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 hierarchy (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 enquiry 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).
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).
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 undergraduate 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.
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/