Browsing by Author "Nsair, Sumaya"

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    Constructive alignment in a graduate-level project management course: an innovative framework using large language models
    (2024-04-17) Pereira, Estacio; Nsair, Sumaya; Pereira, Leticia R.; Grant, Kimberley
    Abstract Constructive alignment is a learning design approach that emphasizes the direct alignment of the intended learning outcomes, instructional strategies, learning activities, and assessment methods to ensure students are engaged in a meaningful learning experience. This pedagogical approach provides clarity and coherence, aiding students in understanding the connection of their learning activities and assessments with the overall course objectives. This paper explores the use of constructive alignment principles in designing a graduate-level Introduction to Project Management course by leveraging Large Language Models (LLMs), specifically ChatGPT. We introduce an innovative framework that embodies an iterative process to define the course learning outcomes, learning activities and assessments, and lecture content. We show that the implemented framework in ChatGPT was adept at autonomously establishing the course's learning outcomes, delineating assessments with their respective weights, mapping learning outcomes to each assessment method, and formulating a plan for learning activities and the course's schedule. While the framework can significantly reduce the time instructors spend on initial course planning, the results demonstrate that ChatGPT often lacks the specificity and contextual awareness necessary for effective implementation in diverse classroom settings. Therefore, the role of the instructor remains crucial in customizing and finalizing the course structure. The implications of this research are vast, providing insights for educators and curriculum designers looking to infuse LLMs systems into course development without compromising effective pedagogical practices.
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    Dynamic Transit Passenger Origin/Destination Estimation: A Bilevel Variational Inequality Approach
    (2020-01-21) Nsair, Sumaya; Kattan, Lina; Liang, Steve H. L.; Kattan, Lina; Dann, Markus R.; Waters, Nigel M.; Liang, Steve H. L.
    Transit origin destination (OD) trip matrices are essential inputs for most problems regarding the planning, operation, and management of public transit systems. Traditionally, OD matrices were obtained statically from passenger surveys. However, due to the need for continuous updates, surveying a representative sample, and the high cost of conducting these surveys, advanced methods estimate transit OD using sensor collected data such as automatic passenger counts (APC). The objective of this research is to formulate a dynamic transit origin estimation (DTOD) estimation model that is transferrable and applicable to scenarios where the available transit data is APC. The proposed methodology is a bi-level optimization model. In this model, each optimization is defined as a distinct level, and each level has its own objectives and constraints. The lower level (follower) seeks to optimize its outcomes, which are then used by the upper level (leader) to optimize its own outcomes. In the bi-level model proposed, the lower level is a dynamic transit assignment model that simultaneously determines the dynamic average travel costs and optimal route choices of passengers in congested transit networks (i.e., estimated passenger flows). The upper level sums passenger route choices from the lower level to obtain transit OD, and minimizes the sum of error measurements between the obtained time-dependent OD matrices and dynamic real passenger counts (APC counts). As a result of considering asymmetric link cost interactions (i.e., the cost of traversing a link in the network is both a function of the flow on the link itself and on surrounding links), the transit assignment is formulated as a variational inequality. The upper level, in contrast, is formulated as a generalized least square estimation. To evaluate the performance of the proposed DTOD estimation model, numerical examples are conducted using MATLAB, in which the model’s solution algorithm is coded. The model is tested on a small theoretical network and a real transit network in Calgary, Alberta, Canada. Sensitivity analyses of the bi-level model to different weighting schemes, link cost function parameters, and congestion levels are performed in which the model converges to unique solutions in minimal times and within acceptable ranges of error.