About the Project

Revita is a novel approach to language learning, it builds on Artificial Intelligence, Language Technology, linguistics and educational data science.  Software engineering is also essential to assure the best user experience. 

revita-fields-diagram

Revita acts as an intelligent tutor or an intelligent teacher's assistant, and has these main features:

  • Beyond beginner level:  Revita is for learners at the intermediate to advanced  levels — i.e., for level A1 or above, not for absolute beginners just starting to learn a language.

  • Learning from stories:  Revita lets students learn from arbitrary content — any authentic text, which the students choose themselves, from anywhere on the internet.  We encourage learners to use learning material based on their interests and preferences — if you learn from material that you like, you will spend more time learning!

  • Unlimited exercises:  Revita automatically creates exercises based on the learner's content.  It generates an unlimited variety of exercises, and they are different each time you practice with the same story.

  • Active competency:  The exercises stimulate the student to actively produce language, rather than passively absorb rules.

  • Assessment ⇒ Personalization:  Revita continually tracks the progress of the student.  It analyzes results from past exercises — learns about common patterns of mistakes and possible learning paths — to adapt future exercises to the student's skills as they mature.

Revita began with the goal of revitalization and support for endangered languages, but has now evolved toward learning  foreign languages in general, including "majority languages."

Revita offers a small "public library" of sample stories for each language, but learners are encouraged to add and work on new stories that they like.

Based on each story, the system creates various kinds of exercises for practice:
• fill-in-the-blank quizzes
• multiple-choice quizzes
• listening comprehension quizzes
• flashcards: vocabulary practice
• and more...

revita-detailed-components-02

For additional information, please see these project pages and consult our publications.   Also, please feel free to contact the Revita team directly!

Collaborators and Credits:

Revita builds upon many resources and tools developed by our international colleagues and collaborators.

With our collaborators we develop components for supporting learning of various languages. 
The goals are: to assess the written and oral skills of learners quickly and accurately, based on patterns of mistakes, and to relate them to a natural order of acquisition of skills, based on significant numbers of L2 learners—thousands of university students.
For oral skills, the research lab is developing new components to analyze the students' ability to process spoken language. This will allow us to test hypotheses about the mechanisms for processing of audio input by learners, and to create components to train this ability by following a personalized path for each student.

Some important resources that Revita builds upon, and content providers, who have granted us permission to use their resources:

    • The GiellaTekno platform: language technology for Uralic languages, and endangered languages from other language families.
    • The Apertium platform for languages from the Uralic, Turkic and other language families.
    • Morphisto—the German morphological analyzer.
    • CrosslatorTagger for Russian, by Professor Eduard Klyshinskiy (Higher School of Economics, Moscow, Russia).
      For Uralic and Turkic minority languages, CrosslatorTagger is used to detect code-switching into Russian in authentic texts.
    • SakhaTyla.Ru—portal for the Sakha (Yakut) language, providing analyzers and dictionaries.
    • ResponsiveVoice used under Non-Commercial License.
    • Stress library (Russian) by Rob Reynolds, Assistant Research Professor, Brigham Young University, Utah.
    • Icons by Madebyoliver from Flaticons and Rohan Gupta from the Noun Project.

    The Revita Project is supported in part by:

    • Academy of Finland, Research Council for Culture and Society (Grant 267097)
    • Opetushallitus: The Finnish National Agency for Education (Grant OPH-1443-2020, TM-18-10846)
    • CIMO: Center for International Mobility (Grant TM-16-10082)
    • HIIT: Helsinki Institute for Information Technology
    • University of Pisa, Italy: ErasmusPlus Programme of the European Commission
    • Tulevaisuus Rahasto 2020: Future Development Fund, Faculty of Arts, University of Helsinki