Friday, January 17, 2020

Learning the vocabulary with AI. Two major cases. The DTA can provide an intelligent environment


Learning the vocabulary with AI. 
Two major cases.  
The DTA can provide an intelligent environment  

The intelligent scene: the things talk to you.  They tell their names. Even tell you aloud.  You have quests.  Just as in a game.  And you learn skills while playing.  The difference here is that the skills you learn in this game have long-lasting value. This article is explicitly licensed Public Domain (CC0).
Magnolia gardens

Two cases? 

Case 1

  • There are many instances of learning a second or subsequent language.
  • The objective is always the same: 
  • Get able to use the language in effective communication with native speakers.
  • Instructional procedures are well understood.  The only problem: it takes practice.
  • Practice takes time.  

Case 2

  • A less obvious case occurs when students prepare to study a specialized subject.
  • Here they do not  learn a whole new language but only a specialized vocabulary.
  • Some of the words are familiar to them, but the meaning is specialized.
  • And some of the things mentioned may be dangerous or inaccessible.
  • Example: anatomy, surgical tools.

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Attaching names 

To objects (nouns)

  • This is a major part of learning a language.
  • In case 1, the objects are familiar and have names in the native language.
  • The goal for the student to be able to name the objects in the target language.
  • In case 2, the objects are unfamiliar (in detail) and would be called a generic name.
  • The goal for the student is to be able to the technical name for each object.

Subtasks

  • The verb attach is abstract.  In concrete terms, it refers to the following subtasks:
  • See the object and give the target name in text or speech.  
  • Read or hear the name in the target language and find the object.
  • Read or hear the name in the target language and give a definition.

Beyond objects

  • Other parts of speech generally need added nouns to have meaning  
  • Verbs: objects in action (animations)
  • Adjectives: properties of objects. 
  • Prepositions: relations among objects.
  • Et cetera
  • Most speech about the environment can be represented in virtual worlds.

Teaching scenes

  • Yes, multiple, just as common experience has multiple scenes.
  • And language has scene affinities.  
  • We learn what things are common to what places.  Situational learning.  
  • Farm animals are common to a farm, not to a school classroom.
  • A digital world can take students to an intelligent farm.
  • And not just to one place, but to various places where farm animals may be found.  
  • A field, a barn, a truck. a pen.
  • More generally, every vocabulary term needs scenes where it would be found.  
  • Such scenes would probably host multiple terms.

Intelligent objects

  • For every term there would be digital versions of a few concrete instances.
  • But it the digital world, the instances can be smart.
  • The digital cows don't just say "Moo."
  • On click, they can say "cow" aloud and display the name in text. 
  • They can also give a token or otherwise record the click
  • The token shows that the learner has found the correct object.
  • The cow's actual response depends on the learner's achievement level. 
  • More skilled learners would get less support.
  • Adaptive learning (Search)
  • Adaptive learning (Wikipedia)

Beyond objects

  • Since verbs denote action, they need action to represent them.  
  • In a digital world, they could be rendered by animations, by GIFs, or by videos.
  • Adjectives describe nouns and so would already be represented in the scenes.
  • The study of adjectives would be stimulated by exercise questions; 
  • Not "Find a cow."  Instead, "Find a brown cow."
  • Prepositions also deal with properties of nouns and so could be represented in the scenes.
  • The exercise questions would ask for "the cow in the barn."

The AI vocabulary class as a game

  • The collection of scenes can provide all the vocabulary information of a textbook.
  • If we bring the learning task into the digital world, it looks more like a game that like school work.
  • The old school assignments can become games that learners play.
  • As they play they can earn badges and progress through ascending levels.
  • That is much like doing homework, passing tests, and passing grades.
  •  And you can add social interaction in the game.  That is not at all like the old days.
  • But it is a lot more like speaking a language than than it the old days,

Related

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License

  • Original text in this blog is CC By: unless specified public domain.
  • Use as you please with attribution: link to the original.
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  • Annotated screenshots made with Jing
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  • Selby Evans in Kitely and hypergrid, Thinkerer Melville in Second Life.


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