Presentation (Commended) at Irish Network of Medical Educators Annual Scientific Meeting 2015

1. Examining the relationships between attendance, online
engagement and assessment outcomes in undergraduates; an
observational prospective, multicentre study.
Jane Holland*, Eric Clarke*, Morag Monro , Evelyn Kelleher , Mark Glynn 
*RCSI Dublin, DCU Dublin
Funded by 3U Partnership
N-STEP Strand 1 Initiative

2. Non-attendance:
 Correlates with poor performance (1)
 Non-anglophones(2)
 Understanding & conceptualisation(3)
 Identifying individual cause is essential (4,5)
Monitoring:
 Time-consuming & potential for recording errors
 Software / biometrics –cost considerations
 Reports & logs
Background & Literature
1. Massingham, 2006
2. Gatherer, 1998
3. Sharma, 2005
4. Dobkin, 2007
5. Rodgers, 2002
Funded by 3U Partnership
N-STEP Strand 1 Initiative

3. o Ethical approval obtained
 RCSI - REC848
o Data collection
Methods
Funded by 3U Partnership
N-STEP Strand 1 Initiative

4. Physical
attendance
Online
activity
Continuous
assessment
Summative
examination
NM
New entrants (n=329) 8 24 2.9% 63.5 %
Repeat (n=29) 7 14 2.3% 54.4 %
AS
New entrants (n=329) 12 25 2.9% 66.3 %
Repeat (n=29) 8 15 2.3% 55.8 %
Results – Descriptive statistics (1)
Funded by 3U Partnership
N-STEP Strand 1 Initiative
JC-NM
0 10 20 30 40 50 60 70 80 90
<20
21-24
25
New JC1 Repeating JC1

5. Results – Descriptive statistics (2)
Funded by 3U Partnership
N-STEP Strand 1 Initiative
Physical
attendance
Online
activity
Continuous
assessment
Summative
examination
NM
New entrants (n=329) 8 24 2.9% 63.5 %
Repeat (n=29) 7 14 2.3% 54.4 %
AS
New entrants (n=329) 12 25 2.9% 66.3 %
Repeat (n=29) 8 15 2.3% 55.8 %
*
* p< 0.005
Mann-Whitney U
JC-AS
0 10 20 30 40 50 60 70
<20
21-25
26
New JC1 Repeating JC1

6. Physical
attendance
Online activity
Continuous
assessment
NM
New entrants R2 < 0.1 R2 < 0.1 R2 = 0.54
Repeat No correlation No correlation R2 = 0.22
AS
New entrants R2 < 0.1 R2 = 0.12 R2 = 0.58
Repeat R2 = 0.17 R2 = 0.21 R2 = 0.15
Results – Regression analysis
Basic assumptions confirmed
Tests for tolerance & VIF- no evidence of multicollinearity
*? Late arrival due to appeals
*
Funded by 3U Partnership
N-STEP Strand 1 Initiative

7. CA
NM AS
Funded by 3U Partnership
N-STEP Strand 1 Initiative

8.  New entrants
o Continuous assessment most predictive
o Online activity – larger effect size than physical attendance
 Repeat students
o Outliers - High risk
o Online activity most predictive
Summary of results
Funded by 3U Partnership
N-STEP Strand 1 Initiative
Caveats & Conclusions...
Caveat – correlation does not equal causation
Non-attendance is a symptom – individual diagnosis & management of
underlying issues is essential...
Developing a screening program for early identification allows early
intervention

9. 1. Massingham P, Herrington T. Does attendance matter? An examination of student
attitudes, participation, performance and attendance. Journal of university teaching
& learning practice. 2006;3(2):3.
2. Gatherer, D., & Manning, F. C. (1998). Correlation of examination performance with
lecture attendance: a comparative study of first-year biological sciences
undergraduates. Biochemical Education, 26(2), 121-123.
3. Sharma, M. D., Mendez, A., & O’Byrne, J. W. (2005). The relationship between
attendance in student‐centred physics tutorials and performance in university
examinations. International Journal of Science Education, 27(11), 1375-1389.
4. Dobkin C, Gil R, Marion J. Causes and consequences of skipping class in college:
Mimeo, UC Santa Cruz 2007.
5. Rodgers JR. Encouraging tutorial attendance at university did not improve
performance. Australian Economic Papers. 2002;41(3):255-66.
References
Funded by 3U Partnership
N-STEP Strand 1 Initiative