The Problem with Learning Apps

And why “learning” might be the wrong word here.

6 min readOct 17, 2013

Some years ago during the boom of the learning apps a Mashable author wrote that it’s possible to become the master of any subject with a suite of quiz based apps.

Frankly, this claim insults my intelligence.

If the approach “there’s a quiz for that” actually works for knowledge retention, I must be an imbecile since for me the knowledge retention is usually out of the question with quiz apps.

The problem is that proportionally, the majority of learning apps focus heavily on dumbed down quiz modes. While it’s an extremely popular and familiar approach to all of us, a quiz mode is based on testing. Testing alone doesn’t produce deeper learning and knowledge retention.

There are apps that do a better job than quizzes at enabling users to learn something, but they only work around content/study material: TEDed goes for lego-like approach to accommodate different learning needs, Coursmos focuses on micro format to avoid boredom, WWF Together brings highly visual interactivity to otherwise sometimes boring content, Urban World is based on the experience of discovery. The thing is, content consumption alone doesn’t do the trick for knowledge retention either.

It seems that proportionally there are not that many apps that are concerned with the actual science of learning, for example, Cerego which analyzes your memory performance while practicing.

The missing link between study material, deeper learning and testing is a carefully designed method of learning. Apps that are concerned with the knowledge retention teach you how to incorporate a method into your mental model of the subject you are trying to learn. They don’t want you to drill into your head that “beer” is “cerveza” in Spanish, they teach you how to retrieve the “cerveza” from your memory with the help of a method.

Notice how apps that don’t even have a subject to learn and are solely based on practicing a specially designed retrieval method — seem much more engaging and addictive: Reform uses geometry to break creative blocks; Matchagram uses recognition method to train your memory.

Our team wanted to be among those who mean it when we call our product a “learning app”. We wanted to design a learning method that fits our subject matter. To do that, we did extensive research on the psychological and brain function aspects of learning.

We’re happy to share this (much shorter) version of our findings below. These insights will be most beneficial to those in the learning app business.

Find the hack for your subject to learn

We, humans, use hacks and shortcuts to make our life easier. The same goes for learning — for example, children learn multiplications much faster with patterns-based tricks. Many of you had your own hacks figured out to remember birthdays or to learn driving rules. If your subject doesn’t seem to have obvious hacks — it doesn’t mean that there are no people trying to find best ways to learn it. Forums and platforms such as Quora are where you’re likely to find people discussing “the best” and “the easiest” ways for pretty much anything. Test out the hacks you discover people use in a closer circle and see if you can apply those to your app — a hack is a very powerful method to learn.

Success of learning does not come from studying hard

OR a lot

It comes from practicing to retrieve the learned material which enables us to retrieve it automatically in the future. Starting from the first retrieval practice experiments in 1923 and all the way to the latest experiments in our time — the results are consistent: learners who practice retrieval perform much better than learners who only study the material. Even though retrieval practice can be done via testing, don’t mix these up. Retrieval mechanism’s purpose is to build and strengthen correct brain paths between query and correct response. So reconsider the “we’ll just add a quiz mode” and think about various non-linear ways you can make your learners practice establishing connection between “beer” and “cerveza” or “smallest mammal in the world” and “Pigmy possum”

Alternate approaches to the same problem to solve

Repetitive approach only makes our brain lazy. If you’re teaching math — offer variety of ways to think about the same problem. With this presented assortment, the learner will pick up deeper patterns via comparison. Simply mixing in True/False and Match types of questions into Multiple Choice set or adding “None of the above” option already makes the brain work harder. In short, the more our brain is forced to switch angles — the deeper the learning.

Spacing between learning sessions is crucial

Learners who practice their study material over three days and then take a test perform much better (are able to retrieve more from their memory) than the learners who study everything in one day and then take the test. What we usually call “the time to digest the material” is actually our brain’s need to strengthen the connections between the material and retrieval paths.

Characteristics of good feedback

We tend to think about feedback as the main part of testing — learners are tested on the basis of being right or wrong. But feedback when done right is a very powerful retrieval technique:

Showing only “right” or “wrong” outcome is probably the worst and least useful you can do. Next least useful approach is to simply refer the learner back to the study material if they are wrong — “incorrect, go study more!” Hopefully now you can see that the least useful mechanisms are the most widely used ones in so called learning apps. These approaches make no difference for your learner’s knowledge retention. Main characteristic of good feedback is that it’s targeted to specific error — it shows the learner the connection between specific query, their incorrect answer, study material surrounding the instance of incorrect answer, and correct answer in wider context.
Wrong answers are not random. There’s usually the same misconception a learner has that can be traced through the majority of their incorrect answers. Help trace these down and correct them with targeted feedback.
Withholding feedback makes learners think more carefully before choosing their answer, which increases deeper learning. If the answer is one click/tap away — learners tend to skip deeper retrieval practice and use the mental shortcut. Think about opportunities to allow your learners to make a repeated attempt of retrieval after they know they might have been wrong and before showing them the correct answer.
Feedback that is too extensive on correct answers will often become annoying and learners are likely to skip it. The danger here is that when it’s provided for incorrect answers where its best value lies — the learners are likely to start skipping it as well. Make feedback on correct answers super light. To account for situations when the correct answer might have been a lucky guess — offer manual ways to access the extended feedback, but don’t force it.

I hope these insights have gotten you excited about all the potential improvements you can make for your learning app. Thanks for your attention!

This research was done for designing the upcoming version of Dgrees — an unapologetically simple Celsius & Fahrenheit converter that will teach you to convert on your own.