Should a daily puzzle tell you how close you were, or only whether you were right?

Every daily puzzle must decide what to do after a wrong guess. The options are binary feedback, proximity feedback, or explanatory feedback. Binary feedback simply tells you if you are right or wrong, while proximity gives you an idea of how close you were to the correct answer. Explanatory feedback provides a reason for why your guess was wrong.

Research on feedback models suggests that task and process feedback, which includes binary and proximity feedback, is more helpful than person-centered feedback like explanations. In puzzles, binary feedback can feel harsh, while explanations can be too costly to produce and may give away the answer.

Designers often struggle with the trade-offs between these models. Proximity can turn a puzzle into an algorithm, making it fun but potentially unsatisfying for daily players. Explanations can be hard to create and may spoil the challenge.

A rubric can help designers score each model on factors like solvability, teaching value, cost, spoiler risk, and shareability. The author currently favors coarse proximity during gameplay, with explanations provided after the puzzle ends. This approach maintains tension while teaching players the underlying skill.

Ultimately, the choice of feedback model influences the difficulty, sharing potential, content cost, and fairness of the puzzle. Binary feedback is straightforward but may not provide much learning, while explanations offer the most teaching value but come with significant costs. Coarse proximity strikes a balance, providing direction without revealing the answer too quickly. The author invites readers to weigh in on their preferred model and to share examples of successful daily puzzles.