Sneha R. Krishna Kumaran (CV)

Instructors are integrating the use of online peer review platforms to keep pace with growing class sizes. However, these platforms typically prioritize random peer assignment and only show the current solution. These choices can result in low quality feedback in project-based design courses. We report on an experiment in which students (N=59) worked on twelve-week product design projects and both wrote and received online feedback at four stages. The experiment tested a novel concept of peer mentorship,where peers were assigned to give feedback to all stages of one project, and tested showing feedback context from the preceding design stage when composing feedback. The results showed that displaying context from the preceding design stage led to feedback with higher perceived quality at certain stages of the design process and feedback from mentors promotes more response from the feedback receiver. Our work contributes deeper empirical understanding of how assignment strategies and showing additional context affects peer feedback and provides practical guidelines for instructors to implement these methods in design courses.

Pollinators are an integral part of agriculture and the ecosystem. However, due to changing land use, populations of wild pollinators are decreasing and plant distributions are changing all around the world. To understand how plant-pollinator networks will adapt over time, we would like to understand how pollinators choose flowers to visit. We will model a pollinator’s interaction with plant species in two ways: first using a probabilistic multinomial approach to fit a preference score to each plant and second to explain our findings from the multinomial model using the traits of the flowers themselves. Our findings show that a model with preferences performs better than a model which does not have preferences. While this model shows potential in finding plant preferences, it does not fully explain the distribution of plant-pollinator interactions. To try to explain the interactions more fully, we incorporated the traits of the plants into the score of the plant. We found that the traits do have some effect on the score of the plant, but again do not fully explain the interactions in this particular model.