Recently, I opened a box containing some of my academic possessions which, years ago, were deemed worthy of being transported across the Atlantic from my native Ukraine. Among them were two artefacts from my secondary studies captured on the photo – high school diploma with honours and ‘silver medal’ that accompanied it. In Ukraine, the medal, like the one on this photo, is given to top students in each graduating class, and reads, “In recognition of high academic achievements, community work and excellent classroom behaviour.”
To me, these artefacts from my academic history are reminders of the years of hard work, self-discipline and work ethic that I developed and nurtured at a young age. At the same time, they remind me of many difficult and frustrating learning moments when, despite effort, hard work and motivation, I struggled to understand basic math concepts and was able to achieve only average performance in math and science.
To explain this learning conundrum, I concluded early on that motivation and effort can take you far as a learner but they are not the only determinants of learning success. Other factors had to account for my differential performance in social sciences and math classes. I reasoned that I was not able to get straight A’s because I was simply not good in math. It never came as naturally to me as humanities and social sciences. Abilities and aptitude had to be the reason for twice the time I needed to spend on my math homework (with parental help to boot) only to achieve average marks.
During my graduate studies, I came across an exciting line of research in educational psychology that looks at individual cognitive and personality differences among learners and might help us explain differential success among learners in our classrooms. This research showed me that motivation, abilities and intelligence are not the only determinants of learning outcomes. A number of other individual variables shape what and how well students will learn. These include:
- prior knowledge and experience which refer to the quality and accuracy of relevant prior knowledge;
- learning strategies and tactics which refer to cognitive and metacognitive strategies used by learners;
- learning or cognitive styles which refer to preferred ways for processing information and approaching a learning task;
- learners’ conceptions of learning and themselves as learners;
- personality (self-esteem, risk-taking, resilience, sensitivity to rejection, tolerance to ambiguity, anxiety, etc.).
In each learning situation, these characteristics of learners interact in complex ways which are not fully understood by researchers. However, I found that being aware of these individual differences – along with cultural, generational and demographic characteristics – helped me be more attentive to diversity among learners and differences in their academic performance. I am encouraged by the central message of this research – most of these characteristics are states not traits and as instructors, we have the ability to influence learning attitudes, conceptions and behaviours of our students and help them become more effective learners.
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Last week while I was judging at a science fair I had a motivating conversation with a small group of lecturers and fellow graduate students. We were sharing our experiences in teaching science when I was surprised that the others believed it near impossible to interactively teach a large class of undergraduate students. It made me ask how many other instructors share this misconception. Motivated by this conversation, I wanted to share some recent evidence that you can engage large classes and some evidence that it works. 