Genevieve Newton, University of Guelph

Profile Photo of Genevieve Newton

Associate Professor Guelph, Ontario newton@uoguelph.ca Office: (519) 824-4120 ext. 56822

Bio/Research

While working as a post-doctoral fellow, Prof Newton had the opportunity to teach Human Biochemistry in a second year undergraduate classroom in the Department of Kinesiology. Prof Newton enjoyed the experience immensely, and after taking an early maternity leave from my fellowship, decided to fo...

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Bio/Research

While working as a post-doctoral fellow, Prof Newton had the opportunity to teach Human Biochemistry in a second year undergraduate classroom in the Department of Kinesiology. Prof Newton enjoyed the experience immensely, and after taking an early maternity leave from my fellowship, decided to focus my attention exclusively on teaching for a period of time. During her brief research hiatus, Prof Newton was very interested in soliciting feedback from students to determine the effectiveness of the teaching strategies that she was using in the classroom. She realized that if structured correctly, she would acquire usable data that could translate into relevant pedagogical findings. This led to her decision to make pedagogy the focus of my research efforts, since it combines her passions for science and teaching together.

Prof Newton's current research projects lie in three areas. First, she is investigating whether using nutrition and nutraceutical based learning modules can facilitate learning and integration of metabolic pathways in a second year Biochemistry course, as well as increase retention of material over the long-term. Second, she is investigating whether breakout groups can be used effectively as an active learning technique across different levels of undergraduate education. And third, the use of technology, (including lecture capture and mobile apps) by undergraduate students and whether it can be used effectively to improve performance. Her overall objective is to identify teaching strategies that can be used to improve learning and that will facilitate deep over surface learning approaches.


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