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Background - One of the most critical challenges in engineering education is improving students’ divergent thinking skills. Usually, we observe students’ fixating on only one single solution for engineering problems. However, their ability to think outside the box and provide alternative solutions should be developed. Research shows that engagement may foster the development of thoughts and boost creativity. Purpose/Hypothesis – Our aim was to investigate students’ engagement with tasks that inspire different facets of creativity (verbal, numeric, and visual). Considering the role of demographics in student engagement, we explored the relationship between their engagement level and demographic traits such as gender, major, age, grades (GPA), and the languages they know besides their native tongue. Design/Method - We utilized electrodermal activity (EDA) sensors, a well-documented proxy of emotional engagement, to measure students’ engagement level while performing tasks that inspire different facets of creativity (verbal, numeric, and visual). Due to the non-normal distribution of the data, non-parametric statistical tests were conducted considering engagement as a dependent variable and demographic traits as independent variables. Results - Statistically significant differences in students’ engagement when exposed to creativity inspired tasks were observed. However, no association between demographics and engagement levels were detected. Conclusions - The results of the study may support educators in designing the instructional materials considering creativity-inspired activities so that students’ engagement level can be increased. Further, results from this study can inform experimental designs, specifically participant selection, in engagement focused studies. 

The goal of this paper is to provide a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement learning can all be seen through the lens of our framework. We then discuss a generic optimization algorithm for our formulation, called probability functional descent (PFD), and show how this algorithm recovers existing methods developed independently in the settings mentioned earlier.