The Effect of Hybrid Training Combined with On demand Classes

One aspect of life hit hard by the COVD-19 pandemic in 2020 was education in general and, in this case, actual ship training. The same is true for anchoring training, implemented annually as part of maritime training. Therefore, to conduct ship training effectively and briefly, this study advocates hybrid training, that is, shortened practical training combined with on demand video learning materials created and distributed to students in advance. Group work on ship handling planning that lasts for three to six hours is normally conducted annually in traditional classes. However, because of the pandemic, the allotted time for group work was reduced to approximately 1.5 hours. Therefore, in 2020, we conducted hybrid training, that is, anchoring training that included on demand video learning materials. Compared to students’ 66.3% average achievement rate in 2018 and 2019, the average in 2020 was 78.3%. ‘Anchoring procedure’ and ‘altering course’ items showed a particularly large gap in evaluation. The following were effects of hybrid training: (1) Videos that can be watched in advance can also be viewed repeatedly at one’s own pace. (2) The capacity to watch videos via smart phones provides students with better access. (3) As videos can be watched in advance, students become familiar with concepts before training, thus leading to better understanding of the training’s content and purpose. These results demonstrated hybrid training’s effect when practical training is combined with on demand materials.

Investigation of time period of activity for on-demand classes among pharmacy students during the COVID-19 pandemic

Background The COVID-19 outbreak brought about major changes in the format in which university lectures were delivered in Japan. Most classes changed from face-to-face classes to remote education methods to prevent infection. This study investigated the relationship between lecture comprehension with on-demand classes and the use of time to complete lecture-related assignments during COVID-19. Methods The eligible participants were 139 third-year pharmacy students; they received seven pharmacotherapeutics lectures via on-demand classes from September to November 2020. We classified the participants into three groups according to the period between lecture upload to the submission of related assignments: submission on the day of upload (“early,” n = 42); the 2nd day to the day before the deadline (“intermediate,” n = 58); and on the deadline day (“late,” n = 39). In our analysis, we mainly used Spearman’s correlation coefficients to examine the correlations among end-of-semester examination scores, total assignment scores, and time of assignment submission. Results A strong positive correlation was evident between test scores and total assignment scores in the early group (r = 0.68, P < 0.001) and a weak correlation in the intermediate group (r = 0.27, P = 0.038), but there was no correlation in the late group. We observed a negative correlation between test scores and assignment submission time in the early group (r = − 0.59, P < 0.001) but not in the intermediate or late groups. Conclusions On-demand classes give university students the opportunity to choose when they wish to study. Remote education offers a certain amount of freedom and encourages students’ initiative; however, our results suggest that some students cannot make the best use of on-demand lecture characteristics and act appropriately. Therefore, while taking advantage of on-demand education during COVID-19, it is necessary to consider students’ use of time.

Benders Decomposition for the Profit Maximizing Capacitated Hub Location Problem with Multiple Demand Classes

This paper models the profit maximizing capacitated hub location problem with multiple demand classes to determine an optimal hub network structure that allocates available capacities of hubs to satisfy demand for commodities from different market segments. A strong deterministic formulation of the problem is presented, and a Benders reformulation is described to optimally solve large-size instances of the problem. A new two-phase methodology is developed to decompose the Benders subproblem, and two effective separation routines are derived to strengthen the Benders optimality cuts. The algorithm is enhanced by the integration of improved variable-fixing techniques. The deterministic model is further extended by considering uncertainty associated with the demand to develop a two-stage stochastic program. To solve the stochastic version, a Monte Carlo simulation–based algorithm is developed that integrates a sample average approximation scheme with the proposed Benders decomposition algorithm. Novel acceleration techniques are presented to improve the convergence of the algorithm proposed for the stochastic version. The efficiency and robustness of the algorithms are evaluated through extensive computational experiments. Computational results show that large-scale instances with up to 500 nodes and three demand classes can be solved to optimality, and that the proposed separation routines generate cuts that provide significant speedups compared with using Pareto-optimal cuts. The developed two-phase methodology for solving the Benders subproblem as well as the variable-fixing and acceleration techniques can be used to solve other discrete location and network design problems.