TITLE: Edinburgh Teaching Award
DATE: 2019-05-10
AUTHOR: John L. Godlee
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I’ve just finished a personal development training programme at the
University of Edinburgh called the Edinburgh Teaching Award, which
aims to encourage teachers to develop their skills in teaching and
generally pay attention to how they teach, rather than seeing it as
a chore that they HAVE to do. I received my certificate from the
Higher Education Academy today which says that I’m now an Associate
Fellow. Following that, I figured I would post here the reflective
short articles that formed my submissions for the Teaching Award.

Discussing study design

When facilitating discussion groups for the purpose of designing
student group projects, I find that initially they begin very
quietly, regardless of the students’ level of experience. I
considered that this grew from students not knowing what to expect
from this particular discussion, they didn’t know what to talk about
as this was their first time meeting together. Gibbs (2014) suggests
that a teacher should aim to set student expectations early and make
students aware of their place in the classroom heirarchy (A5, K2).
To remedy this fear of the unknown and to help set students’
expectations for the tone of the discussion, I avoided overly
open-ended questions like “What hypothesis do you want to test?”,
early on in the discussion. From earlier teaching experience I have
found that these kinds of questions favour a particular kind of
student who is quick and confident in their response, and can lead
to the rest of the discussion being dominated in this way; most
students want time to contemplate their choices before vocalising.
Instead I asked more tangible, directed questions on a narrow topic,
though related to the larger subject. Questions such as “Who can
tell me one environmental factor they think affects forest canopy
cover”. This helped to build confidence and get the students to
think about the topic in approachable, testable terms, rather than
being overwhelmed by the seemingly infinite possibilities of
designing a study. During a 4th year discussion I asked one of the
students to take note of the answers to these ‘brainstorming’
questions, which we then used as a jumping-off point for part of the
discussion, which turned into a lively debate. In the 4th year
discussions, I felt confident relinquishing my role as leader at
this point, as students already knew the different steps of
developing a research question and were more confident in their
approach to group discussion. In these cases I remained present in
the discussion group, acting as a peer within the group rather than
a leader.

In my 2nd year group I found that there was a large disparity in how
interested individual students were in the discussion process, and
also greater uncertainty in debating certain aspects of the
experimental design, such as statistical analysis. I adopted the
advice of Rogers (1969) and attempted to provide a “facilitative
degree of structure” to the study design process. During these
discussions I had to remain more present to encourage deep thinking
and make students aware of the possible lines of inquiry the study
could follow. However, I made sure to allow the students to come to
their own conclusion of what method to follow. In order to ensure
that all students took an active role in the discussion group, even
if they didn’t say much during the discussion itself, I asked each
student to produce a concise set of minutes on what was discussed in
the first part of the meeting, and to record anything they “didn’t
think of until after the session”. These points were then discussed
during the second, shorter part of the meeting. This post-discussion
evaluation helped quieter students to contribute to the discussion
without needing to keep up with the louder students during the main
discussion (V1, V2).

References:

Gibbs, G. (2014). 53 Powerful ideas all teachers should know about.
Staff and Educational Development (SEDA) Online blog. Available at:
https://www.seda.ac.uk/53-powerful-ideas/. Last accessed: 19th Sep
2018

Rogers, C. R., & View, L. A. (1969). Freedom to Learn: A View of
What Education Might Become. Columbus, OH: Merrill Pub. Co. 

Remaining visible throughout data collection

Harland (2012) suggests that “The chance corridor conversation can
be as critical to a student’s learning as the structured tutorial”
(V3). I considered this during my teaching on a field course.
Although the 4th year students were largely left to their own
devices deliberately, and demonstrators were told they didn’t have
to follow their groups around, I made sure that I was available and
visible throughout the day, so my students could easily come and ask
me questions when they needed to. I positioned myself in public
areas and aimed to check in with my groups at least once every 2.5
hours during the working day. I also met with my students in the
evening to discuss how the day had gone in an informal manner. While
these periods of “checking-in” appeared to the students to be just
that, I attempted to use the time to stimulate discussion and prompt
self-evaluation of the work so far, with some formative feedback
from me when appropriate (A3). I also tried to prompt students to
think about the details of the methods they had been using and
taught them finer details (K1). One such example being the use of
hemispherical photography of forest canopies, a complex technique
which requires some knowledge of optics to truly understand. I found
that during these post hoc discussions the students were much more
engaged than when I attempted to teach them before they had used the
photography equipment, presumably because it was easier to link this
abstract knowledge to something more tangible (Boud et al. 1985).

There is often a temptation to get things done as quickly as
possible during fieldwork, with students preferring to stick with a
bad methodology rather than change to a new one due to a perceived
waste of time if they switch. In contrast, critically evaluating
methodology and potentially switching to a new or adapted method is
a valuable learning experience which mirrors real research, and
should therefore be encouraged (V4).

References

Boud, D., Keogh, R., & Walker, D. (1985). Reflection: turning
experience into learning. Learning.

Harland, T. (2012). University Teaching. London: Routledge.

Quantitative Skills in Ecology

The difficulties of effectively teaching quantitative skills in my
field (ecology) are well known (Barraquand et al. 2014). For many
undergraduate ecologists, learning about mathematics and statistics
is not what they signed up for (Duffy 2017). Knowledge of how and
when to apply different statistical methods however, is vital for
graduating ecologists looking to continue in research. It is also a
desirable skill in industry outside of the life sciences, with many
graduate positions preferring students with a background in R, the
current industry standard for statistical computing (TIOBE 2018)
(V4). The question then, is how to support learning of quantitative
skills (A2) and develop effective learning environments for teaching
statistics (A4).

In conversations I’ve had with undergrauate ecologists, they often
speak of having a ‘fear’ of statistics. Probing deeper, this fear is
actually a composite of a fear of mathematics and of computer
programming (V2). For many, this fear has developed through repeated
exposure to traditional statistics classes which merely reinforce
that this is an arcane, impenetrable, and fundamentally boring
field.

In 2016, along with a fellow undergraduate, we set up Coding Club
(https://ourcodingclub.github.io), a learning network with an online
presence and in-person workshops which focusses on collectively
teaching each other about statistics, in a peer-to-peer fashion. We
hold weekly workshops based on changing themes such as linear models
or spatial data visualisation.

We found that leaving most of the learning in the hands of the
students is a much more effective teaching model for quantitative
skills, which require practice and critical evaluation at the
student’s own pace, which will of course be different to that of
others in the class. Students often said they felt like they had
failed when the group moved on before they had fully understood what
they had coded (V2). It’s not that quantitative skills are
necessarily hard or easy by their nature, it’s rather that they take
time, and that this time varies between students. This is why a
non-conventional learning environment like Coding Club works so
well. We decided to avoid the model of another popular quantitative
skills program called Data Carpentry (https://datacarpentry.org),
which focusses a lot on “live coding” by a demonstrator. In
practice, while this method can convey a lot of information in a
short period of time, it is debatable whether that learning is
‘deep’, does the student know why they typed the code (V3)? When a
student has a problem during a Coding Club workshop, whether that is
to do with the prescribed theme or something that they have brought
in from outside, we encourage small groups to form in an ad hoc
manner so multiple people can talk about the issue, which is often
resolved much quicker than a single student staring at code on a
screen and with benefits for both the impromptu teacher and the
student. Conversation helps to rationalise issues which are
initially abstract and difficult to solve (V1). Unfortunately,
current provision of teaching hours and the teacher:student ratio in
many undergraduate quantitative skills classes precludes teaching in
this manner, hence why we felt it was necessary to act outside of
the prescribed timetable.

References

Barraquand, F., Ezard, T. H., Jørgensen, P. S., Zimmerman, N.,
Chamberlain, S., Salguero-Gómez, R., Curran, T. J., Poisot, T.
(2014). Lack of quantitative training among early-career ecologists:
a survey of the problem and potential solutions. PeerJ, 2, e285.

Duffy, M. 2017. Poll results: How mathy are ecology, evolution, and
genetics? [Online] [Accessed 21st Nov 2018]. Available at:
https://dynamicecology.wordpress.com/2017/09/25/poll-results-how-mathy-are-ecology-evolution-and-genetics/

TIOBE 2018. TIOBE Index for November 2018. [Online] [Accessed 21st
Nov 2018]. \ Available at: https://www.tiobe.com/tiobe-index/