Tracking digital reading behaviour to improve students’ e-reading experiences

If I were going to use tracking to enhance publishing practice, I would like to use it to address the needs of educational publishing. In my experience in psychology and biology classes in my undergrad, it’s becoming very common for textbooks to come with digital components. In my classes it was usually a website you could log in to and access the text on the web, as well as view other media. There was usually a limited and finicky highlighting and annotation function, too. My experience as a user varied a lot from book to book. I remember finding some textbook’s corresponding sites useful in their content but frustrating to navigate. I think exploring student preferences and consumption behaviour would be a great application of tracking. If I were an educational publisher I would use reader analytics and tracking to specialize in delivering very user-friendly e-textbooks.

One challenge would be that students are required to read their textbooks. This means they don’t have the option to skip passages that are unreadable. The data would show a high engagement rate, but only because the students had no choice but to finish the chapter. Even in cases where they didn’t, this data would be skewed in that it would not be a reliable measure of the readability of the passage.

For that reason, my tracking in education publishing would instead focus on two other areas. The first would be on measuring time. For example, measuring the average length of time it takes students to complete a passage, or how long students are able to focus on a typical textbook before they have to put down their device. This information (which would likely differ between different fields) could be used to tailor the length of sections and chapters so that they are in readable sizes, and to let publishers know which parts need work before they release the next edition. It would also be useful for professors in planning their syllabi.

The second area I would focus on is making the reader analytics software responsive and customizable. The reader would create an account and read the text, and the reader tracking software would become familiar with their particular reading habits. Once the software had analyzed enough of my reading behaviour data, it would be able to tell me how much time to set aside for each particular chapter, when my prime studying time of day is, and how often I need to take a break.

The challenge here would be that the customizable software would function better and better over time as it became familiar with your reading habits. But by the time the software got good at understanding the student, the semester would be over. So maybe my reader analytics software could include a short “training” period where the reader is asked to run through a few pages of different kinds of text, designed to represent the kinds of text common in that particular field. The reader’s habits could then be understood and taken into account by the software a little faster. This is kind of like how Cortana (the Siri-like bot that comes with Windows 10) “learns” my accent and dialect of English by having me read particular phrases out loud.

The reason I would like to focus on educational publishing is that I would rather apply reader analytics to the goal of improving student success and experience than to hyperfocused marketing campaigns. As textbooks today in many fields are a hybrid of print and digital, educational publishers must understand student’s preferences and behaviour and take them into account when planning digital reading experiences.

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