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Learning vocabulary through Vocablearning has no advantage over guessing from context

Besides the knowledge of basic vocabulary, a good guessing strategy also requires a good foundation in the knowledge of the part of speech and morphology of words, which the student may not possess.

Even with proper training in guessing strategies, for an article with less than 90% of the words known to a student, most students have difficulty in applying the strategies. This can easily be proved by crossing out all the words which are not on the Oxford 3000TM word list Note  in any newspaper article and presenting the article to an educated adult in Hong Kong. Most people will not have a reasonable comprehension on the article. Guessing strategy only works if the reader already has a vocabulary proficiency that can cover 95-99% of the words in an article, of course, the higher the better. Hirsh and Nation (1992) found that a vocabulary size of about 8500 head words was needed to cover 95% of running words in unsimplified novels.

Independent reading requires knowledge of at least 95% of running words in a text (Laufer 1989; Liu & Nation 1985). With 90% of the running words known, a user only has 26-36% chance guessing the meaning of an unfamiliar word right. With 96% of the running words known, the chance rises to 36–43% (Liu & Nation 1985).

On another hand, Paribakht and Wesche (1999) found that even of a text with 98% of the running words known to the readers, 55% of the unknown words were ignored by the readers while they were reading the text. The readers only applied guessing strategy onto 32% of the unknown words.

Prince (1995) also found that learning the meaning of a word from translation was far more effective than guessing the word from the context.

Guessing strategy was usually quoted as an applicable strategy because there was simply no better way to handle the sheer volume of vocabulary that a normal ESL (English as a Second Language) learner would encounter.

A typical primary school student in Hong Kong knew only about 400-600 general words upon graduation. Considering the fact that most of them started learning English from K1, their average daily acquisition rate was only about 0.12–0.14 word/day. At Secondary 1, across the subjects, the vocabulary demand is between 2000-5000 words, about 800 of which are general academic words and the rest subject-specific (Evans, Hoare, O’Halloran & Walker 2001). To cope with this dramatic difference, there is an urgent need to increase the acquisition rate of students to 4.6–15 words/day.

The Vocablearning computer-aided learning platform adopts a very different approach to tackle the problem. By using a mathematic algorithm to generate a tailor-made 10-minute daily exercise for the users, it enables its user to recognize and remember about 5-10 words a day. A computer generated exercise seems to be less sophisticated than a teacher-directed writing assignment. However, according to Folse (1999), it is the frequency of the retrieval that matters most in the memorization of words, not the form of retrieval. Vocablearning can help the student to give an adequate revision coverage to all the content words, about 10,000, that appear in the secondary curriculum.

Note :      Oxford 3000TM word list contains 3000 keywords which have been carefully selected by a group of language experts and experienced teachers as the words which should receive priority in vocabulary study because of their importance and usefulness. Two sample articles, excerpts from Harry Potter and SCMP, with the words not on the list crossed out are attached for easy reference.

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