USING LEARNING ANALYTICS TO ASSESS NURSING STUDENT ENGAGEMENT AND ACADEMIC OUTCOMES

In 2013, the School of Nursing and Midwifery at Flinders University, Adelaide, Australia, introduced a new blended learning curriculum (BLC) which provides a mixed learning environment with the intent to enhance student freedom and independence in learning. Student engagement in a BLC encourages spontaneous and iterative engagement with learning activities both during and between traditional classroom activities and has been considered a ‘rich and effective’ student learning experience. Exploration of variations in student engagement with technology based learning materials has reported a correlation between deep engagement with on-line materials for the development of knowledge and higher academic achievement. Conversely, Ellis and colleagues found that students who superficially engaged with on-line activities to simply fulfil course requirements did poorer in academic outcome, a finding that concurred with other studies of student engagement and academic success. Learning analytics, an evolving area in tertiary education, is where student on-line activity data is collected. This data may allow greater understanding of student learning processes and help identify students at risk of failing early so that intervention strategies to improve student engagement may be implemented. Learning analytics may also provide students with insight into their own learning styles, their areas of strengthen and areas requiring improvement.

Methods:
Two second year topics were offered in two availabilities in semester one 2015. Visual progress bars (VPB) were introduced into the second availability of both topics. The VPBs provided students with colourful visual boxes that changed colour once an activity had been accessed and allowed them and their tutors to monitor their engagement with the topic materials and requirements. Learning analytics informed the progress bars which is the basis of the data collection.

Statistical analysis will be applied to the data. A mixed effect regression model will be applied in STATA (version 13.0) to fit models of student engagement and academic outcome. As the outcome measures will be assessed every week of topic delivery between two groups (with and without the VPB), the model will compare the significant differences of student engagement and academic outcome over time and between the two groups. The within-subject factor will be the outcome of interest (i.e. student engagement and learning outcome), and the between-subject factor will be the group (with/without visual progress bar) with a random intercept for individual students to account for repeated measurements. As the student engagement and academic outcome varies with a number of students’ characteristics, the models will be adjusted by potential confounders. The two sided tests will be performed for all analysis and the level of significance will set at p<0.05. Academic outcomes for the students will be accessed and compared with topic engagement.
Evaluation of a weekly email notification system to tutors that were set up specifically for this project will be evaluated. Tutors involved in the teaching of these topics will be sent an on-line survey using survey software after the completion of the topic that they can voluntarily respond to.

Results:
Data analysis is currently being conducted and the results will be presented. Early results indicate that the VPB improved student engagement with on-line learning activities.