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Some interesting CALL ideas

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  • bytecoollab
    This web page includes some interesting CALL ideas: https://sites.google.com/site/yaoziyuan/ideas ... Foreign Language Learning * Automatic Code-Switching
    Message 1 of 1 , Sep 23, 2009
      This web page includes some interesting CALL ideas: https://sites.google.com/site/yaoziyuan/ideas


      Foreign Language Learning

      * Automatic Code-Switching (ACS) - The computer automatically selects a few words in a user's native language communication (such as a web page being viewed), and supplements or even replaces them with their foreign language counterparts, thus naturally building up his vocabulary. For example, if a sentence
      (Chinese for "He is a good student.") appears in a Chinese person's Web browser, the computer can insert student after 学生 (optionally with additional information such as student's pronunciation):
      他是一个好学生 (student)。
      After several times of such teaching, the computer can directly replace future occurrences of 学生 with student:
      他是一个好 student。
      Ambiguous words such as the 看 (Chinese for "see", "look", "watch", "read", etc.) in
      (Chinese for "He is reading a book before the TV.") can also be automatically handled by listing all context-possible translations:
      他在电视前看 (阅读: read; 观看: watch) 书。
      Practice is also possible:
      他在电视前 [read? watch?] 书。
      Because the computer would only teach and/or practice foreign language elements at a small number of positions in the native language article the user is viewing, the user wouldn't find it too intrusive. Automatic code-switching can also teach grammatical knowledge in similar ways.
      * Progressive Word Acquisition (PWA) - In ACS, long words are optionally split into small segments (usually two syllables long) and taught progressively, and even practiced progressively. For example, when
      (Chinese for "Colorado") first appears in a Chinese person's Web browser, the computer inserts Colo' after it (optionally with Colo's pronunciation):
      科罗拉多州 (Colo')
      When 科罗拉多州 appears for the second time, the computer may decide to test the user's memory about Colo' so it replaces 科罗拉多州 with
      Colo' (US state)
      Note that a hint such as "US state" is necessary in order to differentiate this Colo' from other words beginning with Colo. For the third occurrence of 科罗拉多州, the computer teaches the full form, Colorado, by inserting it after the Chinese occurrence:
      科罗拉多州 (Colorado)
      At the fourth time, the computer may totally replace 科罗拉多州 with
      Not only the foreign language element (Colorado) can emerge gradually, the original native language element (科罗拉多州) can also gradually fade out, either visually or semantically (e.g. 科罗拉多州 -> 美国某州 -> 地名 -> ∅). This prevents the learner from suddenly losing the Chinese clue, while also engages him in active recalls of the occurrence's complete meaning (科罗拉多州) with gradually reduced clues.
      * Subword Familiarization (SWF) - Again in ACS, word roots (e.g. pro-, scrib-) and meaningless word fragments (e.g. -ot) are optionally treated as two special kinds of standalone words and taught and practiced in the user's incoming native language information. Meaningless fragments are considered abbreviations and acronyms derived from real, meaningful words. Getting the learner familiar with all these subword units can facilitate the acquisition of longer, real words that contain them.
      * Phonetics-Enhanced English (PEE) - The computer can add non-intrusive diacritical marks (e.g. the mark in á) above normal English words to better reflect their pronunciations. Unlike radical spelling reform proposals, a word's original literal form is always preserved. Unlike annotating words with their IPA forms above, diacritical marks are closely integrated with letters so a learner can "read once and learn both the literal and the phonetic form." In inputting English, the learner still uses the original literal form only.
      * Orthography-Enhanced English (OEE) - Sometimes spelling a word based on its pronunciation can be hard, even for native speakers. For example, is it Lawrence or Lawrance? We can slightly change a word's visual form to help recall its correct spelling. For example, when the computer displays a word that has the -ance suffix (e.g. instance), it can lower the letter a to some degree, just like Intel has a trademark "intel" with a lowered e. Such a new visual form can help people recall that the unclear letter in inst?nce is a because a is always lowered in -ance while e is never lowered in -ence.

      Computer-Assisted Foreign Language Writing

      * Input-Driven Syntax Aid (IDSA) - As a non-native English user inputs a word, e.g. search, the word's sentence-making syntaxes are prompted by the computer, e.g.
      v. search: n. searcher search~ [n. search scope] [for n. search target]
      so he can now write a syntactically valid sentence like "I'm searching the room for the cat."
      * Input-Driven Ontology Aid (IDOA) - As a non-native English user inputs a word, e.g. badminton, things (entities) and relations that normally co-exist with the word in the same scenario or domain are prompted as a systematic ontology graph by the computer, e.g. entities like racquet, shuttlecock and playing court, relations like alternate, serve and strike, and even full-scripted composition templates like template: a badminton game. The benefits of the ontology aid are twofold. First, the ontology helps the user verify that the "seed word", badminton, is a valid concept in the intended scenario (or context); second, the ontology pre-emptively exposes other valid words in this context to the user, preventing him from using a wrong word, e.g. bat (instead of racquet), from the very beginning.

      Foreign Language Reading without Learning that Language

      * Full-Automatic Layered-Quality Machine Translation (FALQ-MT) - Lexical and syntactic ambiguities are translated to fuzzy concepts and structures instead of precise but error-prone results. Less information is better than misinformation. If the reader can't guess the meaning of a fuzzy occurrence from its context, he can "zoom in" and see more detailed translation possibilities if he feels that occurrence is important.

      Foreign Language Writing without Learning that Language

      * Formal Language Writing and Machine Translation (FLW) - A person not knowing a target language can generate information in that language by composing in a formal language based on his native vocabulary and having the composition machine-translated. Tools such as the input-driven syntax aid and input-driven ontology aid can be borrowed to assist the person in formal language writing. Manual word sense disambiguation (WSD) can be conducted after the composition is finished, on a domain-to-domain basis, because it is cognitively easier for the writer to focus on a single domain at a time and answer a series of questions "Does <word_i> belong to this domain?"

      Ontology-Based Resource Sharing

      * Wikipedia-Based Resource Sharing (WP-RES) - A useful property of Wikipedia is that each Wikipedia article or category can serve as a unique address, or "coordinates", for the topic it corresponds to. With this property, we can enable people with the same interest to rendezvous at the same Wikipedia page and therefore talk with each other. People could also register resources at a Wikipedia page's External Links section so that other people with the same interest can find them. People could even "subscribe" to a Wikipedia page for new and updated resources and opportunities on that topic.

      Ontology-Based Problem-Solving Skills Sharing

      * Wikipedia: From Knowledgebase to Strategybase (STRABASE) - If we're solving a problem, say, a math problem, we choose a seemingly promising strategy from our "strategy bases" in our minds, according to the problem's main type and characteristic conditions. Such a "strategy base" is something we can build up externally using a wiki. A "strategy" is a special kind of knowledge that caters to certain problem characteristics and provides certain problem-solving frameworks. The wiki can store and categorize strategies and domain knowledge by their intended problem types and characteristics, so the human can better evaluate, select and apply strategies relevant to his problem.


      * Chinese Pinyin Input Method Revisited (PYIME) - Today's Chinese pinyin input methods inherit the single-row candidates window from the DOS era. If we categorize candidate characters into multiple rows according to some criteria, the user can more easily home in on his desired character. For example, each row contains characters that have the same phonetic radical, and one row reads "马 吗 妈 码 玛", while another row reads "麻 嘛 䗫". Rows can also correspond to the five possible tones in Chinese, as most mainland Chinese don't type tones. Still, there can be a special, first row for the most frequently used words and characters.
      * A Politically Correct New Name for English (ARCS) - As technology like automatic code-switching would make English a much cheaper commodity for non-native people to acquire, for the first time it will become possible for most people in the world to use decent English. But nationalist sentiments can be a negative factor for some people to adopt English. While it is logically recognized by everybody that all natural languages are actually made of equally random syllables, emotionally people can still more or less feel unequal that one language is more international than others. A reason for this paradox is that languages are named by their nations of origin: English, French, Spanish, etc. Therefore, we can use a "renaming" technique to better reflect a language's random nature rather than nationalist connotation. Actually, the word "language" itself already has a strong nationalist connotation, and I propose the term "code system" to eliminate that connotation. As for English, let's rename it as "A Random Code System", or ARCS for short.
      * Foreign Language Proficiency Measurement (FLPM) - How does a non-native speaker introduce his language level to a native speaker in an understandable manner? The computer can test his proficiency and compare it with native speakers at different ages. Introductions like "My English level is like a 10-year-old American child" should be understood well by a native speaker.
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