First Results from Using Game Refinement Measure and Learning Coefficient in Scrabble

November 07, 2017 Β· Declared Dead Β· πŸ› arXiv.org

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Authors Kananat Suwanviwatana, Hiroyuki Iida arXiv ID 1711.03580 Category cs.AI: Artificial Intelligence Citations 2 Venue arXiv.org Last Checked 4 months ago
Abstract
This paper explores the entertainment experience and learning experience in Scrabble. It proposes a new measure from the educational point of view, which we call learning coefficient, based on the balance between the learner's skill and the challenge in Scrabble. Scrabble variants, generated using different size of board and dictionary, are analyzed with two measures of game refinement and learning coefficient. The results show that 13x13 Scrabble yields the best entertainment experience and 15x15 (standard) Scrabble with 4% of original dictionary size yields the most effective environment for language learners. Moreover, 15x15 Scrabble with 10% of original dictionary size has a good balance between entertainment and learning experience.
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