How to teach Reading Analytics - an attempt
In a few days I will be teaching a course about a topic I have been deeply involved since a while ago, typically known as Reading Analytics.
Reading Analytics has been around my professional life since I co-founded 24symbols. From the very start we provided publishers with a simple (but at that time, unheard of from other book retailers) dashboard with data and graphs about how our users read each publisher's catalog. It was probably too soon and not many publishers were ready to learn and take advantage of data. But we could see, as time went by, the need was growing.
At the same time, and due to my teaching side, I was seeing how the trend towards online and blended learning required more and more information about how students behaved while the course was running. When teaching in a physical class, I can always see how people react to what I am teaching and the way I choose to do it. Are people bored? Interested? Sleeping? But when I teach online, the segments are more difficult to handle:
- The top 20% students are easy to see. They participatte in the video conferences, access the thread discussions quickly, etc.
- The bottom 20% students are also easy to find. They do not log in, do not participate, do not access the documentation in time, etc.
- However, what happens with the remaining 60%? I never know. They log in but seldom participate. The access the thread discussions and sometimes participate, but sometimes not. Are they having any issues? Can I help them anyway?
With both thoughts on our minds, we decided to build a product that could help companies and institutions grasp a better understanding of how the users, patrons or students read in order to provide them a better service. The path has been really interesting.
When Daniel Suárez, CEO and incredible mind behind Zapiens asked me to be a teacher at Mondragon University's Master's Degree on Learning and Innovation, and focus my part on Reading Analytics as a subset of the module on People Analytics, I found myself on the challenge of organizing a topic that, up to where I know, had not been taught before in this context. There was no structures from previous courses I could use. I could only obtain help from the academic work and what my partners and I had learnt in the past years.
The result is a five-day course that will introduce that fascinating concept of "reading".
Unit 1 - Theory of reading
The first unit is about the theory of reading. While this requires a whole course by itself, here we will just oversee the basic elements of what we currently understand as reading: decoding and reading comprehension; word, sentence and discourse; etc. And all in a practical approach, with examples and exercises taken from the best bibliography (e.g. "Laboratorio Lector" by Cassany) I was able to find while studying this fascinating topic myself.
Unit 2 - Neurology of reading
Again, a unit that would take a whole master's degree by itself. While one may have a thorough understanding of what reading implies to a person and all the data-related topics we will see before, I belong to a school of thought that tries to understand where things actually come from. In the case of reading, there are still many things we do not know, but there are also some great books that explain where we stand now. Here we will start by differentiating between visual acuity and seeing well and correctly. And then we will briefly cover how our brain works when we see by using notes from the wonderful book Proust and the Squid, by Maryanne Wolf.
Unit 3 - Traditional reading analysis
Now we start getting into the gist of it. How have researchers and companies been able to analyze how we read until now? Even in the digital age, reading analysis has been poorly done. Some researchers have been able to advance the state of the art due to herculean efforts (like the ones shown in this metaanalysis of studies comparing screen and print reading) but unfortunately reading analytics has not gone, for quite a long time, much further than reading speed and the like. However, there are still some interesting things we can do at this stage, as shown by, for instance, this interesting article from Medium that explains how this blogging system tried to understand reading behavior to improve their own systems.
Unit 4 - Reading behavior analysis
And finally we get where we stand today and the reason this course is finally being taught. The mixture of technology, books, data and (as we will see in the last unit) ethics brings a great opportunity in terms of understanding how and why people read. This brings many challenges, some red lines, but also many opportunities: how to engage people to reading (and therefore, to critical analysis); how to better server your customers if you are a publisher, a library, or your students if you are a professor in university or a teacher in middle school and high school. There have been too many years where students and readers have been treated as a black box. Many attempts have been tried to make people read more and better, but not many of them tried to see how they were actually reading.
I have been involved in changing this at Quantified Reading, providing reading analytics to educational institutions, publishers and other industries so they can better server their users and customers alike. The learnings throughout this epic journey is what I will try to explain here.
Unit 5 - Technology, psychology and ethics
What I probably love the most about reading analytics is that it is mixture of so much cool stuff at the service of books and readers: technology, data and psychology. This last unit, in a summarized way, tries to show the state of the art for these:
- Technology: mobile app technology is the best current way to obtain reading data from a statistically significant volume of readers. But there are other things that are being built. Some are too early for everyone to use it. Some are totally available but we still do not know whether they should be used or not.
- Data: the ability to manage gigabytes like nothing has changed the culture of data-intensive products. But at the same time, most customers have very simple (but critical) needs. We should not just forget about the low-hanging fruits that good, advanced data analysis may provide. Deep Learning is cool, but how many readers do we need for it to actually work? This requires very specialized people that actually understand data in different scopes: from the huge gigabytes of reading behavior to the small data related to how the metadata of a book informs us of some special characteristics we previously did not realize.
- Psychology: in some cases, reading is an engagement game. The author tries to engage the reader with language, action or settings. The reader wants to be engaged... but what if it just doesn't work? What if our current context-switching life is taking us to Netflix? A great movie is just... great. But aren't we missing the advantages and pleasures of a long reading session at home? In some cases, technology can be a crucial role in engaging readers. I am a believer that current reading technology does not help add, but remove, reading fans. But that can change, and behavioral psychology may help on that.
- Ethics. Last but not least at all. If something has changed in the last couple of years has been our understanding of how privacy affects us all in this online world. Therefore, anything that smells like "obtaining data from me" is a no-no. How can we make good use of reading data without compromising the values and the law? This is where ethics comes into play (and for instance, the reason why Quantified Reading has an Ethics statement from day 1).
If I am able to keep my students awake after these days, I hope they will have learnt where we stand in terms of reading analytics, and all the amazing possibilities there are. And I will have learnt from them for sure.