Why Learning is Hard
The Science Behind Frustration and Progress
My Data for Justice course at Williams College introduces students to data science through a social justice lens. Many of my students are encountering the frustrations that come with learning to write computer code for the first time. Since the course is designed for those without prior experience in programming or statistics, I wanted to address their concerns by explaining the science behind why this struggle is both natural and beneficial. In this post, I share some insights on the learning process to help students understand that this frustration is a crucial part of mastering new skills. Perhaps you want to share it with your own students!
Learning new, complex skills such as computer coding and data science in R can be frustrating, but this experience is a natural, unavoidable, and even beneficial part of the learning process. Scientific research in neuroscience, psychology, and education supports the idea that challenges, effort, and persistence play essential roles in building lasting knowledge and expertise. Let’s break down why this happens and how it’s grounded in scientific principles.
Neuroplasticity: The Brain’s Ability to Learn
When we learn something new, such as coding, our brain undergoes a process known as neuroplasticity, which refers to its ability to reorganize and form new neural connections. Each new concept or skill creates neural pathways, which initially are weak but become stronger with repetition and practice. This is why learning takes time — it’s literally the process of rewiring the brain.
Neuroplasticity research shows that with repeated exposure, the brain adapts to new tasks by making these connections more efficient, eventually leading to faster recall and application of skills. Intense practice over time causes measurable changes in the brain’s structure and function, particularly in areas related to motor skills, memory, and problem-solving (Draganski et al., 2004) [cited 3,000+ times].
Cognitive Load Theory: The Limits of Mental Capacity
When learning new material, we often feel overwhelmed. This is explained by cognitive load theory, which states that the brain has a limited capacity for processing new information. When we first encounter complex material, like R programming, we’re juggling multiple layers of unfamiliar information — syntax, logic, functions — leading to a high cognitive load (Sweller et al., 2011) [cited 5,000+ times].
Initially, everything feels difficult because there’s a lot to hold in working memory, but over time, as we become more familiar with basic concepts, we free up cognitive resources to handle more complex tasks. The key is repeated exposure and practice, which reduces the cognitive load over time.
The Role of Productive Struggle: Why Frustration is Good
Feeling frustrated while learning is not only natural — it’s beneficial. Research in cognitive psychology shows that struggle leads to deeper learning. When learners grapple with difficult tasks, they are more likely to engage in deep cognitive processes, which result in stronger, more durable memories and skills (Bjork, 1994) [cited 2,000+ times].
This idea is captured in the concept of desirable difficulties. When learning is effortful and slow, it actually enhances retention and transfer of knowledge, meaning you’ll be better able to apply what you’ve learned to new coding challenges, different types of projects, or even other programming languages. By encountering and overcoming obstacles, you build the cognitive resilience needed for long-term mastery.
Growth Mindset: Embracing Challenges
Carol Dweck’s research on growth mindset emphasizes the belief that abilities can be developed through effort, which is crucial for persisting through difficult tasks like coding. Students with a growth mindset are more likely to view challenges as opportunities for growth rather than as obstacles, making them more resilient in the face of failure (Dweck, 2006) [cited 20,000+ times]. By embracing this mindset, you can recognize frustration as a positive sign that your brain is adapting and improving with each new challenge.
The Power of Practice: Building Expertise
Practice is critical in mastering any skill, and this applies especially to technical skills like coding. Research on expertise suggests that it takes deliberate practice — a focused and intentional effort to improve over time — to develop deep proficiency (Ericsson et al., 1993). Malcolm Gladwell popularized the 10,000-hour rule, which, although not a precise formula, reflects the idea that sustained, focused practice is key to becoming an expert in any field. It’s important to remember that it’s not just about the hours spent coding, but about how you spend those hours. Deliberate practice involves setting specific goals, seeking feedback, and focusing intently on areas for improvement.
Conclusion: Learning is Hard and Takes Time, and Those are Good Things
The frustration you are feeling is an essential part of the learning process. Science shows that the brain requires time to adapt to new tasks, and challenges lead to stronger, more lasting learning. I hope you will come to view your difficulties as signs of progress rather than failure. With persistence, your frustration will give way to mastery.
References
- Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In Metacognition: Knowing about knowing (pp. 185–205). MIT Press.
- Draganski, B., Gaser, C., Busch, V., Schuierer, G., Bogdahn, U., & May, A. (2004). Neuroplasticity: Changes in grey matter induced by training. Nature, 427(6972), 311–312.
- Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
- Ericsson, K. A., Krampe, R. T., & Tesch-Römer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), 363–406.
- Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer Science & Business Media.
