Three Future Features for Digital Learning Tools

2018/06/08

I’ve written here about how you can get learning results with nothing more than a pencil and a few pieces of paper. And in some cases it’s the best strategy.

The future of teaching and learning, though, will take place in an environment shaped by digital platforms and tools. It’s already happening, and we’re all better off because of it.

Digital tools reduce the admin work of learning, not only speeding things up, but making personalized learning accessible and less expensive.

That said, digital tools, if designed poorly, commit learners to interacting with them in one of two undesirable ways: 1) by adopting a more passive posture; and 2) by treating them with less value than a traditional learning process.

When, on the one hand, a computer constantly decides what you’ll study, it’s easy to understand how a learner could become passive. There’s no particular need to set goals, and, in limited interfaces, taking charge of the learning process simply isn’t possible. At most, you get to choose between different software packages, in the role of a consumer, the way you might choose between two different video games.

The second case – where the learning process takes the form of an interesting distraction – is really only a symptom of this passivity.

It becomes most obvious, however, when the platform trivializes the learning process in an environment that’s highly gamified, when learning is only justified on the basis of how fun its games are.

To address these issues, future learning tools will incorporate these three features.

1) Reporting Systems That Are Part of the User Interface

Digital learning systems usually come with some kind of reporting tool – whether it’s number of flashcards studied, performance across units, or simple metrics about hours logged in.

These usually have value, but many users don’t even look at them. Why? They’re buried within the application, in some submenu or account page. And even if users do find them, they don’t know how to connect them to actionable insights.

Data, however, can be presented in an interactive format that presents clear choices for how to study.

Reviewing the Kanji does this, to a somewhat primitive degree, by adapting the Leitner method to an interactive bar graph.

Clicking the chart takes you directly to the material you are to study, and it’s clear what the different choices mean.

2) Outside Data Integration

With the introduction of Health Kit into iOS, the quantified self movement (a heretofore somewhat arcane, but legitimate form of self-observation) became enshrined in one of the most popular human interfaces ever known.

Future learning tools will help learners interact with their learning data in part by allowing them to compare and relate this data to the other measurements they (or their iPhones) are making about their lives.

Supermemo already tracks sleep patterns and correlates them with learning outcomes. But learning programs can also offer insights into how diet, time of day, exercise, temperature, blood sugar levels, and so on, affect the ability of students to learn.

Future learning systems will provide inputs from these other sources so that learners can drive the search for their own insights.

3) Transparent and Meaningful Gamification — That Can’t be Gamed

Gamification provides valuable motivation and, in part, addresses concerns about the actionability of data raised above. Gamification, however, can also distort the learning process by leading students to focus on the ends of the game, rather than on their own circumstances – or the purposes they set for their own learning.

A lot of gamified learning sites like Khan Academy employ gamification systems to increase website engagement – not just motivate learning. To do this, it utilizes a set of so-called Energy Points indirectly linked to the outcome of lessons.

This is not in itself not a problem. Such gamification can form the basis of a legitimate marketing or engagement strategy. These Energy Points are also calculated in a way that is fairly easy to relate to learning progress.

But we can still observe an underlying problem, one that crops up in the issue of cheating. Of course, cheating is not something that the system of gamification needs to solve on its own – it will probably never go away – but there is a difference between cheating in a game and cheating in an academic setting.

Cheating or ‘gaming’ the system in a video game, for example, is not particularly meaningful – you can simply reset the game. It can come across as an Easter Egg, or something kind of funny.

Cheating in an academic setting, however, has consequences on the whole approach that a learner takes toward the material. It creates confusion about the standards of a subject and how they relate to the learners circumstances and goals.

Of course, in a school or other academic community, it also undermines the whole teaching and learning process.

But cheating Khan academy is not like that. It’s like finding a shortcut in a video game.

I think Khan Academy can start addressing these issues by stopgap measures, such as requiring users to take a level assessment and subsequently rewarding users for taking courses at their actual level.

By more closely relating the challenge of actual learning to its Energy Points system, Khan Academy can make this kind of cheating less meaningful.

Future systems, though, will use data about its users to relate its games to learner goals.