Dr Julie F. Hinkle PhD, RN, CNE
University of North Carolina at Wilmington
College of Health and Human Services
School of Nursing
McNeil Hall rm 2041
Dr. Julie F. Hinkle is a nurse educator with over 20 years of teaching experience. She earned her BSN from the University of Pittsburgh, and her MSN and PhD from the University of Michigan at Ann Arbor. She is an expert in pathophysiology with research focusing on innovative technologies and interventions such as simulation, online interventions, and applications. Currently, she is an Assistant Professor at the University of North Carolina at Wilmington (UNCW) School of Nursing teaching in the RN to BSN program, the graduate Nurse Educator track, and the Family Nurse Practitioner program.
Dr Hinkle has been examining the use of an adaptive learning platform called Realizeit in nursing education. She has previously reported on its use in a flipped classroom with a pre-licensure pathophysiology course. Parameters examined include user experiences with adaptive learning, student engagement, and learning outcomes. These studies are ongoing in the context of a variety of modalities including face to face, online, and mixed mode as well as to a variety of levels of students including traditional undergraduate, RN to BSN, and to graduate nursing students. She is currently examining the impact of adaptive learning at UNCW on student outcomes including student engagement and other learner outcomes in an online nurse educator advanced pathophysiology course.
Adaptive learning platforms use algorithms to adjust and deliver content to students based on their prior knowledge and through a continuous assessment that occurs as the student progresses through the content. Our online graduate pathophysiology course content was redesigned using an adaptive learning platform. Additionally, through adaptive learning, students are presented with evolving cases that integrate multisystem concepts and clinical decision making. Each student is presented with a unique case using the adaptive learning system; as students repeat each case, unique variables are presented on the same concepts. This allows students to see new cases with new critical information on the same concepts reinforcing learning and avoiding memorization that occurs with static cases. Continuous updates of student measures are available to both the student and instructor, such as an estimate of knowledge acquired and what content is left to be learned, allowing students and faculty to accurately assess student progress.
This online advance pathophysiology course for nurse educators was designed and delivered using an adaptive learning platform called Realizeit. Major related concepts centered around body systems were divided into modules and related concepts within systems then were then divided into nodes. Each node contains lessons in text, images and videos. Each module contains pre-lesson assessment questions and each node contains post-lesson assessment questions. The pre-lesson questions enable Realizeit to provide a suggested path through the lessons in each node, which can include skipping lessons that are already mastered or suggesting an order of nodes to review. The post-lesson assessment questions enable Realizeit to recommend next steps, including remediation of content, alternative lesson suggestions within a node, review of previously skipped content within a module or within a related module in the course, or which new module to review next. Four case studies were constructed around multisystem concepts to help students make connections between how body systems interact and how these multisystem concepts impact clinical decision making.
Faculty have long delivered course content in ways designed to meet student needs. Technology advances enable more and more sophisticated methods in personalization of content delivery that can be leveraged to overcome challenges in identifying student learning needs in online education. Adaptive learning platforms such as Realizeit, the one used for this course, personalize content delivery based on individual differences. This enables educators to help a more diverse student population succeed by enabling remediation and/or acceleration through content based on individual needs. Faculty can see which students may be at risk based on data provided by the system. All students enrolled in this course in the fall semester were successful this course, reported overall satisfaction with the adaptive content delivery and commented that the adaptive platform helped them stay on track even during a 4-week disruption in their education due to Hurricane Florence.
Allowing students to accelerate or decelerate through each content area in a course based on their needs can lead to a transformation in educational environments, freeing students from the constraints of a traditional model of week or 2-week long modules delivered in a strict order and time sequence. In this course, students generally followed the suggested order of content delivery. Several students flexed the suggested due dates for each module based on their needs post Hurricane Florence with some working ahead and some needing to decelerate for a week or 2. Adaptive learning technologies can be used across courses as well at minimum to create opportunities to connect related content in new courses to previous courses allowing directed remediation by students as needed beyond the confines of the current course content. This would remove the structured repetition that most nursing courses have built in, freeing up instruction time for new concepts but providing the scaffolding support and remediation of old concepts to students that need this but not imposing this repetition to students who have already learned the old concepts.