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Assessment Analytics Should we do it? If so – what might it look like?

Assessment Analytics

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Should we do it? If so – what might it look like?. Assessment Analytics. Learning Analytics. Emphasis on SNA. Emphasis on SNA. Emphasis on attrition/retention. Attrition rate: single figures (%). Learning Analytics. Assessment. Granularity. Granularity. Assessment Analytics. - PowerPoint PPT Presentation

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Page 1: Assessment Analytics

Assessment Analytics

• Should we do it?• If so – what might it look like?

Page 2: Assessment Analytics

Learning Analytics

NewOperationalisation

Poorly understood Gaps

Ethics

Page 3: Assessment Analytics

Emphasis on SNA

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Emphasis on SNA

Partial

• Most student learning doesn’t happen online

• Most teaching happens in classrooms

Not useful

• ‘Isn’t this interesting and aren’t I clever’

• ‘How can I put this to use to improve student learning?

Page 8: Assessment Analytics

Emphasis on attrition/retention

• Attrition rate: single figures (%)

Page 9: Assessment Analytics

Learning Analytics

Useful to only a tiny proportion of

students and teachers

Focus on things other than learning

Page 10: Assessment Analytics

Academic Analytics

Business intelligence

in HE

Learning Analytics

Focused on retention

Assessment Analytics

Assessment data used within a learning analytics strategy

Page 11: Assessment Analytics

InstitutionalisedOperationalisation

Page 12: Assessment Analytics

Assessment

All students are assessed and all tutors mark student work

Ubiquitous

Routine Normative

Widely understood

Expected Everyone ‘gets’ it

Important

It’s what students pay

for

It’s what tutors are paid to do

Focused on achievement

not just retention

Page 13: Assessment Analytics

Degree Classification

GPA at progression

Module Results

Assessment task results

Granularity

Page 14: Assessment Analytics

Degree Classification

GPA at progression

Module Results

Assessment task results

Granularity

Common errors

Assessment criteria

Page 15: Assessment Analytics

Assessment Analytics

Automates• Data harvesting at fine granularity

Allows• handling assessment data over large and dispersed cohorts• monitoring common errors, progression and achievement• informed curriculum planning even in year

Reporting• Student• School• Institution • PSRB

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License to practice

Page 19: Assessment Analytics

Evidence for stakeholders

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1

2

3

4

5

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Rubric Results

First

2.1

2.2

3rd

Fail

Percentage of Students

Ass

essm

ent

Cri

teri

a

Page 23: Assessment Analytics

Criterion 1 09-10

Criterion 1 10-11

Criterion 2 09-10

Criterion 2 10-11

Criterion 3 09-10

Criterion 3 10-11

Criterion 4 09-10

Criterion 4 10-11

Criterion 5 09-10

Criterion 5 10-11

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

2009-10/2010-11 Rubric Result Comparison

1st2.12.23rdfail

% Students

Rubr

ic Cr

iteria

Page 24: Assessment Analytics

Activity

Usefulness• Supporting student learning• Informing curriculum design decisions

(between and in year)

Risks• Ethics• Negative backwash