How Best to Learn Foreign Languages

This is the companion website for Masato Hagiwara's invited talk "How Best to Learn Foreign Languages" at the Baidu Shenzhen office.

August 10, 2018, Shenzhen, China

TL;CE (too long; checked email)

  • My language learning experience
    • I tried to learn Esperanto from textbooks 15 years ago
    • The Esperanto community is now growing and thriving thanks to online technologies
    • We as humankind are getting better at learning languages, mainly thanks to technologies
    • There are 1.2 billion people worldwide who are learning a second language. Optimizing human language learning has a truly huge impact on those people's lives
  • Traits of successful language learners
    • Your total learning = time x learning efficacy. This "time" factor is often overlooked
    • We wanted to find out what separates those who stay on our app and those who quit
    • There are a few statistically significant factors for predicting user retention. Frequency, number, and consistency of lessons stood out
    • We ran user clustering to find out different user behavioral patterns based on time of day, and found clear behavioral clusters (e.g., 9-to-5, prime time, weekend, etc.)
    • The "before bed" cluster was the best in terms of learning performance. This result is consistent with many research studies (e.g., Mezza et al. 2016) on the effect of sleep
  • Modeling learning and forgetting

    • The two sigma problem - tutored students perform two standard deviations (sigmas) better than the ones in a traditional classroom setting
    • "Strength bar" keeps track of how much you still remember the concept taught in a lesson
    • The older version of Duolingo was using the Leitner system, which is a simple system to estimate the halflife based on the numbers of corrects and incorrects
    • Halflife regression (Settles 2015) estimates the halflife based on an arbitrary set of features, leading to a 1.7% increase in next-day retention
  • Conclusion

    • We released the SLAM (second language acquisition model) dataset, which provides Duolingo users' long-term language learning traces and token-level error attribution


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