Algorithms play an essential role in the field of computer science. They are used in many applications including searching engines, social media and other web-based systems, as well as AI and cyber security systems. Still, there are many students and software engineers who struggle to understand algorithms despite their efforts. There is empirical evidence from the cognitive sciences and education psychology that shows working harder does not lead to learning. One should employ effective methods of learning which were proved to be successful during intensive scientific research to improve understanding and retention.
Why Is It So Difficult To Learn Algorithms?
Learning coding is more than just memorization of the solutions. It is more about critical thinking, identification of the problem, abstract thinking and its practical application. Many people study by simply watching tutorials and memorizing solutions to the problem. However, this approach does not work at all.
According to research on learning algorithms, learners benefit the most from active rather than passive approaches to learning. Thus, the most efficient way of algorithmic learning consists in retrieval and reflection.
The use of active recall for improving retention of material is well established. Active recall has been shown (through research) to facilitate retention of material better than rereading. It is not sufficient to simply sit down and reread Binary Search to retain it in your memory. Instead, try to write out how to implement it and its time complexity without looking at any notes.
Research corroborates that recall creates stronger neural pathways in the brain, which leads to improved storage of information. By consistently testing oneself with respect to algorithms, data structures and time complexity, learners will have higher levels of achievement.
Another research backed strategy that uses repeated intervals of time to study the same material is referred to as spaced repetition. As opposed to studying for the entire day while trying to memorize Dynamic Programming, do some reviewing on it after 2 days and then again 1 week later and continue this until several weeks later.
This phenomenon is known as the spacing effect and it has been proven that material that is reviewed over time is retained more effectively by an individual than material that is studied all at once.
To develop skills through practice and problem solving, you need to focus your practice sessions on identifying areas in which you need improvement, and regularly try new types of activities that you have never done before. Challenge yourself by asking progressively more challenging questions about your problems and coming up with new methods for solving them.
Next, once you have identified several possible options for solving a particular problem, assess how well each option would accomplish your goal and choose the best option. You can do this by brainstorming, creating a list of the pros and cons for each option, etc.
Finally, typically people practice each skill, such as arrays, trees, graphs, and dynamic programming, one at a time; however, studies show that alternating between types of problems (or interleaving them) will lead to increased learning.
Do not just focus on one type of problem, but rather switch your focus between all problem types frequently to improve your ability to solve problems in general. This is particularly important during programming interviews and will greatly assist in your ability to perform as a software engineer.
The Advantage of Learning from Mistakes
Mistakes are an integral part of the learning process. Studies show that reviewing your mistakes can help strengthen your grasp on the information and help you avoid experiencing further misunderstandings or misconceptions. Try creating a mistake log where you record your incorrect solutions and any cases you may have missed when you were nearing a correct answer.
Using this log could help you to turn your weaknesses into strengths.
Conclusion
Achieving mastery in algorithms is not just about being smarter than others. Scientific research shows that research studies have shown that how we learn affects how well we learn. You can gain greater results in developing your algorithms by using methods such as practicing retrieval methods, creating spaced retrieval intervals between intervals between learning sessions of new knowledge and developing skills through interspersed practice, as well as learning from your mistakes.
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