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Final Reflection

The nine weeks data visualization journey has been an insightful and exciting opportunity to explore different activities made possible through the use of data. The data visualization activities open me up to huge learning opportunities from the process of thinking about what data to measure down to the analysis and representation of what has been measured. I have done a lot of work with data prior to this course but my greatest inspiration came from the use of hand-drawn visualizations to represent data in a creative and less conventional way. It gave me the opportunity to explore and present the complexities of data in a more friendly and easy-to-understand manner.

My first impression after being introduced to the data visualization task was that it will be a ride in the park. However, after the first few weeks, I quickly realized being placed at the center of the data analysis process from determining what data to capture to how the data will be represented whilst maintaining a high level of privacy was both critical and challenging (Bulger, 2016). The blog visualization activities gave me insight into the use of data under the three themes of learning, teaching, and governing which I found to be quite interrelated after several weeks of visualization. In this final reflection, I will discuss the key takeaways from each block and explain how the course impacted my learning.

Learning with Data 

Learning with data block was the first opportunity to self-track my learning in order to make an informed decision. The process of observing my learning with the use of self-tracked data made me feel empowered and trusted to understand insights about my learning and was not just an emotional irrational subject with actions and behaviors readable and modifiable by machines and algorithmic processes (Knox et al. 2019). One of the things that stood out for me was the datafication of higher education and how institutions try to explain learners through visualizations and dashboards (Williamson et al. 2020). The use of platforms and digital technologies continue to make strides in education and their relevance is seen in multiple ways. However, the continuous use of these technologies has equally reduced learners to data points on a dashboard, limited teacher’s pedagogical practices as well as reduce the complex nature of learners and learning to set of quantities in order to enforce performance measurement (Enyon, 2015).

In spite of the benefits gained from the use of digital technologies in education, it is imperative to know that data in itself is not objective, truthful, and neutral as is often assumed (Kitchin, 2014) ut rather it is partial, selective, and representative. Thus, in order to use it in a sound concise manner, data infrastructure needs to be in place that governs the design of digital technologies for education,  as well as the stakeholders involved and their individual roles clearly spelled out. This way even if machines are tasked with learning proper attention will be given to how learning itself is theorized and encoded (Knox et al. 2020)

Teaching with Data.

The teaching block opened up some interesting conversations in my mind about the issue of control when it comes to data-based technologies and the use of data for teaching. How much control does the teacher have over what data is collected about the learners? What does the teacher do if the learning platforms provide data that might seem irrelevant to the teacher or does not align with his pedagogical practices? These and many others are some of the challenges that the teacher faces when teaching with data. 

In my visualization for week 6, I noticed there are elements of teaching which are otherwise overlooked that could greatly impact teaching and learning. Hence I tracked student engagement beyond performance and engagements on digital platforms but on 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.  Through this lens, I realized the limitation of most digital technologies for education as they focus on only quantified elements of learning and ignore aspects that can foster more efficient personalized learning (Tsai et al. 2019).  

As much as we encourage the use of digital technologies and data dashboards for teaching, it is imperative that teachers understand how to manage and use the tools to interpret results presented and thereby benefiting from the platforms (Brown, 2020). Van Dijck, argues further that 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). They further alluded that different platforms encroach into the educational domain bringing their own values which may not be aligned with educational values.  Since most platforms are designed to enrich the platform owners and not necessarily to improve education, they are gradually changing education from a public good meant to develop society to a more business profit gain concept actualized at the expense of the individual consumer. (Van Dijck, 2018).

Governing with Data

Crowning the journey with the use of data for policymaking which affects both learners and teachers was just amazing. It showed the interrelatedness of the data across all themes. Key insights from governing with data are the idea that most times data does not tell the entire store and therefore needs to be further explored to avoid giving a thin description to what could have thick qualitative complexity.

 (Ozga, 2016). There are benefits to using data for policymaking because it can enhance fast policy-making and curb the slow-paced political approach (Williamson, 2017). Although data has made it possible for policymakers to rationalize decisions and actions based on numbers, it also should be done with a high sense of accountability knowing that they owe a duty to all stakeholders involved (Anagnostopoulos, 2013).

In conclusion, the data visualization blog activities have been a great avenue for learning about myself and most importantly about the nature and use of data in our daily lives. Through the blog activities, I have come to see that there are always multiple perspectives to data and should not be viewed in one dimension. Also, it has inspired me to embraced the criticality of data and how it can be presented in more fun and creative ways to tell a great story about learning, teaching, and governing.

References

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

Bulger, M. 2016. Personalized Learning: The Conversations We’re Not Having. Data & Society working paper. Available: https://datasociety.net/pubs/ecl/PersonalizedLearning_primer_2016.pdf

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

Fontaine, C. 2016. The Myth of Accountability: How Data (Mis)Use is Reinforcing the Problems of Public Education, Data and Society Working Paper 08.08.2016.

Kitchin, R. 2014. The Data Revolution: Big data, Open Data, Data Infrastructures and their Consequences. London: Sage.

Knox, J., Williamson, B. and Bayne, S., 2020. Machine behaviourism: Future visions of ‘learnification’ and ‘datafication’ across humans and digital technologies. Learning, Media and Technology, 45(1), pp.31-45.

Raffaghelli, J.E. & 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, 435-455, DOI: 10.1080/13562517.2019.1696301

van Dijck, J., Poell, T., & de Waal, M. 2018. Chapter 6: Education, In The Platform Society, Oxford University Press

Williamson, B. 2017. Digital Education Governance: political analytics, performativity and accountability. Chapter 4 in Big Data in Education: The digital future of learning, policy and practice. Sage.

Williamson, B. Bayne, S. 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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Governing with Data

The governing with data block was rather insightful as it opened up the conversation on the use of data for policymaking in different spheres of life. What was especially interesting to me was the fact that although the reflections and visualization were made from everyday activities, they still proved relevant to governance in effect. During this block, I tracked activities on student wellbeing, faculty-student interaction, and finally some hospital staff performance. The introduction of big data and advanced technology tools has had a significant effect on the use of data for policymaking (Williamson, 2017). This phenomenon has thus moved policymaking from the hands of more traditional political actors and open it up to multifaceted global actors both in the public and private domains. The continuous access to data or data-producing facilities leads to the increasing growth of data-based policy solutions. 

As I carried out my first visualization for the week on gathering data about student daily activities in order to build a policy on students’ wellbeing, I realized how much personal data is sometimes required by policy agents in order to achieve their goal. It also begs the question of how reliable is the information being used for policymaking and how much can we trust it if policymaking is now solely dependent on numbers instead of expert knowledge (Ozga. 2016). In addition to the unreliability of the data being collected and the questions raised about the data collection process, access to such data can further infringe on the student’s privacy. There is a high level of intrusion into the student’s personal lives and if data agents are also using the policy solution for profit gains then it is difficult to trust giving out such data even if it was for a desirable outcome such as tracking the daily activities of college students to create a balance and boost performance (Williamson, 2017).

There is an increased need to track faculty-student activities by institutions to understand and create policies that boost the performance of students and thereby boost institutional credibility. In the second week, I surveyed interaction platforms between students and faculty in order to create a soft policy that enhances better communication. Through my analysis of this section, I further explored the changes that data processing software has had in educational governance. The notion that all educational problems were measurable, calculable, and knowable, and thus solvable (Williamson, 2017) has drifted the focus of solving educational problems with concise expert knowledge analysis to a much faster data-based solution which sometimes may not be the best approach to attaining desired results, bearing in mind that data collection and analysis can be sometimes subjective and limiting  (Ozga, 2015).

Furthermore, it is quite obvious the strides that big data and data mining have introduced into educational governance. In the words of Jenny Ozga, data mining has supposedly enhanced efficiency, increase transparency, enable greater competitiveness, and made easy the evaluations of schools and teachers (Ozga, 2015). It has also helped in creating faster policies that would otherwise be locked up in bureaucratic political slow-paced systems (Williamson, 2017). However, in the third week, I explored further what Jenny termed “thin description” the temptation to lean towards the more accessible and available data for policymaking as opposed to including the qualitative contextual complexities of data in order to paint the entire picture (Ozga, 2015).

In conclusion, the use of data in governing has been useful to the different stakeholders, from students, parents to the school administrations. Data continues to be a great compliment to expert knowledge in decision-making if properly handled. Data has made it possible for policymakers to rationalize some decisions based on numbers (Anagnostopoulos, 2013). It should also be understood that using data for decision-making is not an escape of accountability but rather a call to accountability to all stakeholders involved.

References

Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.

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 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.

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Block 2: Week 7

The focus of this week was to record data on different platforms or sites I visited during the week, the purpose of my visit, and the mode of engagement. The data was recorded over a six days period to be able to capture as many entries as were possible.

In order to enhance the readability of the visualization, I was selective of the platforms from which I recorded data, and I also grouped the purpose for visiting the platform in a way that I can be able to capture as many as possible and finally I recorded the mode of engagement as this can inform teaching plans for especially teachers using digital technologies and tools during teaching.

The visualization above shows a summary of what my data recording looks like for the week. Despite the growing debate on the willingness of teachers to adopt digital technologies and tools for teaching, several institutions still largely require faculty to make use of data dashboards provided by these tools to inform their teaching (Brown.2020). 

From the visualization, I realized that my learning engagement was not just fixated on the course learning site (Module) and blogs but was transferred to unconventional learning platforms to further enhance my learning. From this observation, I deduced that one major challenge of data dashboards to teachers is that they are unable to capture every element of student learning thereby giving the teacher an incomplete representation of the entire student learning experience (Williamson et al. 2020). An important element of this visualization in the hand of a teacher in the virtual space is the mode of engagement. Course materials provided in the learning management system can take multiple forms or expressed in different modalities. Therefore record the mode of engagement will support the teacher and designer in choosing the most suitable materials to enhance student learning.

Additionally, most data-driven technologies are programmed to only report certain elements of the learning and this also affects what ends up in the hand of the teacher. Hence, the burden falls on the teacher to determine how much more data is needed to make an informed decision about a student otherwise the lack of data literacy might affect the conclusions about a student immensely. (Williamson et.al 2020). 

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.

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

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 6 Visualization




As we transition into the block of teaching with data, I am yet again thrilled to see how data influences the perspective of the teacher about the learner and how data-driven decision-making impacts Higher Education. To set the ball rolling, I sought to gather data around student engagements and investigate how much the knowledge of such data in the hand of a teacher will influence teaching on a more significant scale (Williamson et al. 2020). 

In order to achieve this, I decided to record all the times I shared my learning during the week. I divided my community into three namely: Learning, professional, and family & friends. Additionally, I recorded the elements of my learning that I shared and the trigger for sharing.  The data was gathered over a period of five days and my findings are as follows.

About 50% of my total learning engagement was focused on sharing about my blog activities and about 70% of the time it was with friends and family. This data in the hands of the teacher can help her know what elements of learning students tend to engage with more and this can inform her planning. However, in some cases, the reason for these numbers might be because am mostly around friends and family when recording data and doing my visualization and they tend to ask me a lot of questions about my drawings and paintings. This is why it is important that teachers are data literate to be able to decipher the reliability and accuracy of the data produced and not take it at phase value. (Williamson et al. 2020)

About 80% of the times I shared my learning on the assigned reading and pedagogy was triggered by either teaching someone something new or learning something new myself. Such data can help teachers know how to classify students in terms of personalized learning. This data can also help in empowering students who might need more support to engage with the content (Tsai et al. 2019).
I was also curious about what triggered the sharing of knowledge and how it relates to my learning. I realized that most of my knowledge sharing was triggered by conversations or my desire to know more about a subject matter. This data in the hand of a teacher can help identify different student personalities and know how to design to shape their learning. 

References 

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.

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.

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Week 5 Data Visualization

Introduction 

This week I decided to test how different events impacted my learning. I knew from the beginning that it was going to be a very eventful week, hence I capitalized on that and tried to do my analysis around this fact. Considering the fact that this was my birthday week, my sibling’s graduation, and my relocation to a new house I knew it was definitely going to have a toll on my learning. Hence I decided to record data to investigate how the different events and my emotions at different times will impact my productivity. Most of the events were towards the end of the week and others spread across the week so it was easy to have valuable data to record all through.

Objectivation

To properly understand what was going on I tracked my academic engagements and what event was distracting me during my engagement in that particular event. I decided to represent the activities as follows:

Downward and upward cone to represent the learning activities 

Sun moon and star represent birthday, relocating, and graduation respectively.

The greater than, less than and, equal to signs represent high, low, and moderate productivity respectively.

I used different emojis to represent different emotions. This was an attempt to datafy and personalize my learning to a degree.

Findings 

  • The visualization did not give an explicit correlation between my emotions and my productivity is given a particular event.
  • If it was hard for me to link my emotions and their impact on learning I wonder how a machine will achieve this
  • I had assumed that if am tired and frustrated I would definitely perform low but I was surprised that it was not the case. This shows me that sometimes not so positive emotions do not necessarily have a negative impact on learning 
  • Also, I found that just because one is happy or calm does not mean they will have high productivity 
  • In relation to emotions and events, I realized that positive emotions were mostly linked with my birthday events. This showed the value of the individual