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CS297 ProposalAI Quantification of Language Puzzle to Language Learning Generalization.Harita Shroff (harita.shroff@gmail.com) Advisor: Dr. Chris Pollett Description: Online language learning applications have gamified the objective by providing multiple ways/games to learn a new language. Some of the ways include rearranging words in the foreign language sentence, filling in the blanks, providing flashcards and many more. Primarily this research focuses on quantifying the effectiveness of these games in learning a new language. Secondarily my goal for this project is to measure the impact of some exercises on completing other activities on these language learning platforms. Schedule:
Deliverables: The full project will be done when CS298 is completed. The following will be done by the end of CS297: 1. Provide Feed-Forward Neural Network program using tensorflow to identify Gujarati language digits. 2. Provide Convolutional Neural Networks program using tensorflow to identify Gujarati language digits. 3. Implement the word2vec program to find word embedding for Gujarati words. 4. Data collection. 5. CS297 final report. References: [E. Charniak 2019] “Introduction to Deep Learning”. Eugene Charniak. The MIT Press. 2019. [J. Grego 2012] “Duolingo Effectiveness Study”. John Grego, Roumen Vesselinov. 2012. [D. Huynh 2018] “An Assessment of Game Elements in Language-Learning Platform Duolingo”. Duy Huynh, Long Zuo, Hiroyuki Iida. 2018. |