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

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, 20062. Gatherer, 19983. Sharma, 20054. Dobkin, 20075. Rodgers, 2002

Funded by 3U Partnership N-STEP Strand 1 Initiative

o Ethical approval obtained RCSI - REC848

o Data collection

Methods

Funded by 3U Partnership N-STEP Strand 1 Initiative

Physical attendance

Online activity

Continuous assessment

Summative examination

NMNew entrants (n=329) 8 24 2.9% 63.5 %

Repeat (n=29) 7 14 2.3% 54.4 %

ASNew 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

<20

21-24

25

0 10 20 30 40 50 60 70 80 90

New JC1 Repeating JC1

Results – Descriptive statistics (2)

Funded by 3U Partnership N-STEP Strand 1 Initiative

Physical attendance

Online activity

Continuous assessment

Summative examination

NMNew entrants (n=329) 8 24 2.9% 63.5 %

Repeat (n=29) 7 14 2.3% 54.4 %

ASNew 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

<20

21-25

26

0 10 20 30 40 50 60 70

New JC1 Repeating JC1

Physical attendance Online activity

Continuous assessment

NMNew entrants R2 < 0.1 R2 < 0.1 R2 = 0.54

Repeat No correlation No correlation R2 = 0.22

ASNew 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 confirmedTests for tolerance & VIF- no evidence of multicollinearity

*? Late arrival due to appeals

*

Funded by 3U Partnership N-STEP Strand 1 Initiative

CA

NM AS

Funded by 3U Partnership N-STEP Strand 1 Initiative

New entrantso Continuous assessment most predictiveo Online activity – larger effect size than physical

attendance

Repeat studentso 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 causationNon-attendance is a symptom – individual diagnosis &

management of underlying issues is essential... Developing a screening program for early identification allows

early intervention

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