A Week of Communication

Visualisation of personal communications by platform vs source

This week I captured direct communication data points. Direct means, I’m communicating with someone or I’m involved in a communication direct to me only. Thus, mass messages and emails are removed from work emails and WhatsApp/messages/emails groups. I focused on 4 elements of the data captured.

The legend

  • Communication source: who I am communicating with. I identified 3 main groups : Family ( Husband, Kids, and siblings), Work, Friends, others (in this week it was some Unv communications and one face to face communication with a retailer.
  • Communication platform: Face 2 face, video call, voice call and messages
  • Communication impact : answering the question if this particular communication had a positive, negative impact of how I felt from before the communication to after.
  • Timing of the communication : morning and afternoon (12pm is the time breaker)

I developed the visualisation on focus areas. One with the Sources of the information as the main pillars and another one on the Platforms being the pillars.

Reflection points:

  • Family is the most source of my communications with face to face and voice call occupying the highest interactions. Impact of communication is almost equally split with many with “no impact” communications – catching up and status updates communication
  • Work is very focused on video calls which is expected with working from home. Also the impacts tends to be equally split however more positive communications with video calls compared to messages.
  • For friends, there is a lot of positive communications especially the face to face ones.

Reflection points:

  • Face to face communications has more positive effect as a percentage from all other platforms
  • Video calls has no impact when it came to work and more positive when it came to family.
  • Voice calls are the least of all focusing mainly non family and friends.
  • Messages remains the highest contributor especially with friends and then comes work and family.
  • I need to be calling friends more often instead of depending on the messages as messages tend to create negative impact while calls always give a positive impact.
  • Messages tend to have more negative or no impact across all sources especially friends

Reflecting on Governing with Data Blog and one of the “Tweetorial’ questions: ” What policy problems might big data be used to address in education, or what new problems might governing with data generate?” I wanted to extract from my visualisations some communications policies and measuring validity as possible.

A communication policy that can be deducted from this data set is: in order to improve motivation at work, messages should be eliminated as a mean of communication and face to face and/or voice calls communications should be introduced.

This could be a sample policy deduction looking at the communication sources and platforms from a single attribute of creating a positive impact on the communicator. However, the policy didn’t take into account the condition that, currently there are no face to face data to support this argument in the work environment. It has also ignored completely that many “no impact” communications of messages which could be more than sufficient in the work place. The policy is build on a specific set of data collected and ignored other sources and validations into what could be behind the impact on communication.

This is a basic sample of policy development based on a set of data where little contextual and additional information / data are considered. We can also see that technology platforms (in this case phone, video conferencing and messaging apps) are impacting policy development and measurement. According to Williamson’s concept of “Data Instrumentation” “

Understood as digital policy instruments, data processing technologies can therefore be seen as a digital policy instruments that reproduce and reenforce existing.

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

From an educational perspective, what would be the best communication platform that would improve learning and teaching experiences especially in this increased dependencies on digital education and hybrid learning environment? What data we need to collect and analyse to drive policies related to communication platform to govern the new learning environments?

2 thoughts on “A Week of Communication

  1. Very interesting reflection on what kind of policies could be developed from data about communication. Quite a few institutions have in fact tried to introduce policies such as “no email at weekends” in an attempt to govern staff communication better. So I think it’s plausible to imagine that a visualization of staff communication could be used as a source of policy decision-making at an institutional level.

    Do you think that communication data could be used for other governing interventions? Data about course communication on an online learning platform, for example, could be analysed to generate insights into student communication patterns, which could then be used by course designers to seek to govern students’ participation in courses more effectively (e.g. if the data show that taking part in peer-to-peer discussion boards measurably enhances outcomes, the designers might actively prompt, nudge or cajole and reward other students to do so too). This isn’t so much about ‘policy’ but about other techniques used by education and technology providers to govern learning environments and practices.

  2. “This isn’t so much about ‘policy’ but about other techniques used by education and technology providers to govern learning environments and practices.”

    How we can govern learning environment without policy … like a mechanism for continuous adjustment and improvement based on data !

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