Using Learner Data from Duolingo to Detect Micro- and Macroscopic Granularity through Machine Learning Methods to Capture the Language Learning Journey

Modern language learning applications have become 'smarter' and 'intelligent' by including Artificial Intelligence (AI) and Machine Learning (ML) technologies to collect different kinds of data. This data can be used for analysis on a microscopic and/or macroscopic level to provi...

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Bibliographic Details
Published inResearch-publishing.net
Main Authors Chiera, Belinda, Bédi, Branislav, Zviel-Girshin, Rina
Format Report
LanguageEnglish
Published Research-publishing 12.12.2022
Subjects
Artificial Intelligence
Communities of Practice
Comparative Analysis
Computational Linguistics
Computer Assisted Instruction
Computer Software
French
Independent Study
Network Analysis
Second Language Instruction
Second Language Learning
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Summary:Modern language learning applications have become 'smarter' and 'intelligent' by including Artificial Intelligence (AI) and Machine Learning (ML) technologies to collect different kinds of data. This data can be used for analysis on a microscopic and/or macroscopic level to provide granulation of knowledge. We analyzed 1,213 French language learner data over a 30-day period, publicly available from Duolingo, to compare the progression of individual learners (microscopic granularity) and large groups of learners (macroscopic granularity). Using network modeling, we compared patterns of individual learners against one another and that of a learning community and determined what groups of learners typically practice across communities. Preliminary results suggest how applications for L2 learning can be designed to create an optimal path for learning. [For the complete volume, "Intelligent CALL, Granular Systems and Learner Data: Short Papers from EUROCALL 2022 (30th, Reykjavik, Iceland, August 17-19, 2022)," see ED624779.]