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A polyomino puzzle for arithmetic practice

机译:用于算术实践的多瘤难题

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摘要

Recent trends in mathematics education emphasize discovery learning over drill. This has proven to be a bad idea in some cases, for the simple reason that practice is required to learn basic arithmetic skills. Drills in arithmetic skills can be made interesting through gamification. This study proposes a family of puzzles that gamify arithmetic practice. The puzzles are designed with an evolutionary algorithm forming an instance of automatic content generation. Two methods of evolutionary puzzle design are presented and discussed. The first method used transformed the problem into an almost trivial optimization. The second algorithm was designed to avoid the flaws of the first and produced a huge variety of puzzles. A hardness measure, based on the difficulty experienced by the evolutionary puzzle generator, is employed. The hardness measure is tested on a large collection of puzzles produced with the evolutionary automatic content generation system. An initial assumption, that all the pieces in the puzzle must be used to achieve a maximum score, was shown to be incorrect in puzzles located via automatic search. Two classes of puzzle are defined: those where the optimal solution uses all pieces and those where the optimal solution fails to use at least one piece. The latter sort of puzzle were found to be far more common in the search space.
机译:最近的数学教育趋势强调钻探的发现学习。在某些情况下,这已被证明是一个糟糕的想法,因为实践需要学习基本算术技能。算术技能的钻孔可以通过游戏进行有趣。本研究提出了一种娱乐算术实践的谜题系列。难题设计有形成自动内容生成实例的进化算法。提出和讨论了两种进化拼图设计方法。第一种方法使用将问题转换为几乎琐碎的优化。第二种算法旨在避免第一个并产生各种各样的谜题。采用基于进化拼图发生器经历的难度的硬度测量。在用进化自动内容生成系统产生的大量谜题上测试硬度措施。初始假设,即必须使用拼图中的所有作品来实现最大分数,在通过自动搜索所在的谜题中显示出不正确。定义了两类拼图:最佳解决方案使用所有碎片的拼图以及最佳解决方案未使用至少一件的那些拼图。发现后一种拼图在搜索空间中更常见。

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