Refined metric interpretation in natural language for educational videogames using fuzzy logic

Published: 9 January 2023| Version 1 | DOI: 10.17632/cwjj98mm3g.1
Contributor:
Nayeth Solorzano

Description

With digital gaming's increasing popularity, Educational Digital Games (EDG) are being more commonly used to complement children's early education. Controlled EDG provides educators a way to observe progress. Many existing applications fail to generate automatic data collection to provide reliable information for feedback on academic aspects needed. This paper describes the usefulness of MIDI-AM, a series of EDG, to link a dashboard, including informative outcomes about use and Playability. It explains how rules of fuzzy logic and Natural Language (NL) can provide consumable feedback. The research objective provides a new component in a longitudinal study to identify a more efficient process for developing and implementing a module to refine the dashboard metrics and outcomes of MIDI-AM EDG. The initial platform was redundant and created inconsistent results. Using Artificial Intelligence (AI) generates valuable information to refine the process of generating feedback reports using detailed data interpretations in NL.

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