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The gym badge after an Ex Raid.

Today I reflect on another successful Pokémon Go Exclusive Raid I had last week.

This was my second in about a fortnight. I combine what I wrote about moving from data to information and my resolve from the first raid.

It took only a few weeks for talented individuals to crowdsource user data, suggest patterns, and provide timely information. For example, there is now at least one source on how to increase your chances of getting an Ex Raid Pass and a local example of exactly which gym raids to target over a five-day period. Both avoid “luck” and rely on careful data analyses and projection.

My second Mewtwo.

On a personal note, I managed to accomplish during my second Exclusive Raid what I did not in my first one. For example, I remembered to screen capture my progress.

What I did not anticipate happening was GPS drift occurring one minute before the raid started. One other player and I had to restart our games to reposition ourselves. As I was coordinating the efforts of the group that was with me, I hope that I did not cause too much worry.

I caught my second Mewtwo. It was not as good as my first one, but that is a good first world problem to have!

I also helped an auntie catch a Mewtwo because she was too nervous to do it herself. I showed her the lock-circle technique and how to time the throw of the premier Poké ball. I was hoping that she would eventually learn how to do this herself after a few viewings. (Un)Fortunately, I demonstrated this only once — I caught the Mewtwo in one throw.

Both the uncracking of Niantic’s complex formulae for Ex Raids and how to catch Mewtwo share a common strategy — the scientific method. This is the relentless pursuit of data, testing of hypotheses, rejecting of uninformed hearsay, and retesting of attempts that seemed to provide positive results.

As there is a large number of Pokémon Go players, there is still a mathematical gamble to be selected for an Ex Raid Pass. This is the logical luck of the draw that you cannot control.

What is manageable, however, is everything else — from data analysis of which gyms to hit, to video analysis of catching Mewtwo. Ignore these and you are being wilfully ignorant and relying on dumb luck.

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.

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