Reimagining Higher Education; The Journey from Amateur to Professional

Keynote from APT2017

The “professional scholar, but amateur teacher” model is becoming increasingly indefensible as HE becomes more diverse, accountable and adapts to advances in technology and student expectations (McLaren, 2005). However, the route from “amateur” to “professional” status can be daunting and often not align with traditional academic views and progression pathways. This keynote will provide a personal reflection on this journey and describe a number of case studies that have used technologies and techniques from Computer Science to enhance learning and teaching. It will also propose that placing the “user” at the centre when “reimagining higher education” is key to its future, and can be used to bridge the gap between teaching and research.

Transcript

1. Reimagining Higher Education; The
   Journey from Amateur to Professional
   Dr Ed de Quincey School of Computing and Mathematics, Keele University
   

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2. 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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3. Where is Keele?
   C
   B
   A
   E
   D
   

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4. 1. Go to → https://socrative.com
   2. Click “STUDENT LOGIN” at the top
   3. Enter “UOGAPT” in the Room Name
   

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6. Professional Amateur
   Scholar Teacher
   VS
   

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7. Do you consider
   yourself to be a
   Professional
   Teacher?
   

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8. MSc I.T.
   (2001-2002)
   2001
   

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9. http://gamestorming.com/core-games/card-sort/
   

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10. 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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11. 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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12. Categorisation of Popular Music 12 Songs. 52 Respondents.
    

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15. MSc I.T.
    (2001-2002)
    2001
    

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16. Expectations of an “excellent school”
    As a prospective
    student what would
    you expect to see?
    PROJECTIVE TECHNIQUE
    

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17. “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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18. 18
    THE WEBSITE
    

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19. • 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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21. TAKEN FOR GRANTED KNOWLEDGE
    

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22. MSc I.T.
    (2001-2002)
    2001
    

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23. eHealth Researcher
    (2008-2009)
    

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25. View Slide

26. Search twitter for tweets that contain the word “flu”
    

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27. Most popular words found in all tweets
    

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28. “I have swine flu”
    12,954 tweets
    

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29. “I have the flu”
    12,651 tweets
    

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30. View Slide

31. Map Source Professor Jean Emberlin, PollenUK
    Hay fever
    

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32. eHealth Researcher
    (2008-2009)
    

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33. Social Bookmarking
    

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34. Social Bookmarking
    Bookmarks vs Favorites
    TAKEN FOR GRANTED KNOWLEDGE
    

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35. Social Bookmarking
    

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36. Social Bookmarking
    

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37. Social Bookmarking
    160 users created 1,430 bookmarks
    

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38. Social Bookmarking
    5,032 tags (1,069 unique)
    

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39. eHealth Researcher
    (2008-2009)
    

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40. 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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41. 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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42. 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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43. @DrEddeQuincey or @eddequincey?
    

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45. eHealth Researcher
    (2008-2009)
    

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46. Using Pinterest for
    Learning and Teaching
    

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47. 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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48. OVERVIEW of the CMS INTRANET
    WHAT DATA did we COLLECT?
    

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51. 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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52. Distribution of activity on the Intranet per day
    during the Academic year 2012 to 2013
    

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53. 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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54. 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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55. 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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56. 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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57. 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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58. 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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59. 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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60. 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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61. 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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62. 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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63. Using Pinterest for
    Learning and Teaching
    

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65. Blackboard Analytics
    

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66. Apple Lisa
    PRESENTING STUDENT DATA BACK TO STUDENTS and LECTURERS,
    USING USER CENTRIC QUERIES, FORMATS and METAPHORS
    

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67. Identified and Reviewed 22 Learning Analytics Systems
    

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68. 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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69. User-Centered Design Process Map
    http://www.usability.gov/how-to-and-tools/resources/ucd-map.html
    

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70. 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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71. '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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72. 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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73. Degree Classification Requirements: Panel showing the Percentage
    needed in Future Assignments to get certain Degree Classifications.
    

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74. 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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75. Using Pinterest for
    Learning and Teaching
    

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76. 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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77. 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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78. Student Ambassadors
    

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79. 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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82. 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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83. Revised Motivators
    

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84. View Slide

85. Timeline
    Formed basis of 6 Focus Groups organised and run by Student Ambassadors
    

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86. View Slide

87. Fun
    

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88. Personified
    Professional
    

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89. Learning Analytics in the classroom?
    Students in lecture hall ©Jirka Matousek via Flickr
    

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90. Reimagining Higher Education
    

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91. © dirkcuys via Flickr
    Better uses of data
    

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92. © Andy Bright via Flickr
    User centred technology and processes
    

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93. “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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94. Research Teaching
    

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95. Career
    

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96. twitter: @eddequincey
    e-mail: e.de.quincey@keele.ac.uk
    

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