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This thesis examines the questions of
1. To become a Dual Language Education expert, researcher, or scholar, what does it take?
2. In what ways can a non-Native help Indigenous communities engaged in indigenous language revitalization and sustainment (ILRS)? What would they need to learn or know?
Some significant findings of my thesis work include
1. The strength, versatility, and challenges of the dual language education model in a national context
2. Culturally-sustaining pedagogy and strategies for adapting lessons to local culture
3. The centrality of tribal sovereignty and tribal control over the Indigenous language in order to grow and maintain an IRLS effort
4. Ways in which a non-Native can help an ILRS initiative
5. Respect for native communities’ right to say no to research
The purpose of this project is to create a useful tool for musicians that utilizes the harmonic content of their playing to recommend new, relevant chords to play. This is done by training various Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs) on the lead sheets of 100 different jazz standards. A total of 200 unique datasets were produced and tested, resulting in the prediction of nearly 51 million chords. A note-prediction accuracy of 82.1% and a chord-prediction accuracy of 34.5% were achieved across all datasets. Methods of data representation that were rooted in valid music theory frameworks were found to increase the efficacy of harmonic prediction by up to 6%. Optimal LSTM input sizes were also determined for each method of data representation.
My proposed project is an educational application that will seek to simplify the<br/>process of internalizing the chord symbols most commonly seen by those learning<br/>musical improvisation. The application will operate like a game, encouraging the<br/>user to identify chord tones within time limits and award points for successfully<br/>doing so.