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Posts Tagged ‘drip

Yesterday I mentioned how the edtech vendor DRIP — data rich, information poor — approach was like torture. Today I elaborate on one aspect of data-richness and link that to an under-utilised aspect of game-based learning.

The data-richness that some edtech providers tout revolves around a form of data analytics — learning analytics. If they do their homework, they might address different levels of learning analytics: Descriptive, diagnostic, predictive, prescriptive.

A few years of following trends in learning analytics allows me to distill some problems with vendor-touted data or learning analytics:

  • Having data is not the same as having timely and actionable information
  • While the data is used to improve the technological system, it does not guarantee meaningful learning (a smarter system does not necessarily lead to a smarter student)
  • Such data is collected without users’ knowledge or consent
  • Users do not have a choice but to participate, e.g., they need to access resources and submit assignments to institutional LMS
  • The technological system sometimes ignores the existing human system, e.g., coaches and tutors

I define learning analytics and highlight a feature in Pokémon Go to illustrate how data needs to become information to be meaningful to the learner.

First, a seminal definition from Long and Siemens (2011):

… learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs

ERIC source

The processes of measurement, collection, analysis, and reporting are key to analytics. I use a recent but frustrating feature of Pokémon Go to illustrate each.

My PoGo EX Raid Pass.

The Pokémon Go feature is the “EX Raid Pass” invite system (I shorten this to ERP). Players need to be invited to periodic raids to battle, defeat, and catch the rare and legendary, Mewtwo. The ERP seemed to be random like a lottery and rewarded few like a lottery as well.

Even though Niantic (Pokémon Go’s parent company) provided vague tips on how to get ERPs, players all over the world became frustrated as they did not know why they were not selected despite playing by the rules and putting in much effort.

To make matters worse, a few players seemed to strike the lottery more than once. At the time of writing, I know of one player who claimed on Facebook that he has eight ERPs for the next invite on 9 Jan 2018.

Eight Ex Raid Passes!

Players have swarmed Reddit, game forums, and Facebook groups to crack this nut. Some offered their own beliefs and tips. Much of this was hearsay and pseudoscience, but it was data nonetheless — unverifiable and misleading data.

A few Facebookers then decided to poll ERP recipients about where their EX Raids were. This was the start of measurement as they looked for discrete data points. As the data points grew, the Facebookers compiled lists (data collection).

Such data measurement and collection was not enough to help non-ERP players take action. The collected data was messy and there was no pattern to it.

I know of at least one local Pokémon Go player who organised the data as visualisations. He created a tool that placed pinned locations in a Singapore map as potential EX Raid venues. With this tool, it became obvious that locations were reused for EX Raids.

Potential EX Raids hotspots.

Pattern of reuse of venues for EX Raids.

However, such a visualisation was still not information. While the data pointed to specific spots where EX Raids were likely to happen, they still did not provide actionable information on what players might actually do to get an ERP.

To do this, Facebooker-players asked recipients when their ERPs were valid and when they raided those spots previously. One of the patterns to emerge was normal raids of any levels (1 to 5) at hotspot gyms a few days before Ex Raids. So if an Ex Raid was likely to happen on Saturday at Gym X, the advice was to hit that gym on Wednesday, Thursday, and Friday to increase the likelihood of receiving an ERP.

Collectively, these actions were a form of analysis because of the attempts to reduce, generalise, and ultimately suggest a pattern of results. This actionable information was reported and communicated online (social media networks) and in-person (auntie and uncle network).

The advice to players seeking ERPs is a reduction of much data, effort, and distilled knowledge from a crowd. It illustrates how data becomes information. I have benefitted from the data-to-information meta process because I followed the advice and received an ERP (see image embedded earlier).

The advice does not constitute a guarantee. With more players using this strategy, more will enter the pool eligible for selection. There is still a lottery, but you increase your chances with the scientific approach. You do not just rely on lucky red underwear; you create your own “luck”.

Now back to edtech DRIP. Edtech solutions that claim to leverage on analytics are only good if they not only help the technical system get better at analysis, but also help the teacher and learner take powerful and meaningful action. Edtech solutions that are data rich but information poor only help themselves. Edtech solutions that turn rich data into meaningful information help us.

I reserved this read, Why We Must Embrace Benevolent Friction in Education Technology, for the new year.

A few concepts from the article jumped out at me, but the one that stood out was DRIP — Data Rich, Information Poor. What does this have to do with edtech?

DRIP is a criticism of edtech companies and providers that tout data analytics as a means of controlling, feeding, manipulating, or enabling learners. Data is just that, data. It is not organised information that might become internalised as knowledge and then externalised as intervention.

What edtech providers, particularly LMS and CMS companies, have yet to do is help their clients and partners make sense of the data. This is in part because programmer or provider speak is not the same as teacher and educator speak. There are relatively few people — like me — who can bridge that gap.

So what these providers do is reach out to administrators and policymakers because they all deal with numbers and data. They do so in a way that makes sense to them. It does not help that these discussions are not transparent and also make little sense to teachers and educators.
 

 
A while ago I heard about an interrogative torture technique that involved slowly dripping water onto a victim’s head. The slow drips quickly wear down psychological resistance and the interrogators get what they want.

That method does not transfer via DRIP in edtech. It will only drown clients and partners in meaningless data that does not actually help teachers or their learners.


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