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Reimagining Higher Education; The
Journey from Amateur to Professional
Dr Ed de Quincey School of Computing and Mathematics, Keele University
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Dr Ed de Quincey @eddequincey
Senior Lecturer in Computer Science, UG and PG Course Director
School of Computing and Mathematics, Keele University
Senior Fellow of the HEA
instagram.com/eddequincey
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Where is Keele?
C
B
A
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1. Go to → https://socrative.com
2. Click “STUDENT LOGIN” at the top
3. Enter “UOGAPT” in the Room Name
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Professional Amateur
Scholar Teacher
VS
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Do you consider
yourself to be a
Professional
Teacher?
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MSc I.T.
(2001-2002)
2001
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http://gamestorming.com/core-games/card-sort/
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Students’ and designers’ perceptions of MSc homepages
de Quincey, E. (2010). Software support for comparison of media across domains. Keele University.
http://www.eddequincey.com/Doctoral_Thesis_Final_EdeQ.pdf
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Results
Students interested in Content
Super Ordinate Constructs Form or Content
Number of relevant pages Content
Number of links to other IT courses Content
Current students viewpoint shown Content
Qualifications Content
Departmental Information Content
Familiarity Content
Amount of information Content
Use of acronyms Content
Want to go on course Content
Readability Form
Pictures of people Content
Welcoming Form
Super Ordinate Constructs Form or Content
Navigation Position Form
Colour of links Form
Underlined links Form
Page balance Form
Resizability Form
Page alignment Form
Logo position Form
User friendly Form
Opportunities after course Content
Text or graphics Form
Text Size Form
How to apply Content
Web designers interested in Form
#CSC10034 Requirements, Evaluation and Professionalism @eddequincey
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Categorisation of Popular Music 12 Songs. 52 Respondents.
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MSc I.T.
(2001-2002)
2001
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Expectations of an “excellent school”
As a prospective
student what would
you expect to see?
PROJECTIVE TECHNIQUE
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“School must look well kept – carpets, paint, lighting”
“Expect TFT’s in first lab you see when entering the
building”
“Computer facilities are part of it – but general feel of
building also important”
“Giving students merchandise – mouse mats, usb sticks,
dept clothing - so they feel they belong”
“Clear main entrance with focal point”
“All labs looking good / up to date”
“Research / technology room – to show off cool research”
Example Responses
#CSC10034 Requirements, Evaluation and Professionalism @eddequincey
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THE WEBSITE
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• Complimentary Studies Module (CSP)
• ~150 students
• 12 weeks
• 8 Practicals
• Case study
– develop a website for the School of Computing
and Mathematics
#CSC10034 Requirements, Evaluation and Professionalism @eddequincey
CSC-10020 The Web
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TAKEN FOR GRANTED KNOWLEDGE
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MSc I.T.
(2001-2002)
2001
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eHealth Researcher
(2008-2009)
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Search twitter for tweets that contain the word “flu”
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Most popular words found in all tweets
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“I have swine flu”
12,954 tweets
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“I have the flu”
12,651 tweets
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Map Source Professor Jean Emberlin, PollenUK
Hay fever
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eHealth Researcher
(2008-2009)
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Social Bookmarking
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Social Bookmarking
Bookmarks vs Favorites
TAKEN FOR GRANTED KNOWLEDGE
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Social Bookmarking
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Social Bookmarking
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Social Bookmarking
160 users created 1,430 bookmarks
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Social Bookmarking
5,032 tags (1,069 unique)
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eHealth Researcher
(2008-2009)
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Twitter was introduced during the first tutorial
session for 3 courses at UG and PG level, across 2
Schools within the University
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1. All tweets from lecturer accounts
2. All tweets that contained the
relevant course codes e.g. #COMP1314
3. All direct messages and replies
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161 tweets (56%) were
@mentions i.e. the lecturer
replying to a student’s tweet
indicating a good level of
2 way- communication
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@DrEddeQuincey or @eddequincey?
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eHealth Researcher
(2008-2009)
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Using Pinterest for
Learning and Teaching
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Learning Analytics
has been defined as a method for
“deciphering trends
and patterns from
educational big data
… to further the
advancement of a
personalized,
supportive system
of higher education.”
(Johnson et al., 2013)
Co-authorship network map of physicians publishing on hepatitis C (detail)
Source: http://www.flickr.com/photos/speedoflife/8274993170/
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OVERVIEW of the CMS INTRANET
WHAT DATA did we COLLECT?
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We have collected the usage data
of 3,576 students across the
School (UG and PG) since
September 2011. During
this time there have been
7,899,231 interactions
with the student intranet.
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Distribution of activity on the Intranet per day
during the Academic year 2012 to 2013
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For two modules (Levels 4 & 6),
comparisons between the
student attendance, final mark
and intranet activity, categorized
into various resource types, were
made.
COMPARISON OF MEASURES
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COMP1314: Digital Media, Computing and Programming
1st Year Course with 53 students
Correlation between Average Mark and Attendance % = 0.638
0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60 70 80 90 100
Attendance %
Average Mark
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COMP1314: Digital Media, Computing and Programming
1st Year Course with 53 students
Correlation between Average Mark and Intranet Activity = 0.63
0
500
1000
1500
2000
2500
3000
3500
0 10 20 30 40 50 60 70 80 90 100
Number of interactions with Intranet
Average Mark
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COMP1314: Digital Media, Computing and Programming
1st Year Course with 53 students
Correlation
Intranet interactions/Average mark 0.60
Overall attendance/Average mark 0.64
Intranet interactions/Overall attendance 0.44
COMP1314 Intranet interactions/Average mark 0.63
Lecture/tutorial slide views/Average mark 0.48
Lecture slide/tutorial views/Overall attendance 0.46
Coursework specification views/Average mark 0.23
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COMP1640: Enterprise Web Software Development
3rd Year Course with 109 students
Correlation
Intranet interactions/Average mark 0.17
Overall attendance/Average mark 0.42
Intranet interactions/Overall attendance 0.23
COMP1640 Intranet interactions/Average mark 0.19
Lecture/tutorial slide views/Average mark -0.07
Lecture slide/tutorial views/Overall attendance 0.18
Coursework specification views/Average mark 0.38
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Attribute Full Data
(66 students)
Cluster 0
(40 students)
Cluster 1
(26 students)
programmeID P11361 P11361 P03657
CW Mark (%) 48 34 70
Attendance (%) 61 55 70
Total File Views 40 24 64
Tutorial Views 24 15 37
Lecture Views 13 6 22
CW Spec. Views 2 1 3
66 students enrolled on a Level 4 programming module (COMP1314)
Cluster 0: “Average/Failing” students Cluster 1: “Good” students
Results of the simple K-means algorithm revealed
the two most prominent classes of students
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Red – Cluster 1 i.e. “Good” student behaviour
Blue – Cluster 0 i.e. “Average/Failing” student behaviour
Final Mark %
Programme ID
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Final Mark %
Programme ID
Red – Cluster 1 i.e. “Good” student behaviour
Blue – Cluster 0 i.e. “Average/Failing” student behaviour
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Red – Cluster 1 i.e. “Good” student behaviour
Blue – Cluster 0 i.e. “Average/Failing” student behaviour
Final Mark %
Programme ID
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Red – Cluster 1 i.e. “Good” student behaviour
Blue – Cluster 0 i.e. “Average/Failing” student behaviour
Final Mark %
Programme ID
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Using Pinterest for
Learning and Teaching
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Blackboard Analytics
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Apple Lisa
PRESENTING STUDENT DATA BACK TO STUDENTS and LECTURERS,
USING USER CENTRIC QUERIES, FORMATS and METAPHORS
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Identified and Reviewed 22 Learning Analytics Systems
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Review of Existing Systems
Few systems available for general use
✖
Primarily targeted at educators. Only 5 of the 22
systems being designed purely for use by students
Only 4 of the studies gathered the requirements for
the systems directly from users
Currently analysing visualisation techniques used
and types/sources of data used
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User-Centered Design Process Map
http://www.usability.gov/how-to-and-tools/resources/ucd-map.html
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WP3. Requirements Gathering
Set as Coursework Case Study:
Identify GUI metaphors that will engage and motivate them as
learners and personalise their own learning experience
2nd Year Computing Module - 82 students in 14 groups
Deliverables included:
Sets of User Persona, analysed results of requirements
elicitation sessions and annotated screen mock-ups of
potential LA Dashboards (highlighting 5 key features along with objective justifications wherever possible).
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'My Timeline': Past events which have been completed successfully provide
gratification to the user in the form of positive icons such as thumbs up or smiley faces.
The past timeline balances with the upcoming events to try and alleviate future workload
stress by demonstrating positive success at the same time.
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Course/Module Progress Bar: a progress bar based upon the how far in the course
users are. Indicating how much of the course they should know and how much time is
left until the course finishes.
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Degree Classification Requirements: Panel showing the Percentage
needed in Future Assignments to get certain Degree Classifications.
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Comparison with peers: The chance for the user to see how
they are comparing with the top 20% of the class and how they
are doing compared with the average mark.
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Using Pinterest for
Learning and Teaching
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HEFCE Catalyst Grant £99,790 (£49,988 from Catalyst Fund, £49,802 in matched funding from Keele)
Title: Learner Centred Design for Learning Analytics
This project aims to avoid the common problem in Learning Analytics (LA) of the technology and data driving the
user experience, and therefore the ability to interpret and use the information. By sharing the data directly
with students, using student-centred representations of their learning activity, this project aims to facilitate a
common understanding of the learning experience between lecturers and students. Expanding on a successful
teaching innovation project at Keele University interface metaphors for LA will be identified that motivate and
personalise the learning experience of cohorts with differing levels of technical experience and levels of digital
literacy. We will then produce appropriate visualisations of student activity based on the data available at Keele
University and incorporate them into the delivery of relevant modules with the key aims of
increasing engagement, making the VLE a more active space for learning and teaching and bridging the current
gap between physical and digital spaces.
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Outcomes for students
Students in lecture hall ©Jirka Matousek via Flickr
• access to personalised notifications and support
e.g. highlighting/suggesting resources that have not been viewed.
• increased levels of engagement, in particular VLE usage.
• personalisation of cohort module delivery by the lecturer
• real-time feedback for students enabling them to judge
their progress during a module using a different metric
than current models of formative and summative feedback.
• direct involvement with the development of tools that
support their learning.
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Student Ambassadors
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1 Being the best you can be/Effort (ability to maintain effort)
2 Build self-confidence
3 Career/Vocation/Job prospects
4 Industry
5 Giving yourself options
6 Grades/Marks/Qualifications
7 Mastery of a subject/Interest in Subject/Stretch themselves intellectually
8 Mentoring/Family
9 Money
10 Part of a Professional community
11 Self-efficacy/ Helplessness (this might be the opposite of self-efficacy)
12
Sense of connectedness with others with similar goals/ Success as a group of
peers
13 Social Prestige/Recognition
Initial Identified Motivators for Studying in HE
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Looks how they feel
Shows how hard they are
trying
No point in spending
£9,000 if you’re not going
to try hard and do well
A B
↑
↑
“When thinking about what motivates you to study your
degree, which of these do you prefer and why?”.
“Why..?”
“Why..?”
Laddering
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Revised Motivators
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Timeline
Formed basis of 6 Focus Groups organised and run by Student Ambassadors
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Fun
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Personified
Professional
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Learning Analytics in the classroom?
Students in lecture hall ©Jirka Matousek via Flickr
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Reimagining Higher Education
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© dirkcuys via Flickr
Better uses of data
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© Andy Bright via Flickr
User centred technology and processes
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“The cost of poor usability is
high. It includes unsatisfied,
ineffective learners and
ineffective e-learning
initiatives. Learners who find an
e-learning program hard to use
might:
• Carry out their task reluctantly
• Be confused about the learning
exercise
• Fail to engage with the e-learning,
possibly abandon the e-learning
completely, fail to learn or retain
knowledge.” (Abedour and Smith, 2006)
©CollegeDegrees360 via Flickr
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Research Teaching
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Career