PredictEd: Using VLE data to provide weekly automated feedback on Student Engagement 

Owen CorriganMark GlynnAlan F. SmeatonSinéad Smyth

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Outline

• The Challenge• What did we do?• How did we give feedback?• What did the students say?• What are the benefit &

drawbacks?• The Recommendations

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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.

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What did we do?

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Feedback

Students

Staff

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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

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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

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% )

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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

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Contact details

• mark.glynn@dcu.ie

• @glynnmark

• http://enhancingteaching.com

• https://predictedanalytics.wordpress.com/

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Additional slides

Extra notes

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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

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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.”

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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”

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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)

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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

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

PredictEd Participant Profile

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LG116 MS136 LG101 HR101 LG127 ES125 BE101 SS103 CA103 CA1680%

20%

40%

60%

80%

100%WorkshopsWikisForumsAssignmentsQuizzesscormlessonchoicefeedbackdatabaseglossarywikiurlbookpagesfoldersfiles

Course content

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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. MS136

Module Average Performance Participants vs. Non-Participants

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Databridge

MITM

Course Databa

se

Timetable

ePortfolio

Wifi

LMS

Library

SRS

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LG116: Introduction to Politics

Students / year = ~110Pass rate = 0.78

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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

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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

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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 ?

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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

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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.