Beverly Gish
Mount Carmel College of Nursing, USA
Posters & Accepted Abstracts: Adv Practice Nurs
Student engagement has been defined as ??the level of interest demonstrated by students, how they interact with each other in the course, and their motivation to learn about the topics? (Gray & DiLoreto, 2016, p. 5). Online learning environments are challenged to develop strategies that will engage students, to improve student retention and maximize student achievement of course objectives. Therefore, different online learning strategies require assessment to determine their effectiveness at enhancing student engagement. Hilty, Gill-Rocha, Parkinson, Blackford, & Cook (2018) evaluated the psychometric properties of the Burch et al., (2014) 63-item Student Engagement Survey (SES). Exploratory principal axis factor analysis (EPAFA) was used to determine the number of underlying factors. Using the screen test to determine the number of factors, the EPAFA with an obliging rotation suggested four factors. The screen test indicated four factors (eigenvalues: 17.176, 3.807, 2.942, and 2.151) accounting for 63.6% of the variance. Forty-one (41) of the 63 items loaded on one of the physical engagements, cognitive engagement, deep learning engagement, and engagement skills factors. Coefficient alpha reliability estimates were .921 (Physical), .961 (Cognitive), .905 (Deep Learning), and .937 (Engagement Skills). Masters level graduate nursing students completed the 41 questions designed to measure student engagement. Advanced Pathophysiology students completed an educational intervention based on a passive learning and active learning modules for this online course. Learning activities, such as textbook readings, videos and quizzes, were the same for all modules. The difference was in the discussion of the case studies. Using SPSS 25, the dependent t-test analyzed the passive and active learning approaches by comparing student responses to the physical, cognitive, deep learning, and engagement skill factors. First, there was no statistical (p=.204) difference between the passive and active learning on the physical engagement factor/scale. Second, there was a statistical (p=.019) difference on the cognitive engagement factor/scale. Third, there was a statistical (p=.002) difference on the deep learning engagement factor/scale. Fourth, there was a statistical (p=.022) difference on the engagement skills factor/scale. These findings demonstrated the students were significantly more engaged while completing the active learning modules. Using SPSS 25, the dependent t-test analyzed the passive and active learning approaches by comparing student responses to the physical, cognitive, deep learning, and engagement skill factors. First, there was no statistical (p=.204) difference between the passive and active learning on the physical engagement factor/scale. Second, there was a statistical (p=.019) difference on the cognitive engagement factor/scale. Third, there was a statistical (p=.002) difference on the deep learning engagement factor/ scale. Fourth, there was a statistical (p=.022) difference on the engagement skills factor/scale. These findings demonstrated the students were significantly more engaged while completing the active learning modules.
Beverly Gish, Assistant Professor, has been a nurse for 40 years and have diverse career experiences in direct patient care, staff education, nursing management, and finally nursing education. He started his career on a busy gynecology/oncology unit, moving to critical care, then the emergency department, and finally a specialty in neurosurgery. Currently, He have returned to direct patient care and practice as a certified nurse practitioner (CNP) in a primary care (community clinic) setting. Professionally, He is having fifteen years of teaching nursing.
E-mail: bgish@mccn.edu
Journal of Advanced Practices in Nursing received 410 citations as per Google Scholar report
Spanish 
Chinese 
Russian 
German 
French 
Japanese 
Portuguese 
Hindi 