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She argues that children expect the noun phrases in a sentence to map one-to-one with participant roles in the event described by that sentence. For example, if a toddler hears a sentence that contains two noun phrases, she can infer that that sentence describes an event with two participants.
‘This is a ball.’ If the sentence is true, children should put a tick next to the picture. If the sentence is false, children should put a cross next to the picture. Part 1 tests reading short sentences and recognizing words. Part 2 has a big picture and some sentences about the picture. If the sentence is true, children should write ‘yes’.
Average sentence length in words: −.52: a negative correlation, that is, the longer the sentence the more difficult it is 2: Percentage of easy words.52: the larger the number of easy words the easier the material 3: Number of words not known to 90% of sixth-grade students: −.51: 4: Number of "easy" words.51: 5: Number of different "hard ...
A famous example for lexical ambiguity is the following sentence: "Wenn hinter Fliegen Fliegen fliegen, fliegen Fliegen Fliegen hinterher.", meaning "When flies fly behind flies, then flies fly in pursuit of flies." [40] [circular reference] It takes advantage of some German nouns and corresponding verbs being homonymous. While not noticeable ...
Example of problem/project based learning versus reading cover to cover. The problem/project based learner may memorize a smaller amount of total information due to spending time searching for the optimal information across various sources, but will likely learn more useful items for real world scenarios, and will likely be better at knowing where to find information when needed.
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For example, a computer can now mimic a child's learning progress and induce general language rules when exposed to a list of words with only a limited number of explanations. Nevertheless, as no universal model has yet been agreed upon, the generalizability of word recognition models and its simulations may be limited.
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