Data Analytics on VLE Access DataHow much can we mine from a mouseclick ?  

John BrennanOwen CorriganAly EganMark GlynnAlan F. SmeatonSinéad Smyth

@glynnmark

Outline

• Motivation and goals• Selecting the modules• Study by numbers• The interventions

- What the student sees- What the Lecturer sees

• What the students said• The results

Motivation

Total Moodle Activity – notice the periodicity

One example module – ideal !

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

No significant difference in the entry profiles of participants vs. non-participants overall

PredictEd Participant Profile

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

LG116: Introduction to Politics

Students / year = ~110Pass rate = 0.78

LG116 – Predictor confidence (ROC AUC)

SS103

Student Interventions: Feedback

The Interventions – Lecturers’ Experience

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

Questions and discussion…

Contact details

• mark.glynn@dcu.ie

• glynnmark

• http://enhancingteaching.com

Additional slides

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

So much student data we could useDemographics• 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)

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

LG116 MS136 LG101 HR101 LG127 ES125 BE101 SS103 CA103 CA1680%

20%

40%

60%

80%

100%Workshops

Wikis

Forums

Assignments

Quizzes

scorm

lesson

choice

feedback

database

glossary

wiki

url

book

pages

folders

files

Course content

BE101: Intro to Cell Biology

Results / year = ~300Pass rate = 0.86

BE101

SS103: Physiology for Health Sciences

Results / Year = ~150Pass rate = 0.92

MS136

LG101

HR101

CA103

Some unusable modules

Modules 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