Examining Occupational Therapy Students’ Responses to Integrative Seminars

The integrative seminar is an innovative teaching-learning approach that focuses on active learning and peer collaboration, characteristics that align with millennial learners’ preferences. The use of integrative seminars has been reported by various health professions with positive outcomes. Course feedback survey data from the first cohort of occupational therapy students who participated in a new four-course integrative seminar series were analyzed. Findings suggest that the format of the courses was engaging for the learners. The students particularly valued the small class; the opportunities for peer collaboration; and the variety of active learning opportunities, including simulations. The students also indicated that the seminars helped them to integrate and apply their learning across the curriculum. In another survey completed near the end of their Level II fieldwork rotations, the students indicated that the seminars contributed to their readiness for fieldwork as well as to the development of their critical thinking, interpersonal skills, and professional identity. The findings from this analysis support the potential value of integrative seminars in occupational therapy education

Frugal Optimization for Cost-related Hyperparameters

The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incapable to properly control cost during the optimization process. To address this problem, we develop a new cost-frugal HPO solution. The core of our solution is a simple but new randomized direct-search method, for which we prove a convergence rate of $O(\frac{\sqrt{d}}{\sqrt{K}})$ and an $O(d\epsilon^{-2})$-approximation guarantee on the total cost. We provide strong empirical results in comparison with state-of-the-art HPO methods on large AutoML benchmarks.Comment: 29 pages (including supplementary appendix