Ubiquitous learning laboratory for pediatric nursing: A cultural algorithm approach.

摘要

Quality Medical Care is at the focus of all health care service providers. Each facility maintains a standard level of care that promises not only a precise diagnosis, but also the correct course of treatment. In part, this is due to the educational training and professional experience of Nurses. For high-risk patients such as children, the level of expertise of a Pediatric Nurse is even more critical in order to guarantee patient safety.;Pediatric Nurses do not necessarily have the same level of expertise in critical thinking and overall patient care, however. This can be attributed to variables in teaching institutions, training environments, and even demographic backgrounds. Moreover, the lack of a teaching paradigm that captures the attention of today's technology-savvy student could also be a contributing factor.;In this thesis, a learning framework is proposed that serves as an extension to accepted nursing curriculum. This framework is a 2d serious Educational Puzzle game based on the classic board game Clue. Clue involves solving a murder mystery utilizing character interaction and discovery / observation of objects within a given room. I-CARE is similar except this game involves determining a Medical Diagnoses utilizing patient interaction (i.e. character dialogue) and accessing various rooms (i.e. Class Room, Equipment, Patient, Medical Supplies, etc.) in order to deliver medical care.;The I-CARE application encapsulates a virtual world that makes use of all perceptual modes (i.e. visual, auditory, and haptic), just like a real Children's Hospital. The framework is developed for a mobile platform using XNA 4.0 technology. It offers a portable world where nurses can develop critical thinking skills and practice delivering quality medical care. With game play, they accumulate a progression of tasks required to deliver an Albuterol treatment to a pediatrics patient. These may not be the most efficient progression of tasks, however.;Cultural Algorithms is an agent-based evolutionary method used to computationally determine the most efficient progression of tasks to deliver an Albuterol treatment. It begins by capturing all of the tasks available in the I-CARE virtual world. Next, it describes the rules of how those tasks can come together in terms of pre- and post-conditions of task usage. Finally, these rules are weighted in a manner that allows for task inclusion along with its relative position within the task progression.;Cultural Algorithms is shown to be more than an experimental framework. It is also shown to be a learning mechanism as well. Through the execution of 10 runs at 1000 generations each, an analysis of the best learning example is performed. This analysis breaks down the progression of fitness scores over each generation to identify segments of learning. The idea is to not only determine an optimal solution to the stated problem, but to also identify how pediatric nurses learn themselves.