What are the most common mistakes made when teaching algorithms?

1 Lack of context

One of the most common mistakes made when teaching algorithms is to present them in isolation, without explaining how they relate to real-world problems or applications. This can make algorithms seem abstract, boring, or irrelevant to the learners, and reduce their motivation and engagement. To avoid this mistake, it is important to provide context for each algorithm, such as why it was invented, what problem it solves, how it compares to other algorithms, and where it is used in practice. This can help learners appreciate the value and usefulness of algorithms, and spark their curiosity and interest.

2 Too much detail

Another common mistake made when teaching algorithms is to overwhelm the learners with too much detail, such as complex mathematical formulas, proofs, or pseudocode. This can make algorithms seem intimidating, confusing, or inaccessible to the learners, and distract them from the main idea or logic behind them. To avoid this mistake, it is important to simplify and explain the key concepts and steps of each algorithm, using plain language, examples, diagrams, or analogies. This can help learners grasp the essence and intuition of algorithms, and focus on the big picture rather than the minutiae.

4 No feedback

A fourth common mistake made when teaching algorithms is to provide insufficient or ineffective feedback, such as vague, delayed, or negative comments. This can make algorithms seem frustrating, discouraging, or unachievable to the learners, and prevent them from learning from their mistakes or improving their performance. To avoid this mistake, it is important to provide timely, specific, and constructive feedback, such as hints, tips, or suggestions. This can help learners identify and correct their errors, clarify their doubts or misconceptions, and enhance their confidence and competence.

6 No evaluation

A sixth common mistake made when teaching algorithms is to omit or neglect the evaluation aspect, and assume that the learners have mastered the algorithms without verifying or measuring their learning outcomes. This can make algorithms seem arbitrary, subjective, or unaccountable to the learners, and reduce their awareness and reflection on their progress or performance. To avoid this mistake, it is important to evaluate and assess the learners' understanding and application of each algorithm, such as using rubrics, criteria, or benchmarks. This can help learners gauge and demonstrate their proficiency and competency, and receive recognition and feedback for their achievements.