9 September 2015: 07:00AM GMT What can we do with assessment analytics? A/Prof. Cath Ellis (University of New South Wales, Australia) and Dr. Rachel Forsyth (Manchester Metropolitan University, UK) Your Hosts Professor Geoff Crisp, Dean Learning and Teaching, RMIT University geoffrey.crisp[at]rmit.edu.au Dr Mathew Hillier, Institute for Teaching and Learning Innovation, University of Queensland mathew.hillier[at]uq.edu.au Just to let you know: By par'cipa'ng in the webinar you acknowledge and agree that: The session may be recorded, including voice and text chat communica'ons (a recording indicator is shown inside the webinar room when this is the case). We may release recordings freely to the public which become part of the public record. We may use session recordings for quality improvement, or as part of further research and publica'ons. Webinar Series e-Assessment SIG
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9 September 2015: 07:00AM GMT What can we do with assessment analytics? A/Prof. Cath Ellis (University of New South Wales, Australia) and Dr. Rachel Forsyth (Manchester Metropolitan University, UK)
Your Hosts Professor Geoff Crisp, Dean Learning and Teaching, RMIT University geoffrey.crisp[at]rmit.edu.au Dr Mathew Hillier, Institute for Teaching and Learning Innovation, University of Queensland mathew.hillier[at]uq.edu.au
Just%to%let%you%know:%%By#par'cipa'ng#in#the#webinar#you#acknowledge#and#agree#that:!The#session#may#be#recorded,#including#voice#and#text#chat#communica'ons#(a#recording#indicator#is#shown#inside#the#webinar#room#when#this#is#the#case).#We#may#release#recordings#freely#to#the#public#which#become#part#of#the#public#record.#We#may#use#session#recordings#for#quality#improvement,#or#as#part#of#further#research#and#publica'ons.!
Webinar Series
e-Assessment SIG
Assessment Analytics
• Should we do it? • If so – what might it look like?
A/Prof Cath Ellis, University of New South Wales, Australia Dr Rachel Forsyth, Manchester Metropolitan University, UK
What are Assessment Analytics?
Analysis of assessment data, but
which data, and
for what purpose?
Binary data image by W.Rebel, cc licensed !
Informed decision-making
“In higher education many institutional decisions are too important to be based only on intuition, anecdote, or presumption; critical decisions require facts and the testing of possible solutions”
(Campbell & Oblinger, 2007, p. 2).
Learning Analytics
New Operationalisation
Poorly understood Gaps
Ethics
Emphasis on SNA
Emphasis on SNA
Emphasis on SNA
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?
Emphasis on attrition/retention
Attrition rate: single figures (%)
Learning Analytics
Useful to only a tiny proportion of
students and teachers
Focus on things other than learning
Academic Analytics
Business intelligence in HE
Learning Analytics
Using data to make informed decisions regarding student learning
Assessment Analytics
The analysis of assessment data within a learning analytics strategy
Institutionalised Operationalisation
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
Feedback analysis
Assignment types Student satisfaction ‘scores’ and comments
Marks
Dates when assignments are due
Data we’ve used
Degree Classification
GPA at progression
Module Results
Assessment task results
Granularity
Degree Classification
GPA at progression
Module Results
Assessment task results
Granularity
Common errors
Assessment criteria
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
License to practice
Evidence for stakeholders
0% 20% 40% 60% 80% 100%
1
2
3
4
5
Percentage of Students
Ass
essm
ent C
rite
ria
Rubric Results
First 2.1 2.2 3rd Fail
0%# 20%# 40%# 60%# 80%# 100%#
Criterion#1#09L10#
Criterion#1#10L11#
Criterion#2#09L10#
Criterion#2#10L11#
Criterion#3#09L10#
Criterion#3#10L11#
Criterion#4#09L10#
Criterion#4#10L11#
Criterion#5#09L10#
Criterion#5#10L11#
%#Students#
Rubric#Criteria#
2009L10/2010L11#Rubric#Result#Comparison#
1st#
2.1#
2.2#
3rd#
fail#
Activity
Usefulness
• Supporting student learning • Informing curriculum design decisions
(between and in year)
Risks • Ethics • Negative backwash
Analytics and assessment review
Analytics and assessment review
http://www.celt.mmu.ac.uk/assessment/design/types.php!
http://www.celt.mmu.ac.uk/assessment/design/types.php
Analytics and assessment review
Developing systems
Analytics and systems development
Student satisfaction with assessment
0% 5%
10% 15% 20% 25% 30% 35% 40% 45%
Engagement Feedback Marking Organisation Support
Comparison of positive and negative comments as percentage of total comments about assessment (n=10155)
"Best things" "Things I'd like improved"
Session Feedback Survey
With thanks from your hosts Professor Geoff Crisp, Dean Learning and Teaching, RMIT University geoffrey.crisp[at]rmit.edu.au Dr Mathew Hillier, Institute Teaching and Learning Innovation, University of Queensland mathew.hillier[at]uq.edu.au Recording available http://transformingassessment.com
Webinar Series
e-Assessment SIG
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