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Learner and instructional predictors of student satisfaction in a graduate nursing program taught via interactive video teleconferencing and World Wide Web/Internet

Posted on:1999-10-27Degree:Ed.DType:Dissertation
University:University of San FranciscoCandidate:DeBourgh, Gregory AFull Text:PDF
GTID:1467390014968402Subject:Health Sciences
The changing healthcare delivery system creates a demand for registered nurses with leadership, management, and expert critical thinking skills. To function in increasingly complex healthcare settings, nurses are returning to school for graduate education. Distance education features convenience and flexibility in scheduling, geographic proximity, and learner control. This study investigates graduate nursing students' satisfaction with a course taught via interactive video teleconferencing (IVT) and World Wide Web/Internet (WWW/INT).;A correlational research design examined the relationships among five learner attributes and three instructional variables and student satisfaction. Regression analyses identified learner attributes and instructional variables predictive of student satisfaction.;Forty-three graduate-nurse students were surveyed using a 59-item Student Satisfaction Survey (SSS) modified from a tool designed by Biner. Learner attribute predictors included: (a) previous technology courses, (b) technology competence, (c) between-class technology usage, (d) age, and (e) remote-site group size. Instructional predictors were clustered in three dimensions: instructor/instruction, technology, and course management. Student satisfaction was a composite of overall satisfaction with the course and comparison with conventional classroom courses.;Instructor/instruction explained 21% of the variance in course satisfaction scores (p ;This study provides essential information to faculty responsible for the design and delivery of effective instruction and to students pursuing flexible and convenient options for advanced education.
Keywords/Search Tags:Student satisfaction, Learner, Instructional, Predictors, Graduate
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