Coding & Robotics Sample

Learning to code often involves computational thinking—a key component in developing good coding practices. 

• Abstraction and Problem-Solving: Coding requires understanding complex systems by breaking them down into smaller, manageable pieces. This is at the heart of computational thinking, which includes:

  • Decomposition: Breaking a problem into smaller parts.

  • Pattern Recognition: Identifying patterns that can be reused.

  • Abstraction: Filtering out unnecessary details to focus on the key elements of a problem.

  • Algorithm Design: Creating step-by-step solutions or rules to solve problems.

For beginners, thinking in such an abstract and logical way is not intuitive. Most people are used to dealing with real-world, concrete things, so shifting to the abstract nature of programming takes time and practice.

• Syntax and Language Rules: Each programming language has specific rules that must be followed exactly. Fortunately, Scratch is a Block-Based language that avoids most text-based syntax frustrations.

• Debugging and Error Handling: Beginners often struggle with debugging, which is identifying and fixing code errors. It involves a different mindset—thinking like a machine to determine where the code is failing. This can feel overwhelming without a solid understanding of how the code flows and interacts.

• Logical Thinking and Patience: Writing code demands a certain way of thinking that isn't always natural, especially when solving complex problems. Learning to think logically and break down problems methodically requires patience, persistence, and lots of practice.

• Iterative Nature of Learning: Learning to code is not a linear process. Often, beginners have to revisit concepts and refine their understanding multiple times. This iterative learning process by trial and error can be hard and demotivating.

Teaching programming is challenging because it involves abstract thinking, problem-solving, and computational thinking, which are new to many learners. Students must grasp complex concepts like algorithms, syntax, and debugging, which can be frustrating, especially when small mistakes cause big issues. Different learning styles and paces add to the difficulty, as some students may struggle more than others. Translating abstract concepts into engaging, real-world exercises is tough, and students often find it hard to transfer knowledge between problems. Additionally, programming involves frequent trial and error, which requires patience and persistence that many beginners may find discouraging.

Any fool can write code that a computer can understand. Good programmers write code that humans can understand. ― Martin Fowler

Bad Code refers to programming that works but could be more efficient, easier to understand, and easier to maintain. Here are common causes:

• Code that is difficult for others to understand. This might result from poor variable names, lack of comments, or overly complex logic.

• Good code is organized into reusable, independent pieces (functions, modules). Bad code often lacks this structure, which makes it difficult to maintain and extend.

• Code that is unnecessarily slow caused by poor, inefficient logic.

• Good code follows conventions for the language, such as good variable names and correct coding structures. Ignoring these conventions results in code that's hard to follow or debug.

Why Bad Code Can Be Harmful

Bad code might work in the short term but leads to long-term problems. It is harder to debug, extend, or maintain, which can lead to costly errors, wasted time, and frustration. Over time, poor-quality code increases the technical debt of a project, making future development slower and more difficult. Think of Y2K!