Your SlideShare is downloading. ×
0
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
What do students do outside of class time?
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

What do students do outside of class time?

941

Published on

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, …

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.

Published in: Education, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
941
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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? 2
  • 3. Out-of class activities•  In a survey of science and engineering students, 53% said their study habits were influenced by on-campus spaces 3
  • 4. Student study networksAims:•  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 4
  • 5. Methodology 5
  • 6. Data collection Number of responsesSemester - 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%) 6
  • 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 7
  • 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 8
  • 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%) 10
  • 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 15
  • 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 16
  • 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 17
  • 18. AcknowledgementsThanks to:•  HEA Physical Sciences Centre for a Departmental Funding Grant•  Darren Hendrie and Saul Kohn, University of Edinburgh 18
  • 19. References•  Eric Brewe, Laird Kramer & George OBrien, ‘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) 19

×