Capacity management for personalized services in education

Abstract

Operational Tactical Strategic O.1 How many sessions T.1 How much teaching capacity S.1 How many teachers to plan per activity ? to allocate to activities to hire per subject? O.2 Which teachers to assign on each subject, or to learners? S.2 How many classrooms to sessions? T.2 How many classrooms to build? O.3 Which classrooms to assign to allocate to activities S.3 Which seating capacity to sessions? on each subject? to consider for classrooms? O.4 Which time blocks to assign S.4 How frequently to perform to sessions? competency assessments? O.5 Which learners to assign to sessions?In providing these capacity management tools, we use a variety of OR frameworks. Among these are meta-heuristics, simulation, integer programming, stochastic programming, model predictive control, queueing theory and Markov decision processes. In numerically validating and demonstrating the effectiveness of the presented planning tools, we mainly rely on the data coming from Dutch secondary education schools, through collaborating with the Zo.Leer.Ik! schools network.In summary, this thesis has two overarching goals: showing the impact that an Operations Research perspective can make in the transition toward personalization of services in education, and introducing novel operational planning problems on capacity management to the OR literature and providing new solution methods for them. Capacity management tools for service providersWhen educational service providers manage their available resource capacity (i.e., teaching capacity, classrooms and time blocks) to serve the personalized learning demands of many learners, they face several logistics planning decision problems. We identify these in Table 1.1 at operational (O.1-O.4), tactical (T.1-T.2) and strategic (S.1-S.4) decisionmaking levels for a service system that takes a demand-driven planning approach.The operational-level decision problems facilitate the organization of learning activities on subjects (e.g., Mathematics, English) to serve the personalized learning demands that are directly provided by learners. It must be noted that the problems O.2-O.4 are also relevant for one-size-fits-all systems. However, for PL systems these decisions cannot be fixed in the beginning of an academic year, instead they have to be remade depending on the changes in learning demands throughout the year. Moreover, the decision problems O.1 and O.5 are not relevant at all for one-size-fits-all systems.