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Integrating learning science with digital software development

04 Sep 2020

Integrating learning science with digital software development

James Skilton, Tax Technology Associate, PwC UK
Integrating learning science with digital software development

Bringing learning theory into practice

A lot of thought has gone into finding the single best way to learn. Arguably there is no one perfect solution, given that everyone thinks and operates differently. People engage with, and have a preference for, learning in many different ways: by reading and writing, via a hands-on kinesthetic approach, or a rigorous Leitner Box approach - to name but a few. The variety of learning methods available continues to expand as technologies such as VR become more readily available to Learning Technologists. 

When developing Learning Lab, we built the product around key learning science theories to create what we consider an effective learning experience:

  • Spaced learning 

Spaced learning is the notion of delivering condensed information repeatedly over a short period of time with stimuli in between each learning. Spacing periods of engagement with learning develops long-term memory through the temporal patterns of the stimuli.[1] The repetitive nature of the spaced learning process enhances memory by engaging the learner on selected subjects in short bursts, meaning they retain more information.

  • Forgetting curve 

The forgetting curve is a hypothesis created by psychologist Hermann Ebbinghaus.[2] His research indicates that memory exponentially decreases over time. Ebbinghaus was able to quantify this decrease as an equation that highlighted how much extra time someone would need to spend relearning a particular subject. This was defined as ‘savings’.[2] For example, if someone had a ‘savings’ of 60%, they would need to spend 40% more time relearning lost information.

  • 2 sigma problem 

This theory, observed by Benjamin Bloom, compared students who were tutored one-on-one through the ‘mastery learning technique’ with students who were taught in conventional classrooms sessions.[3] Bloom showed that the average tutored student performed better than 98% of those taught in the conventional classroom.[3] His results highlight the benefit of one-on-one learning experiences and the positive influence that structured learning has on learners’ outcomes.. 


How did we translate these theories to some key features on Learning Lab?

The five key features of Learning Lab that were developed were informed by the theories of Spaced Learning, the Forgetting Curve, and the 2 sigma problem:

  • Short Bite-sized content

Our bite-sized content allows users to learn in short bursts with clear, defined end points. It encourages them to take breaks and return for more, creating the temporal patterns needed for spaced learning theory to make an impact. The hard part is ensuring they want to come back for more, which leads to our second feature.

  • Gamification for regular engagement

Gamification encourages users to feel invested in our product, making them more inclined to return to the platform by rewarding knowledge development and engagement. Our star system rewards learners when they answer questions correctly, and results in learners being asked increasingly difficult questions - so that they feel challenged. Through this strategy learners experience a platform that tailors itself to their learning needs. 

  • Question on subjects the learners are most likely to forget 

Based on the forgetting curve, Learning Lab has created an algorithm that asks questions on topics that the learner is most likely to forget. We like to call this our quiz section. This feature prompts the learner to retrieve previously learnt information at just the right time to ensure long term retention of that information.

  • Giving the learner extra content on topics they have forgotten

By regularly quizzing our learners we are able to develop an understanding of their areas of weakness. This gives us the opportunity to deliver targeted content that addresses possible gaps in a learner’s knowledge. Importantly, we are able to present this information in a different way to how the learner initially ‘learnt’ it. In so doing, we are able to account for the fact that learners prefer to engage with content in different ways. We call this is our Boost section. This feature focuses on the 2 sigma problem, replicating the one-on-one learning experience by offering tailored content.

  • Varying question difficulty 

Increasing the question difficulty through the Quiz section allows users to test their knowledge of topics they previously learnt. The questions range from level one to level three and are based on Bloom’s taxonomy. Easier questions might ask ‘what is that?’, whereas harder questions ask ‘how and why is it like this?’. This is where our gamification feature steps in. The stars are a great way of testing how much knowledge the learner has retained over a period of time.

Through the deployment of Learning Lab, we have demonstrated that users learn 10% more information, in 30% less time, than users learning the same information in a traditional classroom-based environment. This suggests that Learning Lab increases retention and efficiency when learning new materials. It adds evidence to the argument for embedding theory within digital learning products.



[1]: Kelly, P. Whatson, T. (2013) Making long-term memories in minutes: a spaced learning pattern from memory research. https://www.frontiersin.org/articles/10.3389/fnhum.2013.00589/full

[2]: Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology (H. A. Ruger & C. E. Bussenius, Trans.). Teachers College Press. Memory: A Contribution to Experimental Psychology.

[3] : Bloom, B. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring (Vol. 13, No. 6. (Jun. - Jul., 1984), pp. 4-16). http://web.mit.edu/5.95/readings/bloom-two-sigma.pdf


James Skilton
Tax Technology Associate

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