Note to Marker: I was unable to get ggplot running on my cpu and ran out of time to do it at uni, sorry!! :(


Stakeholders – University Coordinators and UOS planners

Executive Summary The investigation into part time and fulltime Sydney University students has identified significant variation in the two groups use of Canvas and academic performance. It is recommended that Unit of study coordinators establish independent attendance standards for part time students


IDA This data set was sourced from the Sydney University Institutional Analytics and Data Science, overseen by Joshua Lee. The ethics of this research has been approved by Prof. Pip Pattison, DVC of Education. The data is limited to an extent for the research of part-time and fulltime students, as it lacks sufficient data regarding extracurricular activities, such as work and sport, which may effect the variables being investigated. The data set is also heavily disproportionate in size, with far more fulltime students investigated. Despite this, the data helps in identifying general disparities in academic performance and engagement between the two groups.


 Average.grade <- c(55.7, 66.2)

Average.Canvas <- c(85.5, 97)

Age.Full <- c(18.8, 72.5, 7.0, 1.8)

Age.Part <- c(5.1, 44.6, 27.8, 22.2)
barplot(Age.Full,
        main =  "Fulltime Student Age Demographics",
        xlab = "Age Bracket",
        names.arg = c("18-19", "19-21", "22-25", "25+"),
        ylab = "Percentage of Fulltime Students",
 sub = "Fig 2 - Age Distribution of Fulltime Students")

barplot(Age.Part,
        main =  "Part-time Student Age Demographics",
        xlab = "Age Bracket",
        names.arg = c("18-19", "19-21", "22-25", "25+"),
        ylab = "Percentage of Part-time Students",
 sub = "Fig 2 - Age Distribution of Part-time Students")

While the gender distribution between the two groups was relatively the same, the distribution of age groups represented in the two groups varied significantly (fig 1). The higher rate of part time study in older students is likely due to a larger proportion of students over the age of 21 living out of home, and therefore being required to work fulltime or part time to support themselves. This requirement to work alongside schooling is known to have a more adverse effect on educational choices and behaviour, notably study engagement and the decisions to continue studying (Baert 2017). Canvas fig

barplot(Average.Canvas,
        main = "Average Canvas Engagment Over Semester",
        xlab = "Enrolment Type",
        names.arg = c("Part Time", "Full Time"),
        ylab = "Percentage of Weeks Canvas Was Accesed",
        ylim=c(0,100),
        sub = "Fig 2 - percentage of weeks students where active on canvas during the semester")

The significant reduction in weekly canvas activity seen in part time students v full time students (fig 2) reflects a clear lack of engagement from part-time students discussed by Baert. Based on this data it is suggested that employees of part-time students working part-time or full-time should be encouraged to give students a small 15-minute break a day specially for online work. This could provide up to 1.5 hours a week for university work during normal working hours, with the aim of increasing part-time student engagement with online software.

barplot(Average.grade,
        main = "Average Student Grades",
        xlab = "Enrolment Type",
        names.arg = c("Part Time", "Full Time"),
        ylab = "Mark",
        ylim=c(0,100),
        sub = "Fig 3 - Average grades for both enrolments")

The dataset also indicated a 7.5% reduction in average WAM in part time students when compared to their full-time counterparts (fig3). This may be in part due to the difficulties placed on part-time students with attendance to field trips, presentations and other events outside a UOS regular timetable. Some part time students may be suffering academically as they are missing out on these opportunities due work and other extracurricular commitments (Lingard 2007). It is recommended that units of study including these activities be flagged as incompatible with fulltime work as so part-time students can more easily pick UOS that are more compatible with their schedule.

References

Baert, S. (2017). Does Student Work Really Affect Educational Outcomes? A Review of the Literature. IZA Institute of Labor Economics , (11023), 2–8. Retrieved from http://ftp.iza.org/dp11023.pdf

Lingard, H. (2007). Conflict Between Paid Work and Study: Does it Impact upon Students’ Burnout and Satisfaction with University Life? Journal for Education in the Built Environment, 2(1), 90–109. doi: 10.11120/jebe.2007.02010090