Nursing Students’ Evaluation of the Effectiveness of Online Collaborative Learning

Abstract

Aims: To investigate nursing students’ evaluation of the impact of collaborative group work in a web-based learning environment. Study Design: Correlational Design. Place and Duration of Study: The study took place in an online, elective “Multimedia Design for Instruction” course, which is offered bi-annually, in a regional campus of Taiwanese Nursing Institute during fall and spring semesters from 2009~2011 academic years. Methodology: One hundred and ninety seven students participated in the study, including 14 males and 183females, who aged between 18 and 32. The participants varied in their demographic profiles, for instance, age, the length of their professional work experience, experience of using computers for learning, and etc. A self-report survey was used to collect data. The data collected were analyzed using summary descriptive statistics, correlations, and a multiple regression analysis. Results: The results revealed that the variables including gender, complexity of tasks, strong group partners, class preparation, ability to use required media, and interest in class topics, yielding R2 =.37, R2adj = .33, F (8, 188) = 9.83, P< .001, had significant impact on participants’ perceptions of online collaborative learning. This model explained 33% of the variance in participants’ perceptions of the effectiveness of collaborative learning. Conclusion: Overall, students rated the effectiveness of online collaborative learning as “high.” The impact of students’ computer skills and their ability to use the required media needs additional examination. It is also suggested that the need for prerequisite skills and competence in using the course management software prior to taking an online collaborative class should be studied.The results of the multiple regression indicated that the independent variables examined in this study, although having an impact on students’ perceptions of the effectiveness of collaborative online learning, suggest that other models might identify variables that have a greater impact.