Validation of a Card Game-Based Reinforcement Learning Methodology for Elementary Students to Understand Core Tamper-Prevention Principles in Blockchain Structures

Abstract

This study proposes a gamification-based educational model that integrates blockchain concepts and reinforcement learning (RL) principles for elementary students. While blockchain education is often abstract and unsuitable for younger learners, the proposed card game-based approach allows students to experience hash functions, consensus algorithms, and distributed ledgers through interactive activities. Instructional design followed the Dick and Carey model, and the MDA framework was applied to align game mechanics with cognitive, emotional, and social objectives. RL mechanisms such as exploration–exploitation balance and reward shaping were embedded to sustain engagement and motivation. The model’s effectiveness was evaluated through expert review involving four technology specialists and nine elementary school teachers. Results showed consistently positive ratings across five metrics (Innovation, Effectiveness, Applicability, Motivation, Efficiency), with averages above 3.8 on a 5-point scale. Particularly high scores were recorded for Innovation (M=4.14) and Efficiency (M=4.09). These findings indicate that the model is both novel and practical, offering a promising approach to making abstract technical concepts accessible at the elementary level.