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Block 3: Week 9.

Week of maintaining balance

One of the most basic struggles of any student enrolled in higher education is maintaining a balance between work, recreation/fun, and sleep. There was a saying in college that one can never have all three and that we always have to sacrifice one to ensure productivity in the other two. This week I decided to tract these three activities of a regular student and see if a certain pattern can be observed from it and used to create a form of guidance or policy for students enrolling in university programs.

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The visualization below captures the three main activities of sleep, recreation, and work across a four days period tracked between 6 am to 12 midnight. I also tracked the time of day these activities were performed, over what duration, and whether or not I was satisfied with my productivity after the activity. 

Legend

Reflection Points.

From my visualization, I realized that most of my day was spent on academic work and the remaining fraction was split between sleep and recreation.

I am more likely to be engaged in a recreation activity in the evening and afternoon hours of the day than I am in the morning hours.

I was mostly drawn to allocating more hours to work than any other activity which is natural but most of the time I found that the more hours committed to an activity the less productive and satisfied I was with the activity be it sleep recreation or schoolwork.

Activities done at any time of the day within 0 to 4 hours durations were more likely to produce satisfied productivity which was an indication of my average concentration span.

Its Effects on Governance 

There is an increased need to track student activities by institutions to understand and create policies that boost the performance of students and thereby boost institutional credibility (Williamson, 2017). Government is as interested in the performativity of education institutions as the institutions are interested in the performativity of the students. Hence using advanced data tracking and analysis process to understand the ideal work, sleep, and recreation balance for students will help build an educational policy that will develop the performativity of students, institutions, and the government in effect.

References 

Williamson, B., 2017. Big data in education. London: SAGE Publications.

One reply on “Block 3: Week 9.”

I am sure that these kinds of data would be of interest to institutions trying to ensure student well-being and a good ‘student experience’. It is good that you have identified how students’ personal lives – and not just traces of their ‘academic’ activity on learning platforms – could be tracked for the purposes of developing institutional policies and governing interventions. Indeed, there have been some proposed projects to track signals of student mental health from social media as a way of informing interventions: https://www.officeforstudents.org.uk/news-blog-and-events/press-and-media/innovation-partnership-and-data-can-help-improve-student-mental-health-in-new-14m-drive/. There are questions here, however, about the level of intrusion into and surveillance of students’ personal lives, even if the outcomes seem desirable.

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