Project: What impact does lecture attendance have on final grades in a modern Australian
university?
Background, project aims and expected outcomes
Anecdotal evidence suggests that students who do not attend lectures are more likely to do poorly
than those who do attend. Prior research on this theme is sometimes contradictory, focuses
mainly on business students, and largely predates the widespread use of online learning
platforms. This project aims to explore the link between lecture attendance and final grades at a
modern Australian university (Macquarie University), and by combining this with data retrieved
from student-system databases, see if other factors in a student’s life are as or more influential.
The study will focus on students in science. If successful, this study will act as a pilot to more
extensive cross-institutional studies.
Research into the relationship between class attendance and final grades has been undertaken for
some time but is sparse in relation to Australian circumstances and science disciplines. Rodgers
(2002) looked at business students at Australian Universities and suggested that poor attendance
adversely affected the performance of the student and that this is widespread while at the same
time acknowledged that there was little evidence to support this idea. Clark et al. (2011) looked at
environmental science students in the UK, found a similar relationship, but found that this
relationship was complex. They presented a literature review and showed that the research is
divided with some supporting a link and others not. Paisey and Paisey (2004), who studied
accounting students in the UK, found that class attendance had a clear positive correlation with
performance, as did Wigley (2009) who studied UK psychology students and Chen and Lin
(2008) with finance students in Taiwan. In contrast, Oakley et al. (2011) found no relationship
between lecture or tutorial attendance and performance in their study of education students in an
Australian university, as did Dolnicar (2005) in her study of Australian students across a number
of disciplines.
The relationship between attendance and performance appears to be affected by the type of class,
eg a problem-based learning class versus a lecture or tutorial where the same information may be
available elsewhere, and by the type of learner that the student is, eg a surface learner may attend
all lectures but not engage with the information. Clark et al. (2011) found that students who
regularly attended class could gain an extra ten percentage points on average compared to those
who did not attend.
Similarly, Moore et al. (2008) found a link between lecture attendance and student performance
for business studies students in the UK but qualified the outcomes of their study by suggesting
that other factors including individual student strategies (eg Kember et al., 1995), competencies,
personality and lecture quality were also important. A much more positive relationship was
obtained by Newman-Ford et al (2008) who looked at 22 first year modules in four humanities
award programs in the UK using an electronic class attendance monitoring system.
Factors such as age, gender, socioeconomic status, affluence, entry pathway, attendance type,
year of study, language spoken at home, family responsibilities, distance between home and the
university, etc, may also contribute to poor performance and be as important or significant as
lecture attendance. Paisey and Paisey (2004) concluded that, apart from illness, paid employment
was the most likely reason for poor attendance. Students work as they may have to pay for all or
part of their education and are less likely to receive grants from government sources than they
were in the past (Paisey and Paisey, 2004). Employment may clash with their study timetable.
Faculty of Science L&T grant application 2013
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Students may be less focused and more stressed due to their finances. Linked to the likelihood of
combining paid employment with study are demographic factors such as those listed above, and
these have been found to influence study patterns (Oakley et al., 2007).
We are now able to mine some of this data from the student system to examine whether there are
statistically significant correlations between this and academic performance. Most of the studies
cited above relied on manual collection of data including both attendance and demographic data.
This may introduce errors and be difficult to collect from those student who do not attend.
Many studies are from the 1990’s or early 2000’s. Much has changed in the higher education
sector since then, in particular, the use of online learning platforms such as Blackboard and
Moodle have become common. These platforms allow students to study in a flexible manner and
this may (Traphagan, et al., 2011; Sawon et al., 2012) have an impact on attendance. They may
choose to miss a lecture but can listen to it minutes later online. Oakley et al. (2011) found no
statistical correlation between attendance and performance when lecture notes were available
online and that reliance on online material was influenced by age with younger students much
more likely to access their coursework online. Traphagan et al. (2011) found that US students
accessing webcasts rather than attending lectures in a science unit did better than the other cohort
who attended lectures.
Very few studies have used science students as the study group. Most have focused on business
students. There is insufficient research to show whether the findings of these studies can be
transferred to classes and cohorts in science or similar disciplines and some suggest that it is not
transferable (eg Dolnicar, 2005). Classes in business schools tend to be large and use a
combination of lectures and tutorials whereas some science disciplines have smaller classes
(particularly above 100 level) and use laboratory and practical sessions as well as tutorials and
lectures. Students in smaller classes have more time to interact with staff and often work in
groups in laboratory and practical classes. Both these factors have been found to increase
engagement and improve student attitudes to their studies (Dadd, 2009; 2011; Fitzpatrick et al.,
2011). In contrast, many of the business students studied by Sawon et al. (2012) found lectures
boring and too easy which lead to absenteeism and poor engagement.
References
Chen, J. & Lin, T-F., 2008. J Economic Education, 39, 213-227.
Clark, G., Gill, N., Walker, M. & Whittle, R., 2011. J Geog. in Higher Ed, 35, 199-215.
Dadd, K.A., 2011. Asian Social Science, 7, 50-60.
Dolnicar, S., 2005. Quality in Higher Education, 11, 103-115.
Fitzpatrick, J., Cronin K. & Byrne E., 2011. European J Engin. Education, 36, 301-312.
Kember, D., Jamieson, Q.W., Pomfret, M. and Wong, E., 1995. Higher Ed, 29, 329–43.
Moore, S., Armstrong, C. & Pearson, J., 2008. J Higher Ed Policy & Manag., 30,15–24.
Newman-Ford, L., Fitzgibbon, K., Lloyd, S. & Thomas, S., 2008. Studies in Higher Ed, 33, 699–
717.
Oakley, G., Lock, G., Budgen, F. & Hamlett, B., 2011. Aust J Teacher Ed, 36, 31-47.
Paisey, C. & Paisey, N.J., 2004. Accounting Ed: An International Journal, 13, 39-53.
Rodgers, J.R., 2002. Australian Economic Papers, 41, 255-266.
Sawon, K., Pembroke, M. & Wille, P., 2012. J Higher Ed Policy & Manag., 34, 575-586.
Traphagan, T., Kucsera, J.V. & Kishi, K., 2010. Ed Technology Res Dev., 58, 19–37.
Wigley, S.C., 2009. J Further & Higher Education, 33, 183-190.
Faculty of Science L&T grant application 2013
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Approach and Methodology
There are two parts to this project that require data to be collected in different ways. Firstly, a
study of student attendance at lectures and how this relates to student performance. Attendance
data is not currently collected on a regular basis and not via electronic means. The plan is to
collect this data using the student Campus Card and CARDEX readers at the entrance to lecture
theatres. Work on this has already commenced and a list of lecture rooms with CARDEX access
is being compiled. Information on student performance is available in electronic form and easily
accessed in association with student numbers. However, in order to match this information, the
identity of students attending class will be known until it can be matched with performance data
and de-identified. A human ethics proposal has been submitted to gather this data. All classes
selected for this study will have an iLearn site associated with the unit. The extent to which this
is used to convey information, particularly that included in the lectures, will be examined.
The second part of this study makes use of student data in the online student systems in the
university. Mining of this data was the basis of an ISP grant in 2012 and this part of this project
will utilize the results of the earlier work.
Delivery of material in science disciplines is traditional - lecture plus laboratory practical and/or
tutorials. This study will inform future directions such as whether traditional lectures can be
replaced by more intensive small group work in practical-style sessions.
This will have
implications for student engagement. There are also cost implications, as small group learning is
more intensive and can be more expensive. The cost of this change needs to be compared to the
cost of infrastructure changes.
This study will also help inform university policy and direction on online learning and help those
in policy-making positions to determine the level of further investment in online delivery and the
role of the lecture. A move away from lecture delivery has significant implications for
infrastructure investment across the university.
Results from this project will be disseminated through journal publication (eg Journal of Higher
Education, Journal of Higher Education Policy and Management) and presentation at fora such as
L&T Week. We will also use the results to leverage further funding for a larger crossinstitutional study. A colleague at Charles Sturt University in Bathurst has already expressed
interest in collaborating and this would provide data for a smaller institute that specializes in
distance education. A collaborator will also be sought from one of the larger “sandstone”
universities to provide a different perspective.
Project team: AProf Kelsie Dadd (project leader; science liaison), Mr Jamie Gabriel (student
systems data analyst), Dr Ayse Bilgin (data manipulation and statistics).
The project leader will oversee the direction, timeline, budget and deliverables (there is a budget
allowance for teaching relief for the project leader). Mr Gabriel will be in charge of the mining
and manipulation of data from the student systems and Dr Bilgin will assist with the statistical
tests required to validate the data.
Faculty of Science L&T grant application 2013
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Timeline
The proposed timeline extends beyond the December 2013 deadline. Data for lecture attendance
will be collected until classes end in November and this will be matched with final grades. Data
analysis matching attendance to final grades cannot start until the grades are finalized in
December and therefore the timeline shows this extending into 2014. The results of the work
will be presented during L&T Week in September 2014 and to the Faculty in 2014.
July–
Sept
2013
Human ethics application
Prior
to
start
xxxxx
Literature review
xxxxx
xxxxx
xxx
Activity
Collab with ISP grant on data mining
Data collection in classes
Data analysis
Reports and publications
Presentation
OctDec
2013
JanMar
2014
xxxxx
xxxxx
xxxxx
xxxxx
xxxx
April- July–
June Sept
2014
2014
xxxxx
xxxxx
xxxxx
xxxxx
xxxxx
xx
xxxxx
xxxxx
xx
Evaluation
xxx
xxx
Budget and Justification
Budget
Personnel (salaries + on-costs)
Research assistant at Level A (Step 2) for 5 hours per week for 10 weeks plus 28%
on-costs.
A research assistant is requested to help with data collection and analysis. One-day
per week for 10 weeks should be sufficient to complete a significant portion of the
project in the allowed timeframe.
Teaching relief
Demonstrator/Marking assistance for S1 at 2hr/week for 13 weeks + 28% on-costs
Demonstrator/marking assistance is requested for the life of the project. The project
leader has a full teaching load and this support will ensure that there is time to devote
to the project.
Total required
Faculty of Science L&T grant application 2013
$
2716
1436
4197
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