Purpose
This study aims to understand how mode of delivery, online versus face-to-face, affects comprehension when teaching operations management concepts via a simulation. Conceptually, the aim is to identify factors that influence the students’ ability to learn and retain new concepts.
Design/methodology/approach
Leveraging Littlefield Technologies’ simulation, the study investigates how team interaction, team leadership, instructor’s guidance, simulation’s ease of use and previous software experience affects comprehension for both online and face-to-face teaching environments. Survey data were gathered from 514 undergraduate students. The data were then analyzed using structural equation modeling.
Findings
For the face-to-face population, this study found that team interaction, previous software experience, instructor’s guidance and simulation’s ease of use affected student comprehension. This differed from the online population who were only affected by the simulation’s ease of use and instructor’s guidance.
Originality/value
Understanding how the mode of delivery affects comprehension is important as educators develop new online teaching techniques and experiment with innovative technologies like simulation. As demand for online education grows, many instructors find they need to refine their methods to ensure students comprehend the concepts being taught regardless of modality.
Resource planning (RP) in a professional service organization matches workforce resources with project tasks while considering a myriad of factors such as skill requirements, service delivery role, skill type, workforce proficiency level, and geographical location. The multiperiod stochastic resource planning studied in this article extends the one-period deterministic resource planning by explicitly coping with both internal resource attrition and project demand uncertainty in a sequential decision-making framework. It allows resource managers to make effective use of their internal resources and identify the need to outsource to external contingent resources. We model the multiperiod stochastic resource planning as a Markov decision process and implement an approximate dynamic programming algorithm to obtain dynamic and adaptive solutions in reasonable computation times. A comprehensive computational study shows that our approximate dynamic programming algorithm achieves higher profitability and internal resource utilization compared to the rolling horizon approach used as a benchmark.
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