5. Scholarship

Evaluating and sharing effective teaching practice is central to my teaching practice. I have long been surprised by the disconnect between how research and teaching are treated by many academics in the sciences. In scientific research, we situate our work in the context of existing scholarly literature, rigorously evaluate our assumptions and outcomes, and share our results widely -- if the work hasn't been communicated, it may as well not have been done at all. Why isn't this the norm for teaching science?

I approach teaching science the same way I approach conducting research. As an example, I describe here the process I have used for developing, evaluating, and disseminating new curriculum to teach computing and modelling skills to earth and environmental science students.

Start from the literature

Once it was decided that I would be teaching computing skills in our first-year curriculum, my first step was to dive into the scholarly literature. My goals were to better understand how students learn the complex skills of problem-solving and programming, to become aware of any pitfalls and challenges I may face in teaching these skills, and to identify best practice and strategies to overcome these challenges. I then integrated findings from the literature into the curriculum. For example:

• A significant body of literature has shown that one of the major challenges for novice problem solvers is that the high cognitive load associated with using working memory to apply new techniques can prevent students from drawing from and building on existing mental models stored in long-term memory (Hodges, 2015, pp 68-70 and references therein). One suggested strategy to address this problem is asking students to articulate in writing what they are thinking as they solve problems – therefore building their metacognitive awareness and connecting their step-by-step solutions with their conceptual understanding (Hodges, 2015, pp 76-77). I implemented this by including an explicit marking criterion (“comments”) on every exercise that required students to explain each step in language that their peers could understand.
• Trafton and Reiser (1993) found that students learn new techniques most effectively when worked examples are followed immediately by problems for students to solve, a strategy that helps students form “rules” that apply to both the example and the problem. For programming specifically, Lahtinen et al. (2005) also found students considered examples to be the most useful type of learning material. I implemented these findings by interleaving worked examples and problems to be solved in every weekly exercise (Figure 1).
• Jacobs et al. (2016) reported on strategies developed over five years of teaching introductory programming to geoscientists (a similar cohort to my students) and made their final, tested materials and strategies available online. I used these as a starting point for my curriculum, adapting to increase the relevance to earth and environmental science students (an important consideration for non-computer-science students learning programming; Forte and Guzdial, 2005).

Evaluate the outcomes

With support from a faculty Strategic Educational Development Initiative (SEDI), I have been evaluating how the new curriculum shapes our students’ aptitude and attitudes towards computing skills. In addition to collecting traditional feedback (e.g., student evaluations, peer observation, reflection), I survey students at the beginning and end of the semester using a validated survey instrument (Hoegh and Moskal, 2009) that measures (1) their confidence in their ability to learn computing skills, (2) their interest in learning these skills, and (3) their perceptions of how useful the skills will be. An example of preliminary data analysis is shown in Figure 2. I am currently in the process of linking survey results to student performance (quiz results) and behaviour (self-reported and as recorded in quiz data) to understand how I might further modify the curriculum and teaching strategies to improve student learning and student perceptions of their own abilities.

Disseminate best practice

I have now delivered the new curriculum to three cohorts of UOW students (~350 students). While I continue to improve the materials each year in response to feedback and reflection, they are now relatively mature – and I consider it imperative to share them widely.

I am currently preparing a publication (targeting the Journal of Geoscience Education) covering both the curriculum and the evaluation of survey data discussed above. This will be submitted once I have collected additional years of survey data. In the meantime, I have also presented this work to a variety of audiences:

• At the faculty level, via a poster at the Innovation in Education: Learning and Teaching in the Faculty of Science, Medicine and Health Forum, December 2016
• At the university level, via an oral presentation at the WATTLE Forum Hybrid Learning @ UOW, February 2019
• At the national level, via an oral presentation in the Education and Outreach session of the Australian Meteorological and Oceanographic Society National Conference, February 2017
• At the international level, via an oral presentation in the session Education Through Exploration: Research-Oriented Teaching and Data Management in the Digital Age Il of the American Geophysical Union Fall Meeting, December 2017.

More broadly, I have made all of my materials available online with explicit permission for others to reuse and modify as needed. I have shared the link in my scholarly presentations listed above, via Twitter, and directly with colleagues teaching at other universities (Glendale Community College, University of Miami). The response has been incredibly positive (Figure 3). I have even now had a former student contact me about adapting the materials for use in his new job teaching high school computer science!

References

Hodges, L. C. (2015). Teaching undergraduate science: A guide to overcoming obstacles to student learning. Stylus Publishing, LLC.

Hoegh, A., & Moskal, B. M. (2009). Examining science and engineering students' attitudes toward computer science. In 2009 39th IEEE Frontiers in Education Conference (pp. 1-6). IEEE.

Forte, A., & Guzdial, M. (2005). Motivation and nonmajors in computer science: identifying discrete audiences for introductory courses. IEEE Transactions on Education, 48(2), 248-253.

Jacobs, C. T., Gorman, G. J., Rees, H. E., & Craig, L. E. (2016). Experiences with efficient methodologies for teaching computer programming to geoscientists. Journal of Geoscience Education, 64(3), 183-198.

Lahtinen, E., Ala-Mutka, K., & Järvinen, H. M. (2005). A study of the difficulties of novice programmers. ACM Special Interest Group on Computer Science Education Bulletin, 37(3), 14-18.

Trafton, J. G., & Reiser, B. J. (1993). Studying examples and solving problems: Contributions to skill acquisition. In Proceedings of the 15th conference of the Cognitive Science Society (pp. 1017-1022).