Learning Analytics - value what we measure or measure what we value
Dr Mark Glynn
@glynnmark
Outline
• Introduction• Motivation and goals• Challenges• Examples• Technical bits• Discussion
– What would you like to analyse
– Collaboration
Teaching Enhancement Unit
TEACHINGENHANCEMENT
UNIT
Onlineand Blended Learning
Support
Awards and Grants
Credit Earning Modules
Professional Development
Workshops
Data Analytics
Data analytics is the science of extracting actionable insight from large amounts of raw data
DIT – MSc in Computing
Youtube
Tesco
Definiton
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs. A related field is educational data mining.
- wikipedia
Discussion
What data do we already collect?
So much student data we could use
Demographics• Age, home/term address, commuting distance, socio-economic status, family composition, school
attended, census information, home property value, sibling activities, census information
Academic Performance• CAO and Leaving cert, University exams, course preferences, performance relative to peers in
school
Physical Behaviour• Library access, sports centre, clubs and societies, eduroam access yielding co-location with others
and peer groupings, lecture/lab attendance,
Online Behaviour• Mood and emotional analysis of Facebook, Twitter, Instagram activities, friends and their actual
social network, access to VLE (Moodle)
Motivation
Discussion
What challenges do you foresee in your institution?
Core Principles – Open University UK
Learning analytics is a moral practise which should align with core organisational principles
The purpose and boundaries regarding the use of learning analytics should be well defined and visible
Students should be engaged as active agents in the implementation of learning analytics
The organisation should aim to be transparent regarding data collection and provide students with the opportunity to update their own data and consent agreements at regular
intervalsModelling and interventions based on analysis of data should be free from bias and
aligned with appropriate theoretical and pedagogical frameworks wherever possible
Students are not wholly defined by their visible data or our interpretation of that data
Adoption of learning analytics within the organisation requires broad acceptance of the values and benefits (organisational culture) and the development of appropriate skills
The organisation has a responsibility to all stakeholders to use and extract meaning from student data for the benefit of students where feasible
Amazon
Examples
The earlier we diagnose, the earlier we can treat
John CarrollMark GlynnEabhnat Ni Fhloinn
@glynnmark
Maths Diagnostic test
Data Analytics on VLE Access DataHow much can we mine from a mouseclick ?
John BrennanOwen CorriganAly EganMark GlynnAlan F. SmeatonSinéad Smyth
@glynnmark
No significant difference in the entry profiles of participants vs. non-participants overall
PredictEd Participant Profile
Total Moodle Activity – notice the periodicity
One example module – ideal !
LGxxx – Predictor confidence (ROC AUC)
All modules
LG116 MS136 LG101 HR101 LG127 ES125 BE101 SS103 CA103 CA1680%
20%
40%
60%
80%
100%WorkshopsWikisForumsAssignmentsQuizzesscormlessonchoicefeedbackdatabaseglossarywikiurlbookpagesfoldersfiles
Course content
a b c d e f g h i j
Study by numbers
• 17 Modules across the University (first year, high failure rate, use Loop, periodicity, stability of content, Lecturer on-board)
• Offered to students who opt-in or opt-out, over 18s only
• 76% of students opted-in, 377 opted-out, no difference among cohorts
• 10,245 emails sent to 1,184 students who opted-in over 13 weekly email alerts
The Interventions – Lecturers’ Experience
Student Engagement - individual
Student Engagement - individual
Regression analysis- class grades
What to measure?
Content Transfer
Interaction Collabora-tion
Assessment
Collaboration
Modules which work well …
• Have periodicity (repeatability) in Moodle access• Confidence of predictor increases over time• Don't have high pass rates (< 0.95)• Have large number of students, early-stage
LGxxx: law based subject
Students / year = ~110Pass rate = 0.78
Student Interventions: Feedback
Relative data
Student Experience of PredictED
Students who took part were asked to complete a short survey at the start of Semester 2 - N=133 (11% response rate)
Question Group 1 (more detailed email)
Group 2
% of respondents who opted out of PredictED during the course of the
semester4.5% 4.5%
% who changed their Loop usage as a result of the weekly emails
43.3% 28.9%
% who would take part again/are offered and are taking part again
72.2% (45.6%/ 26.6% )
76.6% (46% /30.6% )
33% said they changed how they used Loop. We asked them how?
• Studied more– “More study”– “Read some other articles online”– “Wrote more notes”– “I tried to apply myself much more, however yielded no results”– “It proved useful for getting tutorial work done”
• Used Loop more– “I tried harder to engage with my modules on loop”– “I think as it is recorded I did not hesitate to go on loop. And loop as become
my first support of study.”– “I logged on more”– “I read most of the extra files under each topic, I usually would just look at
the lecture notes.”– “I looked at more of the links on the course nes pages, which helped me to
further my understanding of the topics”– “I learnt how often I need to log on to stay caught up.”
Did you change Loop usage for other modules?
• Most who commented used Loop more often for other modules– “More often”– “More efficient”– “Used loop more for other modules when i was logging onto
loop for the module linked to PredictED”– “Felt more motivated to increase my Loop usage in general
for all subjects”
One realised that Lecturers could see their Loop activity“I realised that since teachers knew how much i was
using loop, i had to try to mantain pages long on so it looked as if i used it a lot”
Subject Description Non-Participant ParticipantBE101 Introduction to Cell Biology and Biochemistry 58.89 62.05CA103 Computer Systems 70.28 71.34CA168 Digital World 63.81 65.26ES125 Social&Personal Dev with Communication Skills 67.00 66.46HR101 Psychology in Organisations 59.43 63.32LG101 Introduction to Law 53.33 54.85LG116 Introduction to Politics 45.68 44.85LG127 Business Law 60.57 61.82MS136 Mathematics for Economics and Business 60.78 69.35SS103 Physiology for Health Sciences 55.27 57.03Overall Dff in all modules 58.36 61.22
Average scores for participants are higher in 8 of the 10 modules analysed, significantly higher in BE101, and CA103
Module Average Performance Participants vs. Non-Participants
Measuring the Flipping effect
Patrick DoyleMark GlynnEveyn Kelleher
@glynnmark
Assessment Challenge
Logistics
• 200+ students• 4 assignments each• 5 minutes per
assignment• 10 lecturers• 2 weeks of assessment
Marking guide
Related research
Comparing students who watched versus not watched video oneComparing Means [ t-test assuming unequal variances (heteroscedastic) ]
Descriptive StatisticsVAR Sample size Mean Variance
Didn't watch 84 51.86905 691.58505Watched 102 63.15686 576.74743
Two-tailed distribution
p-level 0.00284 t Critical Value (5%) 1.97402
One-tailed distribution
p-level 0.00142 t Critical Value (5%) 1.65387
Discussion
What would you like to measure?
Selectively Analyzing your Course data?
@glynnmark@drjaneholland
Dr Jane Holland, RCSIEric Clarke, RCSIDr Mark Glynn, DCUDr Evelyn Kelleher, DCU
Constructive Alignment
Learning Outcomes
Particulars
• Attendance– Tutorials– labs
• Moodle logs• Defined times• Assessment results
Excel results Video tracking
Zero One Two Three Four Five Six Seven0%
10%
20%
30%
40%
50%
60%
70% What students watched "x" amount of videos
Watched
Watched before
All activities
One activity in particular
Multiple activities
Health warning
Questions and discussion…
Talking to one another
LMS
SRS
CMS
Timetable
Wifi
Library
Databridge
MITM
Course Databa
se
Timetable
ePortfolio
Wifi
LMS
Library
SRS
Additional slides
Building classifiers for each week/each module
Training DataTesting
Notes on model confidence• Y axis is confidence in AUC ROC (not probability)• X axis is time in weeks• 0.5 or below is a poor result• Most Modules start at 0.5 when we don't have much
information• 0.6 is acceptable, 0.7 is really good (for this task)• The model should increase in confidence over time• Even if confidence overall increases, due to randomness
the confidence may go up and down• It should trend upwards to be a valid model and viable
module choice
BExxx: Intro to Cell Biology
Results / year = ~300Pass rate = 0.86
BExxx
SSxx: Health Sciences
Results / Year = ~150Pass rate = 0.92
MSxxx
LGxxx
HR101
CAxxx
Some unusable modulesModules where the ROC AUC increases slowly (e.g stays below 0.6) e.g. PS122
Timescale for Rollout
• Still some issues on Moodle access log data transfer to be resolved
• Still have to resolve student name / email address / Moodle ID / student number
• Still to resolve timing of when we can get new registration data, updates to registrations (late registrations, change of module, change of course, etc.) …
• Should we get new, “clean” data each week ?
Why did you take part?• The majority of students
wanted to learn/monitor their performance
• Many others were curious
• Some were interested in the Research aspect
• Some were just following advice
• Others were indifferent
How easy was it to understand the information in the emails ?(1= not at all easy, 5 = extremely easy)
• Average 3.97 (SD= 1.07)
• Very few had comments to make (19/133)– Most who commented wanted more
detail.
Week 3
Training DataTesting
Week 4
Training DataTesting
Week 5
Training DataTesting
Week 6
Training DataTesting
Week 7
Training DataTesting
Week 8
Training DataTesting
Week 9
Training DataTesting
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