studieadviseur

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Data warehousing

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No or last access

1

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Preliminary results › students

support A4L (96%)

become active in course

some become alert on correct study choice

› instructor

earlier & correct detection invisible students

effective remedial teaching

maybe better grade distribution

› study advisor

earlier contact

study advice (effective studying)

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Next semester › strenghten

educational innovation (course re-design)

actions by 3 stakeholders

› datawarehouse and privacy

increase data quality (ZWGG)

perform Privacy Impact Assessment (PIA)

draft a Code of Practice

› reports and dash boards

analyze historical data

customization

23 | 08-11-2016 centrum voor informatie technologie

ews

Hans Beldhuis, PhD [email protected]