The complexity of algorithms is an important aspect of computer science, and students have a hard time relating theory to practice. In spite of the fact that gamification is a good and entertaining way of facilitating learning, the traditional methods lack an interactive and comparative example of how different inputs are reacted to by algorithms. The research describes a game-based platform, which in this case is called Battlefield of Algorithms, where two of the users play each other by choosing algorithms to perform a task, such as sorting. The system not only measures time and memory consumption but also executes the two algorithms on personalized inputs and declares the winner and description. Students are not bored and can understand more about complexity because of this handy real-time comparison. Early evidence reveals increased motivation and understanding of performance algorithms, which makes theoretical concepts more important and attractive.

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Improving Algorithmic Grasping Using Sustainable and Explainable Gamified Performance Metrics

  • Siddhant Yenpure,
  • Advait Mhalungekar,
  • Purva Ratnaparkhi,
  • Gayatri Bhurguda,
  • Chetan Channa,
  • Parikshit N. Mahalle

摘要

The complexity of algorithms is an important aspect of computer science, and students have a hard time relating theory to practice. In spite of the fact that gamification is a good and entertaining way of facilitating learning, the traditional methods lack an interactive and comparative example of how different inputs are reacted to by algorithms. The research describes a game-based platform, which in this case is called Battlefield of Algorithms, where two of the users play each other by choosing algorithms to perform a task, such as sorting. The system not only measures time and memory consumption but also executes the two algorithms on personalized inputs and declares the winner and description. Students are not bored and can understand more about complexity because of this handy real-time comparison. Early evidence reveals increased motivation and understanding of performance algorithms, which makes theoretical concepts more important and attractive.