Data Visualisation #5

A week of ‘effective & ineffective communication’

Week 7 was not my best week of the course so far, and this data visualisation is not the most accurate I’ve created so far, and it’s also the one that took me the most work to design…

This week I was reflecting on the different ways education is ‘datafied’, not only in the more traditional sense of data collection, but also in the way that communication is structured, presented and encouraged in an ‘modern’ educational context. The ways in which students can contact me as a teacher are many! Students can:

  • Write an email
  • Send a message (in Teams)
  • Video call
  • Send a message through the school’s LMS
  • Visit office (when in school)
  • Contact administrative staff at the school to pass on messages

Always available

Teams, messages, emails, video calls. Educators can be contacted in almost every possible way and at any time. Although the expectation of ‘replying’ and ‘being available online’ varies between institutions, the thin line between the ‘private life’ and ‘work life’ is blurring now more than ever, especially as many people are teaching and learning from home.

Instantaneity

There is also a sense of ‘instantaneity’, where students, educators and administrators have different expectations than before in terms of the acceptable time required to reply to a message, email, etc. While in school, it is assumed that teachers are ‘busy’, as they are in the classroom, with students, in meetings or supporting in extracurricular activities.

While working from home, this expectation has changed, I think, as it is assumed communication is easier and more immediate. My experience this week was that I ‘expected’ in a way to receive a response from a colleague almost immediately after I saw the ‘read’ indicator next to the message. I knew the he had read the message, and therefore a reply was expected soon. The same way, directly or indirectly, other people ‘expect’ me to reply to communication as quickly as possible.

Miscommunication & generational gap

The way I tend to communicate with students, is more closely related to the way I communicate with my friends. I use emojis, giphs and memes to transmit the message and meaning I want to.

On the other hand, with some colleagues communication is more formal and direct. Also, depending on the cultural context, some interactions might be perceived as ‘cold’ or ‘unfriendly’ depending on who is sending the message and who is receiving it and interpreting.

Teaching & Communicating with Data

Overall, the connection I wanted to make, although maybe not so successfully, was that ‘communication’ in an educational context could be said to be a ‘form’ of data that is shaping relationships, learning and teaching between students and educators around the world. COVID allowed for a rapid change and adoption of new technologies and practices, and at the same time, redefined, in my opinion, the way students and teachers interact online.

One Reply to “Data Visualisation #5”

  1. I can understand why this was a hard dataviz to make — you are grappling with some really complex issues. However, I like the way you have attempted to identify the forms of commmunication that are effective versus those that are not — the pink-coded “messages” look much less effective, and much more prone to problems of misinterpretation, wrong information etc than the others. Given how social media messaging has become so dominant of late, it would be interesting to have large-scale data on this wouldn’t it! Maybe short-form messaging is a major social-technical problem of our time?

    I think this line from your commentary indicates something very important: “‘communication’ in an educational context could be said to be a ‘form’ of data that is shaping relationships, learning and teaching between students and educators around the world”. Of course, in one sense all digitally-mediated communication has to be translated into data to be transferred from one site and user to another. Notifications like “read” — a simple signal from the data — do indeed give us a sense of instantaneity. In another sense, our typed words and voices might be translated into data for analysis too. And that could have significant effects on relationships, learning and teaching. What if your “productivity” as a student can be assessed from your typing in discussion forums, your spoken contributions to videoconferences etc? One aim of some learning analytics research and development is automated communication via educator-chatbots, for example, that can respond to student voice queries. I’d encourage you to continue thinking about these issues at the intersection of communication and data in education.

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