drill

parse story

sengen

Scans a string, separating into dictionary words.

Longest words first.

repeat symbol ๆ is sometimes missed. see story Maanii.

• identify rules
  • ccredup
  • cciva
  • ccive (are cciva and ccive the same?)
  • cct
  • ccf
• test dict
• load test suite from dict pf=true

A typical recommendation engine would choose stories with similar content. We leave that to the student to judge by reading the story titles.

Leveling is one of the tools we use.

student: vocabulary size, average level
each story: average level, number of mastered words, number of new words, ratio, total number of words

find stories with words that the student should know by now to be at this level.

Goal. Introduce vocabulary to the student gradually, more common words first, more advanced words later.
Always reinforcing and building upon what the student already knows.

totally wrong. stories are recommended based on specific vocabulary comparison of known and unknown words.

leveling is a vague way of sorting stories and words and students and choosing what to offer to the student next.

the level of a student can be used to as motivational, but would be better to just use size of mastered vocabulary.

controlled vocabulary. stories or paragraphs can be designed with specific level of words.

The goal of leveling is to recommend appropriate stories to a student depending on his vocabulary. An appropriate story is one that has many known words and a few new words. (Coach and student are expected to sometimes override these recommendations and instead choose stories based on the student's interest in the subject matter.)

The “level” of a word is an index from 1 to 100.
A lower level indicates a more common word.
A higher level is more advanced, rarely-used word.

The level of a story is the weighted average of the levels of all the words used in the story.

The levels of words and stories are re-calculated on the server once each day.

The level of a student is the average level of all the mastered words in his vocabulary.

1. Make the mean-table in memory with an entry for every word/mean in the dictionary. Each entry in the table contains word, mean, old-count, old-level, new-count, new-level.
2. Read all the stories. For each:
   • open the consolidated words field into an array
   • (the number of loc items in the array is the use-count for that word/mean)
   • add the use-count to the new-count in the master word table
3. Loop thru the mean table saving lowest and highest use-count.
4. Loop thru the mean table again. For each entry:
   • calculate and set the level for the mean, by interpolating an index from 1 to 100 as follows:
     • level = 100 - (use-count * (high-low)/high)
     • max/min : 1/100
     • freq/max-min = 100-level/100
     • 100 * (freq/max-min) = 100-level
     • level + (100 * (freq/max-min)) = 100
     • level = 100 - (100 * (freq/max-min))
   • Update mean table record setting count and level if changed.

1. Read all stories. For each:
   • open the consolidated words field into an array
   • get the level of each word from the dictionary
   • save total-use-count
   • save total-level as level*use-count
2. Calculate average-level.
   • average-level = total-level / total-use-count
   • update the story record, setting level

For each story

• Num words
• Num uniques
• Avg level

For each story per student

• Num mastered
• Num new words
• Num working
• % new / uniques

Calc avg level for each user

New table, story-user

Curiosity, exploration, thirst for knowledge

• Name
• Age
• Sex
• Associates -
• IQ
• Education
• Job

• Hobbies
• Countries visited
• Books read
• Authors favorite
• Movies favorite, recent
• Movie stars favorite

• Wealth
• Income
• Bank accounts

Links to proper names

Sleep period to organize data

Blockages to action, like denying sex or flying a helicopter.