Teaching Statement

TEACHING STATEMENT

Over the years, I have found that four principles guide my teaching practice as an instructor in formal classroom settings and mentor for student learning. They are:

  1. Problem Based Learning
  2. Balancing Technical Mastery and Conceptual Maturity
  3. Teaching with Technology
  4. Creating a Lab Learning Environment

Problem Based Learning

Problem based learning is a cornerstone of my teaching approach.

My philosophy of teaching emerges from a fundamental belief in the role of problem solving in the acquisition of knowledge. My own experience is that learning happens best once I find myself using the tools, facts and concepts at my disposal in application to questions that I am curious about. As a learner gains experience solving problems, confidence and independence is fostered that I believe to be core to any creative activity both inside and outside of the academy.

Solving problems – from the simple to the complex – empowers the student to be less dependent on rote knowledge, teaching instead the importance of understanding the nature of asking good questions, having the confidence to try ideas and develop intuition, and the satisfaction of participating in the full cycle of discovery.

For environmental science, by focusing on problem solving techniques the learning experience can embrace the complexity of the natural world, showing how a variety of approaches and assumptions can give varying solutions to a given problem. I feel this more abstract view of knowledge is crucial within an interdisciplinary teaching context, and will only become more important as the scale of facts and complexity of tools we use on a daily basis grows.

Balancing Technical Mastery and Conceptual Maturity

Striking a good balance between technical mastery and conceptual maturity is crucial for effective teaching in quantitative environmental science.

My experience teaching theoretical and conceptual courses in the biological sciences has revealed a conundrum: students need to learn both the tools (mathematical and programming) and ecological ideas to which those tools are applied. However, focusing on the latter deprives students the ability to manipulate and engage with the ideas on their own, while focusing on the former can detract from the core learning objective: to consider biological problems. My early attempts to teach both simultaneously often resulted in having the course reduced to a technical introduction, with students barely understanding why these methods were needed, as there was little time left to consider biological problems once basic mastery of the tools was complete.

I see this as a challenge which teaching in our field must confront, or risk alienating students (and stakeholders) who are ill-prepared to connect necessary and specific technical knowledge with the problems which drive their curiosity and that they ultimately want to solve.

More technical fields will often resolve this by having many core classes that teach the tools, but the interdisciplinary nature of modern biological sciences will rarely result in students with a consistent set of underlying technical knowledge. Such has been my experience with formal university teaching as well as less formalized mentorship of graduate students. Therefore the importance in trying to find a successful middle ground between technical mastery and conceptual maturity is, I believe, a crucial component of effective teaching in quantitative environmental science.

Teaching With Technology 

There is great potential for technology to aid learning, and I am active in exploring how this can be done effectively.

I am interested in how the emerging growth of powerful teaching platforms can be applied in developing more interactive teaching materials (e.g. interactive graphics, video, computational documents). As such, I was involved in the creation of a set of example teaching materials designed to teach third year undergraduate ecology students an introduction to the theory of population and community ecology. We chose to have no programming component in the course and instead designed simple interactive notebooks that allowed the students to manipulate the biological structure of the models and experiment with the output in a directed, but conceptually driven manner. By allowing the models to have a wider set of outcomes than just what we asked the students to explore, we created a learning experience akin to conducting a simulation experiment, where potentially incorrect answers could be found.

Though only a pilot study, I was amazed by the engagement that the students had with this approach.

Many students came to office hours to discuss simulations they had tried that went beyond the course work; the interactive platform prompted students to ask (themselves and teachers) difficult questions on the behavior they observed, further reinforcing the conceptual ideas we were focusing on. Students that were more focused on specific tasks and techniques associated with course material also benefit from the ability to explore the concepts we were teaching directly, often exhibiting proficiency in the logical connections between the theories. I look forward to further developing and extending similarly engaging course materials, such that they ultimately empower students to design their own simulation and conceptual models without this scaffolding.

Creating a Lab Learning Environment

Diversity strengthens learning in a supportive lab environment.

While a graduate student, I was part of a lab which comprised a diversity of technical backgrounds including those from a purely fieldwork perspective with no theoretical training, students using microcosm experiments trying to test theoretical models directly, as well as a few of us working on largely theoretical questions.

This environment was extremely influential on me as it fostered a rich culture of knowledge sharing and mentorship.

In this setting I often taught programming and approaches to modelling or analyzing numerical data to my peers. Unlike teaching undergraduates, teaching at a higher level – to someone who is research oriented and keen to start on a specific project – presents different challenges. I found that success in teaching in this faster atmosphere required a project approach, wherein we first simplified the model that could be a candidate for the student’s full project into the absolute simplest form. After this simplified model is developed focusing on the technical details in analyzing this system, the everyday requirements of program structure, mathematical tools could be developed in an iterative, problem driven format. As the problem is related to the larger problem that the student wishes to analyze, it is possible to use their biological intuition and knowledge of experimental designs to help make the programming concrete and self-directed.

Though seemingly slower than just having the student manipulate more direct models and data that they are interested in, my experience suggests it saves time in the long run when the students are able to learn these more fundamental approaches to problem decomposition and design, as it gives the student a sense of how to structure and adapt individual pieces in a manner that is much harder to grasp when trying to manipulate other peoples code that might have been inherited. I found this approach extremely successful in both one on one instruction as well as in small seminar style groups that I organized.

A list of the courses I have taught is available here.