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What Neuroscience Tells Us About Learning

Students today spend more time on academic learning than generations before. They cover more ground – learning things like programming or environmental science that their parents didn’t have to fret about – and spend more hours doing homework after school. One study found that in the sixteen-year period from 1981 to 1997, there was a 25% decrease in time spent playing outside and a 145% increase in time spent doing homework. 

As our society advances even more, students will have to cover more and more content, not just during their K1-2 school years but throughout their careers. By some estimates, students growing up today will have to learn entirely new domains and reinvent their careers every few years. Learning is no longer limited to younger ages but is becoming a lifelong journey. 

What does this really imply?

Students have to learn to learn –  acquire knowledge and master concepts faster – without which they will find it harder to stay abreast of new developments coming their way. But it’s not just about superficially memorizing things. Students will have to understand how to apply their newfound knowledge to problem-solving. In other words, learning has to become a more efficient process in terms of speed, depth, and understanding.

Thankfully, advances in neuroscience are giving us clues on how to make learning more efficient. Understanding how the brain processes information can help students take charge of their own learning, not just in their student years but throughout their life.

Neuroscience Of How Our Brain Learns

At a high level, we can view learning as a three-step process. When we encounter any new information, our brain first encodes this information and places it in short-term memory. For example, if you come across a new fact, say learning about a new breed of dog, the information first goes into your short-term memory. The next day, you might recall that your childhood friend had a similar-looking dog, and now you start to remember other details about the dog – how friendly it was, how it played, and so on.

At this stage, your memory is in long-term storage; it continuously consolidates other pieces of information that you already had. Over time you might add more connections to this piece of information, maybe a joke you heard about it, and it starts to get more and more enmeshed with other pieces of memory. 

After a few days, you might forget the name of the breed and try to recall it. You struggle a bit and then remember your friend’s dog, the joke, and other bits of memory that were tied to it. And then the name suddenly comes back to you, and you get a sudden burst of relief! 

A few days later, as you share a story about your childhood friend, her dog and the name of the breed come to your mind effortlessly, and you marvel at how well you remember this now. 

The picture above encapsulates how our memory works. Once we consolidate information into our long-term memory, subsequent retrieval and reconsolidation help to strengthen the memory traces and make it easier to recall information in the future. 

Forgetting Is The Path To Learning

Over the last couple of decades, neuroscientists have discovered interesting things about how our memory works, and counterintuitive as it sounds, forgetting information is an important aspect of remembering! Our brain is constantly pruning information that it thinks it doesn’t need so that it can serve the really important bits of information faster. 

Imagine if your brain stored every little nugget of information that it receives – the color of the shirt a passenger wore in the subway, or the name of the street your friend in another state lives on – it would make it much harder to find the useful information that you really need. So if you don’t need any piece of information, its retrieval strength starts to get weaker. However, when you try to recall something that you have forgotten, i.e., when you have to struggle a bit to remember it, that’s when the brain gets a cue that this particular memory is important and might be needed again.

So, with the process of retrieval, it starts to reconsolidate the information – find newer connections to other traces of memory so the memory is stored more strongly. As a result, this process of forgetting and remembering actually helps you learn better. 

Neuroscience-based memory models give us clues on how to structure our learning for maximum effectiveness. Here are three ways to boost your learning.

Repurpose Failure

When students don’t remember or don’t apply concepts correctly, it’s a sign that the information has been stored weakly in the brain. However, instead of feeling that they are ‘not cut out’ for this kind of work, students need to understand that their failure is simply a sign for their brain to reconfigure and become more efficient. Human brains are designed to learn through mistakes, so it makes sense to reframe forgetting as what it really is – a trigger that tells us that we need to take additional steps to ensure learning is complete. Students should use the opportunity to review concepts again and try to reconcile the mistakes so their understanding of the subject increases.  

Adopt Active Learning Strategies & Neuroscience

Adding some challenge to the learning process that taps our brain’s natural mechanisms to process, store and understand information can significantly boost learning. Such challenges are ‘desirable difficulties’ because they make learning more efficient. Here are a few strategies that students and teachers can adopt: 

  • Retrieval Practice: When learning new information, periodically quiz yourself about the central ideas and new terms encountered without looking at the text. This forces your brain to fetch the answers from long-term memory, and repeated retrieval is going to strengthen your memory.
  • Spaced Learning: To add more desirable difficulty to learning, practice retrievals after a period of time. When you start forgetting, you exert more effort in trying to remember, which then cues the brain to store the information more deeply. The gap between learning and retrieving can be anything from a day to a week – the key is that the gap should allow for some forgetting to happen.
  • Interleaving: Instead of waiting to thoroughly master one concept before moving on to the next, try mixing up different kinds of problems or concepts once you feel you have gained sufficient understanding in one. Not only does this make good use of spacing, but it also allows you to spot connections or differences between different kinds of problems. 

Research studies show that such strategies can be very effective in the classroom. In one study, students who practiced math problems in three sessions spaced apart by a week performed twice as well on the final test compared to students who did all the practice problems in one session.  In another study, students performed significantly better on their science exam when a practice quiz one month before the exam interleaved concepts on the quiz. 

Associative Learning & Neuroscience

Another useful strategy in learning is to connect the information you are learning to other pieces of knowledge you already possess. If retrieval practice creates deep roots, then associative learning creates more branches that help anchor the information better. To build associative learning

  • Find an analogy: Ask yourself if the new concept is similar to any other piece of information that you already possess. As an example, you might make a connection between gravity and magnetism as both involve a force that they can’t see and attract objects. 
  • Find a personal connection: In some cases, your personal experience can be helpful in finding connections about what you are learning. For example, while learning about the ice age, you might remember an earlier trip to Grand Coulee, where they saw how the Missoula Floods carved out a massive canyon in a very short time. The scale and impact of the event will give you an enhanced perspective of the topic and deepen your understanding. 

Conclusion

By understanding the neuroscience behind learning, students can take charge of their own learning. The key to efficient learning is to add and embrace the right kind of challenges that push our brains to reconfigure themselves. Unless students lack relevant background or specific skills to make sense of the concept in front of them, such challenges should be welcomed instead of dread. 

With a deeper understanding of the learning process, students can try different approaches and customize them to their needs. As an example, for some students, one day of spacing might be enough, whereas, for others, it might be a week. For the latter set, practicing a skill every day might not be as effective because they haven’t forgotten enough for reconsolidation to take place. With some trial and error, students can identify strategies that work best for them and become smart learners. 

This article first appeared on edCircuit

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Assessing The Creative Health Of An Organization

With the proliferation of AI tools and their tremendous potential to improve productivity, leaders are reevaluating business priorities, and more specifically changes they need to make to work and culture. Given that AI can now handle many tasks better than humans, it stands to reason that creativity will take on an increasingly important role. It not only provides a sustainable competitive advantage but also improves employee engagement and organizational resilience.

Our understanding of creativity has evolved considerably over the last couple of decades. Despite the common perception that creativity is a fuzzy skill that you are either born with or not, we now know that creativity is a highly cognitive skill that can be tracked and improved. If companies want to get a better understanding of employee creativity and how it can be converted to meaningful innovation, they first need to assess their existing levels of creativity and how their culture might be inadvertently stifling innovation. Research has shown several different dimensions at varying organizational levels impact creativity and innovation. 

The Innovation Pipeline

At the heart of any disruptive innovation is a creative idea. The creative idea often starts small and over time, with iterations and feedback, becomes a breakthrough one. The 4-C model captures the level of creativity found in the real world starting with mini-c all the way to the boundary pushing Big-C. In an organizational setting, it makes more sense to view it in three phases (little-c, Pro-c and Big-c) as mini-c creativity is associated with personally meaningful ideas whereas employees typically offer ideas that are creative in broader contexts. 

The picture above shows a simplified view of the organizational innovation pipeline. (As a side note, we refer to innovation as gathering broad support among the management/company to take an idea to market, as opposed to market success which is the more common definition. We believe that within an organizational setting our definition is more appropriate). 

The creative journey starts with one person who conceives the idea, does some simple checks and determines that the idea has potential (in other words, it is both novel and useful). She then shares the idea with her team who debate the idea in more depth and through constructive discussions improve the idea even more – finding ways to make the idea more appealing to a broader set of people, or finding solutions to remove some of the challenges in the original idea. The team then decides to build a prototype to test out the idea further with real people. So from little-c, the idea moves on to Pro-c. Finally, the idea gets buyoff from management who allocate additional resources to make the idea launch ready. There might be more in-depth user level testing and additional iterations involved at this stage. Eventually the creative idea transitions into an innovative one that has a high chance of success in the marketplace if the right processes and checks were in place. At each stage of progression, the creative idea becomes more sophisticated and more potent, finally culminating in a (hopefully) breakthrough innovation. 

The fundamental challenge organizations have is to ensure that the pipeline allows ideas to flow freely and mature, so enough of them make it to the innovation stage. This is where deliberately building an innovation-friendly culture becomes essential. 

How Culture Impacts Innovation

An organization’s culture can either nurture or stifle innovation. To understand different ways that innovation gets affected in an organization, let’s look at it from the perspective of an idea as it makes its way through the innovation pipeline. 

Individual Level

A creative idea starts with a person who perhaps notices a problem or finds an interesting connection. If a sufficient number of ideas are not being generated at the beginning of the funnel, then the likelihood of reaching a breakthrough idea becomes low. Here are a few ways that ideas don’t go past the first stage:

  • Creative Capacity: If someone lacks creative confidence or specific creative thinking skills they might be coming up with few or minimally creative ones. Or, they might not be getting any time in their schedule to reflect and think creatively. Either way, their capacity to produce creative ideas is diminished. 
  • Motivation to share ideas: Assuming that people are capable of coming up with potentially creative ideas, the next barrier we hit is sharing ideas. People are less inclined to share an original idea if they feel the idea might be ignored or judged poorly, thereby affecting their social standing. Or people might simply not want to share their ideas, if they feel that they don’t get due credit for their work. In general, organizations that are hierarchical, risk averse or biased, disincentivize people from sharing their ideas. 

Team Level 

Most people assume that psychological safety is the main thing you need at a team level to allow good ideas to emerge. While this is a necessary first step, it’s not sufficient. For an idea to grow from little-c to a more improved Pro-c version, it needs to go through some extensive discussion. The main benefit of taking an idea to a group is that different perspectives and different ideas clash in a meaningful way to create something much more powerful. This crucial step separates mediocre teams from stellar ones as it requires both high cognitive and high emotional skills from the whole team. When done poorly, ideas can zoom past straight to innovation where they then face a higher chance of failure. Below are two broad ways teams fail at this stage:

  • Critiquing Instead Of Creating: The most common mistake that people make is to focus on fault-finding, with the intent of choosing the “best” idea instead of trying to create the best possible version of each incoming idea. People might also lack skills to engage in constructive debates and end up creating either a conflict-averse culture or a highly competitive one where ideas don’t get a chance to grow. 
  • Not Experimenting: Simply talking is usually not enough for an idea to be evaluated thoroughly. Data collected through prototypes or mini-experiments can lead to more healthy debates. Cultures that incentivize bold, visionary thinking without the rigor of research or experimentation create conditions (“pipe dream” culture) where people chase shiny ideas that often turn out to be riddled with insurmountable problems.  

Organizational Level

At the highest level leaders need to create structures and behavioral norms to support innovation throughout the organization. Without adequate support, it’s nearly impossible to convert employee creativity into organizational innovation. 

  • Formal Structures: To take incoming Pro-c ideas to market-ready innovations, organizations need to have formal programs that systematically and equitably review all incoming ideas. Many companies create hackathon-like programs as avenues for employees to exercise their creativity but such programs fail to produce any meaningful innovation as they are not integrated into the regular work process. Companies also fail to create formal incentive programs specifically for creativity that tap into people’s intrinsic motivation. 
  • Behavioral Norms: Company leaders play a crucial role in setting norms that promote an innovation-friendly culture. Do they explicitly solicit ideas from employees? Do they encourage their employees to challenge the status quo? Do they involve their employees in setting vision and values? Such behaviors create a more egalitarian culture that motivates employees to go above and beyond. 

Innovation Readiness Assessment

With the increasing importance of creativity and innovation in the business world, leaders need to understand in what ways their current culture supports or stifles innovation. Our Innovation Readiness Assessment is a research-based tool that helps identify bottlenecks in the innovation pipeline. It incorporates multiple dimensions that are known to impact creativity including work characteristics and biases, and covers all stages of the innovation pipeline.

Edgar Schein, the renowned organizational psychologist and author of Organizational Culture and Leadership, noted “the only thing of real importance that leaders do is to create and manage culture.” By staying vigilant about how their culture influences innovation, leaders can ensure their company’s long-term success in a hyper competitive world.  

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Capitalizing On The Promise of AI

The rapid proliferation of Artificial Intelligence (AI) tools like ChatGPT have created both awe and anxiety among people, and led to intense debates about the future of humanity. How should people prepare for a world where AI has a significant presence? What does the future of work look like?

Business leaders have an equally enormous task ahead of them. What are the threats and opportunities posed by AI? What changes do they need to make to their strategy, operations or culture in order to stay relevant? And how does one handle this radical transformation of work while keeping employees engaged?

One estimate says that AI will unlock $15.7 trillion in productivity gains by 2030. However, companies have to take deliberate steps to adapt their organizations in order to capitalize on the potential that AI offers.   

With Productivity Commodified, Creativity Becomes More Salient

AI is enabling productivity gains that are orders of magnitude higher than what humans can accomplish. AI can spit out code snippets or new graphics in a matter of seconds, compared to hours it takes a person. 

Competing with AI on skills where it can outperform people is pointless. Any business that doesn’t adopt AI productivity and employs people to do the same tasks, will soon fail. Instead, the most promising approach is to harmoniously coexist with AI where people focus on tasks that are uniquely human, like creativity, while AI handles more routine tasks. 

Focusing on innovation is important from a differentiation perspective too. When everyone uses AI to help improve their productivity, productivity becomes commodified. The only way then, to stand out among your peers is by using creativity – finding new ways to use the latest technology or creating new capabilities that delight customers. As an example, the low-code/no-code platforms are allowing people from different industries, including health and finance, to create their own applications and improve their workflows. 

Creativity is where humans have a distinct advantage. AI hasn’t performed well when it comes to creative tasks because creativity, by definition, requires production of ideas that don’t exist before. Even though AI is transforming the creative industry, the nature of its output is only superficially creative. Current set of AI tools are only good at rearranging existing elements in different ways to give an illusion of creativity. When prompted to create something that is truly original, tools like ChatGPT tend to fail. 

Leaders need to recognize that human creativity is now the differentiator for their business and they need to invest heavily in fostering internal innovation. 

Laying Off People Is The Wrong Strategy

Staying ahead of the AI acceleration requires proactively redesigning the boundary between human and AI work. This is a continually shifting line and businesses have to be agile when it comes to rearranging work for maximum productivity and innovation. 

As a business leader, it might be tempting to think that with AI taking over more and more tasks, your company needs fewer people to do the work that was being done before. This mindset can be dangerous for a company’s competitive advantage because it does not tap into the productivity gains that any new disruptive technology like AI unleashes. Barring a few domains (like some service industries), for any industry that has scalability potential and demands continuous innovation, reducing overall headcount will slow progress and almost certainly backfire in the long run. 

To see why this approach is damaging in the long run, let’s assume that your work requires some level of innovation to stay competitive in your market and therefore needs humans-in-the-loop. As more work starts shifting towards AI, it seems reasonable to reduce headcount in order to retain the same level of productivity for a much smaller cost. 

However, after a certain level of reduction, people become the critical resource in an organization. Even assuming that people are only doing tasks related to creativity that AI doesn’t handle well,  the organization becomes limited by the level of innovation it can harness from its employee base. As the AI capability expands even more (at a much faster rate than people upskilling), the extra capacity remains untapped due to limited people resources. After a short-term boost in productivity and lower costs, organizations get hamstrung in their ability to produce outsized innovations that can put them ahead of their competitors. 

Instead of simply reducing headcount to maintain the current levels of productivity, leaders need to take a long-term approach to talent management. Retaining and hiring people with the right skill set for innovation will help organizations take advantage of growing AI capabilities and provide higher levels of productivity and innovation compared to their peers. 

Engineering A High Performance Culture

A natural consequence of the increased productivity offered by AI, is that it allows more complex work to be possible. Complex work is high on both innovation and productivity, and the more complex the work, the more you need to tap into the intelligence and expertise of others. However, handling complexity, when humans are a critical part of the loop, is tricky. Groups can behave as intelligent swarms but they can also arrive at incredibly poor outcomes. How smartly a group behaves depends on the overall ability of individuals in the group as well as how independently they are allowed to think. Relatively smart people when thinking without the influence of others, cancel out each other’s errors and biases leading to much better decision making and problem solving that wouldn’t be possible at an individual level.

The single most important thing that organizations can do, in addition to hiring good talent, is to set up processes, tools and norms that allow people to contribute ideas and participate in decision making in independent ways. Without the right performance-focused culture, organizations will find it hard to capitalize on the higher complexity demands. 

Leadership Takeaways

Any transformative technology, by its very definition, radically changes the way people do things and opens up new markets and opportunities. How companies respond to the new environment determines how well they can capitalize new opportunities. 

Compared to disruptive technologies of the past, AI places new challenges due to the rapid pace of development. Companies need an agile approach to managing people and work. 

  • Innovation Management is key: With high productivity gains that benefit everyone, It’s inevitable that people-work will shift towards creativity. How well companies harness employee innovation will determine how they differentiate in the market and tap into the value that AI provides.
  • Hire for the long-term: Automation eliminates some jobs but typically creates many more new ones. A Deloitte study found that automation in the UK created over 4x more jobs than it eliminated that on average paid more. Instead of laying off people in order to boost short-term efficiency metrics, companies should focus on retaining and retraining employees to handle the higher workloads that are bound to come. 
  • Build a culture of performance: With AI taking over time-consuming tasks, more complex work that involves higher innovation and productivity will now be possible. Complex problem solving relies on individuals with different expertise to work together towards a common goal. Leaders will need to create a high-performance culture that incentivizes both individual expertise and swarm intelligence. 
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Why Managing Innovation Is Key To Organizational Success

Analogies are tricky. They can suddenly illuminate a hidden facet and bring to light new insights, new ideas and new solutions. But just as often, they push you to think in a single direction, that when taken too far, causes more harm than good. 

One analogy that is surfacing again is that of wartime and peacetime CEOs. Now that many companies are facing existential crises, there are calls to move away from a peacetime mode and bring a more wartime mentality to doing business. But this is a false choice. Companies are not at war (yes, they have to compete but that’s different) or at peace – instead, their primary task is to continuously innovate and stay relevant. 

The reason that this particular analogy is dangerous is that a warlike approach – think of a general directing orders, employing (mostly) sticks and (sometimes) carrots to get his troops to perform – is the exact opposite of what is needed for true innovation to take place on a regular basis. To be fair, the analogy works at times because it has some truth to it. But every analogy has its limitations. A wartime approach works in narrow situations for short periods of time, but making it the default mode of operation in an environment where high levels of innovation are the only solution to stay alive can only cause long-term damage.

The Two Beasts: Innovation and Productivity

Every company has to do innovative work as well as routine or productive work. For example, deciding what new product or feature to release that is both novel and solves an important customer problem is innovative work. With the innovative idea finalized and the workability of the novel aspect determined, implementing it becomes more of a routine/productive work. Even though both kinds of work require problem solving (barring some of the most mundane routine tasks), they are of very different nature. While most people think of creativity as a fun, relaxing activity, in reality it is more cognitively demanding compared to critical or logical thinking.  

Productive work is much more linear and therefore predictable in nature, making it easier to plan and track. In contrast, innovative work is much more non-linear, it requires multiple iterations and carries larger risks. While project management tools can handle productive work well, innovation management needs a very different type of tool to capture its underlying risk and complexity. 

Incentives also work differently for the two kinds of work. External motivators (like monetary rewards or threat of layoffs) can improve performance of routine tasks but can backfire for creative work in some situations. When people are nudged to adopt an extrinsic orientation or expect to be rewarded for a task, they produce less creative work. 

And finally, even though both kinds of work require collaboration, the nature of collaboration is different. For routine/productive work, collaboration is primarily coordination of tasks – tracking individual tasks and dependencies between different tasks and people. Creative work, on the other hand, requires collaboration of ideas – different ideas clashing together to create something much more interesting. In other words, even the day to day work that people engage in, including the kinds of things people talk about in meetings, is vastly different for innovative and productive work. 

Innovative and productive work are both equally essential for a company. However, they are two very different beasts and the real challenge comes in managing both without compromising either. 

When productivity metrics are applied to innovative work, as often happens in the corporate environment, a natural and predictable consequence is for innovation levels to drop. If you are tasked with a feature and your success is tracked through completion deadlines, there really is no option for you other than to pick the safest and simplest implementation that allows you to show up “green” on the project dashboard. Risk averseness starts to creep in at every level from padding estimates to reducing complexity, and innovation, which by its very nature involves high risk, is suppressed. 

Or, when companies implement some form of “rank and yank” system, idea collaboration among employees drops. If you are forced to compete with the person sitting next to you, it doesn’t make sense for you to collaborate and share credit if you believe you have a winning idea. An idea that could have bloomed with new perspectives, remains stunted. People become more focused on protecting their turf, leading to more politics and again, less innovation. 

If you think that hiring the smartest people would circumvent this problem, you would be mistaken. The underlying currents of human motivation and self-preservation are far too strong for things to go any other way. Most people eventually learn to adapt to the system and those who can’t or don’t want to, get frustrated and leave. 

The challenge for any company is to manage both these processes successfully, and neither the wartime nor peacetime approach is adequate. 

How The Manhattan Project Managed Radical Innovation

One of the most interesting examples to have successfully tamed the two beasts to produce groundbreaking work was the Manhattan Project, which produced the first nuclear weapons. To understand how impressive this accomplishment was consider the following:  

  • The Manhattan project started modestly but grew to about 130,000 people in just a few  years, spread across multiple locations.
  • The team faced huge scientific and technical challenges – prior research on producing fissionable was very preliminary and had many gaps. Processes that were eventually adopted, either did not exist before the project or had never been used with radioactive materials before. 
  • Fears that the German nuclear research team would produce the first atomic bomb created a strong time pressure. Fundamental research, and the design and building of the plant had to be done concurrently, something that had never been done before. 

These and other issues placed considerable management stress from scaling the team to shipping a challenging product on an accelerated schedule. So, how did the Manhattan project pull off such a feat?

To start, Leslie Richard Groves, the general in charge of the overall project, had the foresight to recognize that the typical command-and-control management style would not yield the required levels of innovation and there was no existing playbook to go by. Despite being in the middle of an actual war, he took a decidedly “un-wartime” approach to management. He first hired J. Robert Oppenheimer, a well respected theoretical physicist at UC Berkeley, as his counterpart to work with the scientists and researchers. Effectively working as co-CEOs, they found new ways of managing people and work in order to accomplish their audacious goals. 

And they faced massive challenges from the get go. For example, when Groves asked scientists how much fissionable material would be needed for each bomb, he expected an estimate within 20%-50% and was horrified when he got a factor of ten! He quipped, “My position could well be compared with that of a caterer who is told he must be prepared to serve anywhere between ten and a thousand guests. But after extensive discussion of this point, I concluded that it simply was not possible then to arrive at a more precise answer.”

Faced with such high levels of uncertainties, Oppenheimer and Groves realized that they will have to pursue multiple solutions at the same time. Given the time constraint, they decided to explore all options in parallel both for producing fissionable material and for gun design. They spun off multiple teams to explore different alternatives, and plant design proceeded under the assumption that any or all of these approaches would be needed. As research progressed, Oppenheimer realized that some of the processes could be combined for higher efficiency, a lucky turn of events that wouldn’t have happened without the parallel approach! 

Managing Innovation

Groves and Oppenheimer showed that it is possible to create an environment where there is a balance between urgency and innovation, structure and flexibility, hierarchy and egalitarianism. 

Innovation is fundamentally about managing uncertainty and risk, and the more radical the innovation the higher the uncertainty to manage. Using a top-down, highly directive leadership style doesn’t work because it increases risk (one person’s judgment is more error prone for complex problems) and reduces intrinsic motivation among employees which is essential for innovative problem solving. 

Despite the fact that Groves and Oppenheimer were both highly competent in their own ways, they did not push down any directive that would interfere with problem solving. Instead they did the opposite – by really listening to the people doing the work, they were able to clearly understand the inherent limitations in the project. They did several other things that led to  a creative climate. For example, Oppenheimer insisted early on that scientists have full access to the compound so they could observe all aspects of the project, leading to free flow of ideas. He was also known to take good care of his people, so compensations were generous and equitable. Teams also didn’t shy away from conflicts – vigorous debates were common but they were focused on problem solving. Intentionally or intuitively, they made sure that none of the factors that harm creativity and problem solving were accidentally introduced in their management approach. By enabling the scientists and engineers, they allowed more creative solutions to emerge for the myriad of challenges that kept popping up.  

Groves and Oppenheimer were successful, not by following any pre-existing playbook, but by systematically removing any barriers that came in the way of innovation. 

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Address Unfinished Learning Using Creativity And Arts  

During the Covid-19 pandemic, students all over the world faced one of the largest disruptors of education in history. School closures across more than 190 countries impacted the routine and learning outcomes of 1.6 Billion students. By August 2020, 93% of households in the US reported the use of remote learning. Despite things returning to normal for over a year, we continue to see the ripple effect of this massive disruption. 

A number of organizations and researchers have tried to estimate the loss in learning due to the pandemic. A report by Stanford University estimated that during Spring 2020, students lost between 57 to 183 days of learning in Reading and from 136 to 232 days of learning in Math. A study in the Netherlands, which had a relatively lockdown period and is known for its equitable and well-funded schools, showed that students made little to no progress in learning from home. 

One of the challenges with the analyses on learning loss thus far, is that they focus narrowly on a few subjects, primarily math and reading. Finding an effective set of solutions to address unfinished learning is not an easy task. However, there is a wrong solution – solely relying on standardized tests. A myopic focus on test scores leads to sub-par teaching (“teaching to the test”) and pushes out more holistic interventions. We have seen this play out before when the No Child Left Behind Act was implemented in 2002. The Act converted testing as an informational mechanism to an accountability mechanism, changing teaching patterns inside classrooms. Teachers spent less time on subjects that were not being tested, like the arts, and spent less time on open-ended exploratory work like project based learning. 

What we need are more holistic approaches that build student enthusiasm, curiosity and engagement towards learning. Here are three strategies to incorporate when designing intervention programs to address unfinished learning, without falling into the trap of standardized testing. 

Teach For Creativity

Incorporating creative thinking into regular subjects, like math, reading or science, deepens learning. Paradoxically, this approach ends up improving standardized test scores despite test results not being an explicit goal. Creative thinking encourages students to think about a concept from multiple perspectives, question assumptions they might be making and synthesize solutions from multiple strands. These cognitive processes help students deepen their understanding of the subject matter and retain information for longer. More importantly, they help students become better thinkers and problem solvers in the long run. 

For teachers to incorporate creativity into the classroom, they need to be mindful of the distinction between “teaching creatively” and “teaching for creativity.” When educators find a clever way to introduce a concept in the classroom, say using Bitmoji classrooms, they are teaching creatively. Their own creativity is at play in designing the lesson plan for the concept. This by itself is a good thing if it makes the lesson more engaging and helps students learn the concept more easily. However, “teaching for creativity” goes a step further – it allows students to build and exercise their own creativity. 

Well designed project-based learning modules are one way to encourage students to be more creative. However, it’s not hard to incorporate creative thinking in smaller doses by substituting a couple of traditional assignment questions with more creative ones. With this approach neither the students or the teachers are burdened with extra work. Key elements to keep in mind when incorporating creativity, in big or small doses, are:

  • Open-ended problems where multiple solutions are valid.
  • Problems are designed so that one more creative cognitive processes, like associative or analogical thinking, are engaged. 
  • An ability to share their work with their peers and learn from them.  

Integrate Arts

High quality arts programs, especially visual arts and music, have a strong correlation with academic success. Arts provides an avenue for students to build confidence to try new things in a safe environment. Incorporating arts increases both children’s self-efficacy and original thinking, according to a research study. As the authors noted, “Self-efficacious children believe they can be agents in creating their own futures and are more optimistic about what the world has in store for them.”

A randomized controlled trial with over ten thousand K-12 students in Texas, found that arts-learning experiences led to fewer disciplinary issues, higher student engagement and improved writing achievements. 

Reevaluate Evaluations

While standardized tests are a useful way to track student progress, they should be a small part of broader evaluations. Teachers should balance graded work with non-graded work that rely on constructive feedback. Quantitative scores orient students away from their own intrinsic motivation to extrinsic motivation, which leads to higher anxiety and lower interest in the subject matter. Providing opportunities to students to share their projects or other original work with their peers and get their feedback is another low-pressure way for students to learn from each other. Finally, teachers should also encourage frequent self-evaluations to build student capacity to critically analyze their own work. When students find their own areas of improvement, they are much more intrinsically motivated to improve and learn. 

The learning loss created by the pandemic is significant and real, but we need to approach it cautiously. Prioritizing standardized test scores instead of more holistic approaches can lead to disastrous consequences in the long-term. Instead we need to double down on tried and tested methods – incorporating creative thinking and arts, and toning down the performance pressure.  

This article first appeared on edCircuit.