The Art of Teaching English

<div class="table-wrap"><table data-ke-type="table" data-ke-align="alignLeft" style="width: 100%; height: 89px;" border="1" data-ke-style="style7"><tbody><tr style="height: 18px;"><td style="width: 23.6798%; height: 18px;"></td><td style="width: 37.9125%; height: 18px; text-align: center;">NNS</td><td style="width: 38.4076%; height: 18px; text-align: center;">NS</td></tr><tr style="height: 18px;"><td style="width: 23.6798%; height: 18px;"></td><td style="width: 76.3201%; height: 18px; text-align: center;" colspan="2">per1000 words</td></tr><tr style="height: 18px;"><td style="width: 23.6798%; height: 18px;">possibility and ability</td><td style="width: 37.9125%; height: 18px; text-align: center;">12.24</td><td style="width: 38.4076%; height: 18px; text-align: center;">6.64</td></tr><tr style="height: 18px;"><td style="width: 23.6798%; height: 18px;">obligation and necessity</td><td style="width: 37.9125%; height: 18px; text-align: center;">5.31</td><td style="width: 38.4076%; height: 18px; text-align: center;">1.64</td></tr><tr style="height: 17px;"><td style="width: 23.6798%; height: 17px;">predictive and others</td><td style="width: 37.9125%; height: 17px; text-align: center;">4.06</td><td style="width: 38.4076%; height: 17px; text-align: center;">4.22</td></tr></tbody></table></div> <hr data-ke-style="style5">언어 분석 도구 wordsmith (6.0)↙                                    ↘1) quantitive 양적 연구 (frequency)              2) qualitate 질적 연구  →  compile corpus*메모장에 있는 단어만 !→  textcomputerised datause + pattern 1. learner corpus2. text books Q. can 이 몇번 나오나? = 원시적인 방법 → 하나하나 세기→  이걸 wordsmith 가 해준다는 것! NS (native speaker) 1. 단어 수 = tokens2. corpus size 규모보통 만 단어 이상이 되어야 측정에 신빙성이 있다.  choose textsNNS (none native speaker)bottom line에서 statistics 보기tokens 보기→  NNS = 92,380 change selectionNS 50 (BAWE) 선택statistics 에서 tokens 보기→  NS = 124,461 (단어수라는 말) <순서>wordlist → File →  new →  text selection → make a ~ →   nns → ctrl + a (모두 선택) →  끄고word list → clear →  ns <hr data-ke-style="style5"> normalised frequency = 비례적인 frequency (숫자) entry / 92,386 = 0.00804 -> 소수점 이동 ->  search word  per 1000word 8.04 1000단어 마다 CONCORD however nns  221 92386 = 0.0023921373368259 -> 2.39 ~ ns 319 124,461 --------------------- NNS = 92,386 NS = 124,461 --------------------- therefore NNS) 123 0.00133137055398 -> 1.33 NS) 257 0.0020649038654679 -> 2.06 --------------------- reference load 꼭 해주기 그 다음 ok ---------------------- p~ 1131 0.0122421146061092 -> 12.24 827 0.0066446517382955 -> 6.64 obligation nns 491 0.0053146580650748 -> 5.31 ns 205 0.0016471023051398 -> 1.64 pre nns 376 0.0040698807178577 -> 4.06 526 0.0042262234756269 -> 4.22