Active Learning Strategies

Trying to come up with an answer rather than having it presented to you, or trying to solve a problem before being shown the solution, leads to better learning and longer retention of the correct answer or solution, even when your attempted response is wrong, so long as corrective feedback is provided.

Priming the Mind for Learning

When you’re asked to struggle with solving a problem before being shown how to solve it, the subsequent solution is better learned and more durably remembered.

Unsuccessful attempts to solve a problem encourage deep processing of the answer when it is later supplied, creating fertile ground for its encoding, in a way that simply reading the answer cannot. It’s better to solve a problem than to memorize a solution. It’s better to attempt a solution and supply the incorrect answer than not to make the attempt

• What are the key ideas?
• What are some examples?
• How do these relate to what I already know?
• What went well?
• What could have gone better?
• What might I need to learn for better mastery, or what strategies might I use the next time to get better results?

Rephrasing Key Ideas

Express the main ideas in their own words and relate them to other concepts. Restating concepts in your own words and elaborating on the concepts by generating examples of them. Asking someone to try to solve a problem before being shown how to do it, produce stronger learning and retention of the correct information than more passive learning strategies, provided there is corrective feedback.

See mistakes not as failures but as lessons and turning points along the path to mastery

Embracing Challenges for Deeper Understanding and Better Retention

In active learning, the learner engages deeply with the material rather than passively receiving it. Active learning strategies include attempting problem-solving before being shown the solution, elaboration, self-explanation, and self-questioning.

For instance, if you’re learning how to code, rather than simply watching a tutorial or reading code, you might first try to write a program or solve a coding problem on your own. Even if you make mistakes or can’t come up with a solution, the act of struggling with the problem prepares your brain for learning. When you later review the correct code or solution, you’re likely to understand it more deeply and remember it more effectively. This process is also related to the concept of “productive failure.”

Rephrasing key ideas and restating concepts in your own words is another active learning strategy. For example, if you’re studying the concept of gravity in physics, you might say:

Gravity is the force that pulls two objects toward each other. It’s why when I jump, I come back down to the ground instead of floating off into space.

Elaboration involves going beyond the basic idea and connecting it to other concepts you know. Using the gravity example, you might elaborate by saying:

That’s similar to how magnets attract each other, but with gravity, it’s about mass, not magnetic fields. And every object that has mass has gravity, even though we don’t notice it unless the object is really big, like a planet.

Self-questioning is a strategy of actively checking your understanding and identifying areas of confusion. After studying a topic, you might ask yourself:

• Do I understand how gravity works?
• Can I explain it in my own words?
• What are the formulas involved?
• Do I understand how they work?

Viewing mistakes as learning opportunities is another key idea. When you make an error in problem-solving, it’s not a failure, but a chance to improve. For example, if you make a mistake in a math problem, reviewing where you went wrong can help you understand the correct process better.

All these strategies are more engaging and require deeper cognitive processing than passive reading or listening, and they’re often more effective for learning and retention. They’re examples of metacognitive strategies – strategies that involve thinking about your own thinking and learning. They help learners become more independent and effective learners, which is a valuable skill for lifelong learning.

Computer Science Problems

The phenomenon of enhanced understanding and retention through active problem-solving is particularly evident in the field of Computer Science, a domain that thrives on problem-solving and abstract thinking. Here, merely reading about an algorithm or data structure does not equate to grasping its essence. It’s when students engage in coding, debugging, and optimizing algorithms that they truly internalize the concepts and their applications.

For instance, consider the computer science concept of Binary Search. You may read about it, understand its logarithmic time complexity, and recognize its efficiency. But the profound understanding comes when you attempt to code the Binary Search algorithm, apply it to a set of data, and observe its behavior. This active process, where you deal with real data, adapt the algorithm accordingly, and possibly encounter and correct errors, etches the concept more deeply in your mind than passive reading ever could.

Let’s take an example where you’re asked to write an algorithm to find a specific data element in a sorted array. Even if your initial attempts fail, the process forces you to think deeply about the problem space, the properties of a sorted array, and how these properties can be leveraged to locate the data. When you finally come across the Binary Search algorithm solution, it will seem less like an abstract concept and more like an elegant answer to a problem you’ve wrestled with. Your struggle with the problem builds a fertile mental framework where the concept of Binary Search can firmly take root.

To solidify the learning, a student could engage in self-questioning techniques like:

• How is Binary Search different from Linear Search?
• In what scenarios would Binary Search not be appropriate?
• How can I adapt Binary Search for a different problem?

They can also engage in practices such as paraphrasing the concept of Binary Search in their own words, or generating different examples where Binary Search could be applied.

Moreover, embracing failures, or seeing them as part of the learning process rather than a setback, is crucial. When your algorithm doesn’t work as expected, it isn’t a failure; it’s an opportunity to understand the problem and the algorithm more deeply. By rectifying your mistake, you’re more likely to remember the correct approach in the future, fostering a growth-oriented mindset.

In conclusion, active learning strategies such as attempting to solve problems before being shown the solution, self-questioning, paraphrasing, and learning from mistakes can lead to more robust and long-lasting learning in the field of Computer Science. This approach not only develops technical proficiency but also cultivates essential problem-solving and critical thinking skills, equipping students to tackle real-world computing challenges.

Metacognitive Strategies

Metacognition, or thinking about thinking, refers to the ability to control and reflect on one’s own cognitive processes. Metacognitive strategies can significantly enhance the learning of complex subjects such as Computer Science. Here are some metacognitive strategies that can be used for this purpose:

Self-questioning:

After learning a new concept, students should ask themselves questions about it. For instance, after learning about an algorithm, they could ask:

• What is the problem this algorithm is solving?
• What would happen if I changed this part of the algorithm?
• In what scenarios is this algorithm most effective?

Active Reflection

Spend time reflecting on what you have learned.

• How does a new concept fit in with what you already know?
• How can you apply it in different contexts?

This helps in creating mental connections, aiding long-term retention and understanding.

Prediction:

Before starting a new topic, make predictions about what it will involve based on the title or introductory material. This prepares your mind for new information and helps you connect it with prior knowledge.

Summarization and Paraphrasing:

After studying a topic, try to summarize it in your own words. This demonstrates understanding and helps to consolidate knowledge.

Self-explanation:

Try to explain the concept to someone else. This could be a classmate, a friend, or even an imaginary audience. The process of explaining often reveals areas of confusion that need further study.

Self-testing:

Regularly test yourself on the material you’re learning. This could involve doing practice problems, using flashcards, or writing summaries from memory. Self-testing is a powerful metacognitive strategy known as retrieval practice, which significantly boosts long-term retention.

Planning and Organizing:

For larger tasks, such as coding projects, planning and organizing your approach is essential.

• What steps will you take? What resources will you need?
• How much time will each step require?

Evaluation:

After completing a task, take the time to evaluate how it went.

• What worked well?
• What did not go well?
• How can you improve for next time?

Mindset:

Maintain a growth mindset. Embrace challenges and see mistakes as opportunities for learning rather than as failures. This mindset encourages persistence and effort, which are crucial for mastering difficult subjects.

By consciously applying these metacognitive strategies, students can enhance their understanding and retention of complex computer science concepts, improving their learning efficiency and effectiveness.