In our courses, we supplement whole class lecture sessions with a variety of timetabled workshop / tutorial / example class sessions. All courses require additional study outside formal classes, usually centered around solving problems associated with the current section of the course. We know next to nothing about what students do during these out of class sessions. Do they work along? Together? Do self-study networks persist over time? This talk describes work that seeks to shed light on patterns of informal group study amongst Physics students, investigating what these informal sessions are used for and how this changes over time and across different levels of the programme. We describe different attempts to gather representative data from all students across our physics programmes, at multiple points during the year. Data that was collected from students captured demographic data (gender, degree intention etc) along with details of peers with whom a particular survey responder had interacted in the past two weeks. This was used to construct network graph plots of interactions, which revealed little if any inter-year interactions. In first year, a significant quantity of network interactions involved members outwith the physics class, possibly even outwith the university. We also present analysis that correlates network membership and ‘connectedness’ with end of course performance.

1. What do students do outside of
class time?
Judy Hardy, Darren Hendrie,
Simon Bates, Ross Galloway
j.hardy@ed.ac.uk
Physics Higher Education Conference, 8/9 September 2011

2. What do students do outside of class time…
and where do they do it?
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3. Out-of class activities
• In a survey of science and engineering
students, 53% said their study habits were
influenced by on-campus spaces
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4. Student study networks
Aims:
• To understand the patterns of informal
group study
– Using social network analysis tools
• To identify what types of interaction
are most effective
– So that student learning can be supported and
promoted
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5. Methodology
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6. Data collection
Number of responses
Semester - Collection (response rate)
Week method
Year 1 Year 2 Year 3 Year 4 Year 5
S1 w3 Online 28 63 47 21 4
Minute paper 122 118
S2 w4 44 21 7
in class (60%) (60%)
Minute paper 99 114
S2 w10 37 24 2
in class (48%) (58%)
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7. Characterising Networks
In- Out-
Degree Degree
Alice 1 1
Bob 1 2
Carol 2 1
Ted 3 3
• In-Degree: arcs terminating at node
– receptivity, popularity, prominence
• Out-Degree: arcs originating at node
– influence, expansiveness, gregariousness
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8. Centralisation
• Freeman’s graph centralisation
• Measure of the range of Degrees of actors
in a network
• Expressed as a percentage of those in a
star network of the same size
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9. Year 1 (s2 w4)
Blue: Physics Yr 1
Red: Physics Yr 2
Grey: non-Physics
In-Degree Out-Degree
Mean 1.5 1.5
Min 0 0
Max 10 7 9

10. Laminar networks
• Year 1 students:
Non-
Yr 1 Yr 2 Yr 3 Yr 4 Yr 5
Physics
122 5 51
S2 w4 - - -
(69%) (3%) (29%)
99 2 32
S2 w10 - - -
(74%) (2%) (24%)
• Year 2 students:
Non-
Yr 1 Yr 2 Yr 3 Yr 4 Yr 5
Physics
118 2 1 22
S2 w4 - -
(83%) (1%) (1%) (15%)
114 1 9
S2 w10 - - -
(92%) (1%) (7%)
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11. Male study networks
In-Degree Out-Degree
• Year 2 (s2 w4)
Mean 1.8 1.8
Min 0 0
Max 11 7 11

12. Female study networks
In-Degree Out-Degree
• Year 2 (s2 w4)
Mean 1.4 1.4
Min 0 0
Max 12 6 12

13. On-campus networks
In-Degree Out-Degree
• Year 2 (s2 w10)
Mean 1.8 1.8
Min 0 0
Max 12 5 13

14. Home-based networks
In-Degree Out-Degree
• Year 2 (s2 w10)
Mean 1.2 1.2
Min 0 0
Max 5 4 14

15. Effect on final course grade
Out-Degree
• First years
ANOVA: d.f. F Sig Eta-sq
S2 w4 5 1.675 0.143 0.083
S2 w10 5 1.722 0.157 0.083
• Second years
ANOVA: d.f. F Sig Eta-sq
S2 w4 5 0.457 0.810 0.018
S2 w10 5 2.266 0.055 0.097
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16. Effect on final course grade
In-Degree
• First years
ANOVA: d.f. F Sig Eta-sq
S2 w4 5 4.245 0.008 0.187
S2 w10 5 3.781 0.021 0.166
• Second years
ANOVA: d.f. F Sig Eta-sq
S2 w4 5 1.201 0.302 0.047
S2 w10 5 4.966 0.001 0.191
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17. Conclusions
• Students build extensive communities of
learning outside of class time
• Networks are “laminar”
– Few interconnections between year groups
• Physical space is important
– The most extensive networks exist in
University social and group study space
• Some evidence that In-Degree (receptivity,
popularity, prominence) is linked with
performance
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18. Acknowledgements
Thanks to:
• HEA Physical Sciences Centre for a
Departmental Funding Grant
• Darren Hendrie and Saul Kohn, University
of Edinburgh
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19. References
• Eric Brewe, Laird Kramer & George O'Brien,
‘Investigating Student Communities with Network
Analysis of Interactions in a Physics Learning
Center’, AIP Conf. Proc. 1179, 105 (2009)
• Robert A. Hanneman and Mark Riddle,
‘Introduction to social network methods’,
available online at
http://www.faculty.ucr.edu/~hanneman/nettext/
• Stanley Wasserman & Katherine Faust, ‘Social
Network Analysis: Methods and Applications’,
Cambridge University Press (1994)
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