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Block 3: Week 11

Week of Visitations

This week was a rather challenging one because I spent most of it in the hospital due to ill health, however this did not stop me from capturing data for my blog activity. As I lay on my hospital bed and wondered what data to capture to be able to still add value to my reading I noticed the visits of different medical practitioners in the ward to look after patients’ needs. There it was. I decided to track every time a medical personnel came into the ward, the duration of their visit and the time of day they visited. Since I was weak and asleep most of the time I told my kid sister who was mostly with me to do the tracking.

It was interesting because my initial thought was to record the visits which were made to me but I realized she recorded every visit whether it was directed to me or not as long as medical personnel walked in, she recorded it. Initially, I was upset but after critical consideration, I realized this will be a good opportunity to expound and visualize something that will buttress Ozga’s concept of ‘thin description’  about data-based decision making. The image below represents the visits to my ward by medical personnel over a five days period at the hospital.

Findings from my visualization

  • Across all the days, over 80% of the visits made were under 30mins 
  • Over the week, I recorded an average of ten visits per day by medical practitioners 
  • Visits that lasted more than 30mins were only made during the day and night visits were always relatively short
  • Monday and Wednesday recorded the highest visits with Sunday accounting  for the least number of visits 

Relationship with Governance

As discussed by Jenny Ozga, one of the limitations of data based decision making over expert knowledge is the inability for data to tell the whole story especially when certain indicators have been excluded from the visualization.  This is what she calls a thin quantitative description of data. Where the data is stripped of its qualitative contextual complexities and replaced with accessible and available data which is used as a base for policy making. 

 One might look at my hospital dataviz and infer that because I had several visits  a day by medical practitioners, it means I had the best care or even go a step further to recommend the hospital for good performance. Except that was not the case, most visits were not for any medical purposes and sometimes it took hours for medical personnel to show up when patients are in distress.

Envisioning data in its completeness including all its complexities is a fix for such problematic description of data. For this to be possible proper infrastructure needs to be put in place from the indicators, the data collection process through to the analysis and interpretation of data. This can help curb some of the issues arising from the use of data for policymaking in education and other areas of governance.

Reference

Ozga, J., 2015. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1), pp.69-81.

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Block 3: Week 10

Week of Engagement

One of the major elements of governing with data is the ability of institutions to use tracked data to improve institutional management. In order to further harness this reality, this week I decided to track my engagements with learners in a course I facilitate. The rise and prevalence of big data have given governments and other data management organizations the ability to continuously track and monitor individuals and use such data for governance (Williamson, 2017). Hence, I wanted to identify patterns from my visualizations that can be used to inform some elements of policy around student engagements with faculty in the educational setting.

The visualization below represents and captures the different platforms on which I engaged with the students, I also tracked the different issues that prompted the engagements, the time of day it happened, and whether or not my response was immediate or delayed.

Findings from Visualization

Upon observation, I realized that over 60% of my engagements with students were via email and google hangout. The question I had in mind was whether or not this outcome was based on the efficiency of the platforms or because it was our organizational culture and engagement structure. Just as argued by Ozga, sometimes the use of data for policymaking is not sufficient when compared to expert knowledge because there could be several hidden information that the data might not communicate and thus decisions might be taken that further harm students and institutions. (Ozga,2015). Although using only expert knowledge to create policy has its limitation, the use of a combined system of data as well as expert knowledge can bring about a shift in the accuracy and efficiency of policy created by governments.

From my visualization, I also realized that engagements during the morning and afternoon hours of the day tend to get an immediate response as opposed to requests made during the night hours. Such data in the hand of an administration can help inform policy around faculty students communications timings and efficient platforms. I might not have captured enough information from which to build a policy but high volume of data collection in this regard can go a long way in helping institutions observe patterns that can help to build systems that will boost performativity and enhance governance (Williamson, 2017).

References

Ozga, J., 2015. Trust in numbers? Digital Education Governance and the inspection process. European Educational Research Journal, 15(1), pp.69-81.

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

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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.

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.

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End of block 2: Teaching with Data

The transition from learning with data to teaching with data was not smooth for me. Having taught in higher education for a couple of years I believed it would be like a ride in the park but it was far from my expectations.

This block exposed me to a variety of concepts and made me question most elements of modern teaching from a more critical perspective. The three visualizations produced in this block were not particularly designed from the beginning to answer any particular questions but as I reflect at the end of the block I realized I focused on a number of concepts: knowledge sharing and how it impacts teaching, datafication of learning platforms and how they impact teaching and finally the issues of data privacy and how it affects student data management.

In my first visualization, I focused on the concept of knowledge sharing and how that data in the hand of a teacher can influence or impact her teaching. I realized from my visualization that there are elements of teaching which are otherwise overlooked that could greatly impact teaching and learning. In the visualization, I decided to track student engagement not just focusing on performance and engagements on digital platforms but also explore the social and interpersonal aspects of learning and teaching (Williamson et al. 2020). This social and interpersonal relationship-based aspect of student learning can provoke great reforms in education if properly explored and harnessed. This also showed the limitation of most digital technologies for education as they focus on only quantified elements for learning and neglect an aspect that can give more insight about students and foster more efficient personalized learning (Tsai et al. 2019).

In the second week, I decided to explore further the concept of platformization because it has become a dominant element of teaching and learning. This is especially because of the explosive growth of online education over the past years (Van Dijck, Poell and De Waal, 2018). The growing debate on teachers’ willingness to use digital tools and technologies has not been a hindrance for hundreds of institutions requiring their faculty to make use of data dashboards provided by these tools to inform their teaching despite the fact that most teachers and institutions have still not been able to answer the question of how data-driven dashboards improve the teaching or learning process  (Brown, 2020). 

Therefore,  for teachers to properly utilize and benefit from teaching dashboards, there must be an awareness and understanding of the data being processed in order to properly interpret the results being communicated. This will put teachers in a better place to effectively and efficiently use these technological tools (Brown, 2020). In addition to the technical knowledge provided to boost faculty literacy development, a different literacy module has to be adapted to manage and preserve data because even social media platforms are now venturing into the educational space and leveraging student data for profit generation (Van Dijck, Poell and De Waal, 2018). 

The invasion of social media and other digital platforms in the educational space with the aim of using student data for monetary gains led me to produce my final visualization for the block. Learning institutions are to be conscious that they house a lot of sensitive data about learners that if not properly managed can be used to negatively impact individuals. The use of digital platforms is meant to improve the learning system and not have teachers focusing on dashboards instead of classroom activities that might give more insight into student learning (Van Dijck, Poell and De Waal, 2018)

References 

Brown, M., 2020. Seeing students at scale: how faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), pp.384-400.

Raffaghelli, J. and Stewart, B., 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature. Teaching in Higher Education, 25(4), pp.435-455.

Sarikakis, K. and Winter, L., 2017. Social Media Users’ Legal Consciousness About Privacy. Social Media + Society, 3(1), p.205630511769532.

Tsai, Y., Perrotta, C. and Gašević, D., 2019. Empowering learners with personalised learning approaches? Agency, equity and transparency in the context of learning analytics. Assessment & Evaluation in Higher Education, 45(4), pp.554-567.

Van Dijck, J., Poell, T. and De Waal, M., 2018. The Platform Society. Oxford: Oxford University Press USA – OSO.

Williamson, B., Bayne, S. and Shay, S., 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4), pp.351-365.

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Block 2: Week 8 Visualization

My week of secrets.

One of the most interesting concepts I encountered during my readings in this block is the idea of “Liquid Surveillance”. Normalizing the datafication of the different elements of our lives thereby generating a constant flow of data about individuals (Williamson et al. 2020). I am conscious that most of this data collection is enforced by digital technologies, however, I wanted to explore the intentionality of data sharing from an individual perspective. 

This week I decided to track all the times I intentionally withheld information in different spaces of my life namely: work, study, personal, and social life. This was important to track because of the increasing concerns of data privacy and how it affects the willingness to share personal data. The image below shows my visualization and some observations I picked from it.

Findings from my visualization

The first thing I realized was how unwilling I was to share personal data irrespective of the engagement space. It was interesting that whenever providing personal data was optional I quickly took the opportunity to skip especially when using digital technologies. It didn’t matter whether I was completing a recommendation for a student or I trying to create a profile for a social media platform (Raffaghelli and Stewart, 2020).

Another observation from my activity this week was that I was less trusting with sharing data online than in face-to-face interaction.  However, given how datafied all our systems of learning and teaching have become, it is almost impossible to avoid sharing information since most times we are not given the option to do otherwise (Sarikakis and Winter, 2017).  Also, this idea that the data recorded is stored and can be used in the future to the benefit or the detriment of the individual raises an even bigger concern about data privacy beyond just the collection and use of data but the ethical implications of data sharing and management (Raffaghelli and Stewart, 2020).

Reflection on this data in teaching 

Teachers should understand that given the option, students are not readily open to sharing personal information because of the raising awareness of data privacy and its effects on the students in the future.

Teachers are mostly in possession of individual student data collected either through the admission platforms or just learning activity. It is important to understand how improper management of said data can have a detrimental effect on students in the future. Hence the pressing need for teachers to undergo new forms of data literacy beyond the collection and interpretation of data (Raffaghelli and Stewart, 2020)

Finally, teachers should understand that since individuals are not willingly open to sharing personal data, dashboards and other learning management tools might create analyses that are false due to manipulated data from learners.

References

Raffaghelli, J. and Stewart, B., 2020. Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: a systematic review of the literature. Teaching in Higher Education, 25(4), pp.435-455.

Sarikakis, K. and Winter, L., 2017. Social Media Users’ Legal Consciousness About Privacy. Social Media + Society, 3(1), p.205630511769532.

Williamson, B., Bayne, S. and Shay, S., 2020. The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4), pp.351-365.