The document discusses a project that used virtual learning environment (VLE) data to provide weekly automated feedback to first-year students on their engagement. The project aimed to improve student engagement and progression. Students received emails summarizing their VLE activity and survey results found most students changed their VLE usage and would participate again. The document recommends using VLE data to provide feedback but ensuring appropriate ethical approval is received.

1. PredictEd: Using VLE data
to provide weekly automated
feedback on Student
Engagement
Owen Corrigan
Mark Glynn
Alan F. Smeaton
Sinéad Smyth
@glynnmark

2. @glynnmark #heie
Outline
• The Challenge
• What did we do?
• How did we give feedback?
• What did the students say?
• What are the benefit &
drawbacks?
• The Recommendations

3. @glynnmark #heie
Challenge
1) Provide regular timely feedback to first year students
with respect to their engagement with the VLE
2) To improve first year students’ engagement with
course materials and therefore by association hopefully
improve overall progression rates through improved
grades
3) Demonstrate how the VLE data can be harnessed to
advance course design.

4. @glynnmark #heie
What did we do?

5. @glynnmark #heie
Feedback
Students
Staff

6. @glynnmark #heie
Benefits & Drawbacks
• +2.67 % in their final
exam scores
• Staff became more
aware of what content
on the course pages
that students engaged
with
• overall increase in
awareness amongst
staff of the benefits of
the VLE
• extensive ethical
consideration
• Hawthorne effect
• Not as significant as
desired

7. @glynnmark #heie
What did the students say?
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 semester 4.5% 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% )

8. @glynnmark #heie
Conclusions & Recommendations
VLE data can be used to provide regular automated
feedback and feedforward to students
Communicate thoroughly with the students and staff
involved in this project
if you decide to use VLE data please ensure that you get
appropriate ethical clearance first

9. @glynnmark #heie
Contact details
• mark.glynn@dcu.ie
• @glynnmark
• http://enhancingteaching.com
• https://predictedanalytics.wordpress.com/

10. @glynnmark #heie
Additional slides
Extra
notes

11. @glynnmark #heie
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

12. @glynnmark #heie
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.”

13. @glynnmark #heie
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”

14. @glynnmark #heie
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)

15. @glynnmark #heie
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

16. @glynnmark #heie
No significant difference in the entry profiles of
participants vs. non-participants overall
PredictEd Participant Profile

17. @glynnmark #heie
0%
20%
40%
60%
80%
100%
LG116 MS136 LG101 HR101 LG127 ES125 BE101 SS103 CA103 CA168
Workshops
Wikis
Forums
Assignments
Quizzes
scorm
lesson
choice
feedback
database
glossary
wiki
url
book
pages
folders
files
Course content

18. @glynnmark #heie

19. @glynnmark #heie
Subject Description Non-Participant Participant
BE101 Introduction to Cell Biology and Biochemistry 58.89 62.05
CA103 Computer Systems 70.28 71.34
CA168 Digital World 63.81 65.26
ES125 Social&Personal Dev with Communication Skills 67.00 66.46
HR101 Psychology in Organisations 59.43 63.32
LG101 Introduction to Law 53.33 54.85
LG116 Introduction to Politics 45.68 44.85
LG127 Business Law 60.57 61.82
MS136 Mathematics for Economics and Business 60.78 69.35
SS103 Physiology for Health Sciences 55.27 57.03
Overall 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. MS136
Module Average Performance Participants
vs. Non-Participants

20. @glynnmark #heie
Databridge
MITM
Course
Database
Timetable
ePortfolio
WifiLMS
Library
SRS

21. @glynnmark #heie
LG116: Introduction to Politics
Students / year = ~110
Pass rate = 0.78

22. @glynnmark #heie
Importance of Ethics
• Ethics are important to ensure safety of participants and
researchers
• Educational Data Analytics is a new area of research
– Not much previous research to highlight possible ethical issues
– Requires extensive ethical consideration
• We have spent a lot of time this Summer preparing a DCU REC
submission
– We’ve submitted and had approval for a test case
– We’ve met with REC chair to brief him
• We are following the 8 Principles set out by the Open University
who are at EXACTLY the same stage as us

23. @glynnmark #heie
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

24. @glynnmark #heie
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 ?

25. @glynnmark #heie
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

26. @glynnmark #heie
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.