Kirjain-äännevastaavuuden oppimisen mallinnus bayesilaisella menetelmällä
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2019Copyright
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Tämä tutkielma kuvaa erään tavan mallintaa kirjain-äännevastaavuuksien oppimista. Malli on luotu Ekapeliä varten käyttäen apuna pelistä kerättyä dataa. Mallin toteutuksessa käytettiin bayesilaisen tilastotieteen menetelmiä. Tavoitteena oli käyttää mallia uuden adaptaation luomiseen. Malli ei kuitenkaan sopinut suoraan pelin adaptaatiossa käytettäväksi laskennallisista ongelmista johtuen. Mallin avulla haluttiin myös visualisoida pelaajan osaamista ja kuvaajien avulla voidaankin helposti näyttää kokonaiskuva kirjainten osaamisesta. This thesis describes a bayesian model for learning letter-sound correspondences. The model was created for Ekapeli using data from the game. The model was created using bayesian methods. Purpose of the model was to create a new adaptation for Ekapeli. Because of high computational time, the model doesn't suite for an adaptation without simplifications. Another goal for the model was to help visualize the player's learning. The model suited well for visualizing the player's knowledge of the letter-sound correspondences.
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