Among all abilities, fundamental English reading ability is fundamental for readers to read all kinds of English texts, including academic texts. 許蔚農 (2003) revealed that the reading problems of non-English major students usually resulted from the lack of
vocabulary and grammar knowledge. Even though students might know a sentence consisted of subjects, verbs, and objectives, they just could not figure out the parts of speech in the sentence when they were reading. As to vocabulary, if students only
memorize one meaning of each word, they would not be able to make the whole sentence in sense. 周碩貴 and 吳庶任 (2004) suggested high school students, before entering university, have to learn English by themselves in addition to the learning in regular English classes. Without acquiring sufficient vocabulary, they would encounter a great amount of difficulties in reading academic English textbooks. Uso-Juan (2006) emphasized that linguistic proficiency was one fundamental element significantly influenced academic reading performance while the other one was discipline-related knowledge. Cheng (1995) demonstrated that reading covers three categories of knowledge – linguistic knowledge,
knowledge about rhetorical structure, and background knowledge. Fundamental linguistic knowledge is necessary for academic reading, and grammar and vocabulary knowledge are two of the central factors covered within linguistic knowledge. Readers usually get more confused with long or complex sentences rather than short or simple sentences due to grammatical problems. Cohen, Glasman, Rosenbaum-Cohen, Ferrara, and Fine (1988) illustrated that “heavy noun phrases, in various syntactic functions not only caused difficulties for informants, but were among the few structures that were predictably problematic for students” (p. 158). Eskey (1973) also expressed that non-native students would have more problems identifying the meaning of complex structure in reading.
In academic reading, readers more or less encounter unfamiliar vocabulary on a regular basis. This unfamiliar vocabulary might be some general words that readers have yet to encounter, or could also be some academic terms the readers have not yet learned.
Cheng (1995) indicated that readers had difficulty in the overall understanding of an academic text when they encountered unknown words during the reading process. The reason for this was that a great amount of words in academic texts were related to professional knowledge in specific fields.
Academic texts always involve a great amount of technical terms. Technical terms are an inevitable part of the reading process for graduate students. Mudraya (2006)
emphasized that the characteristics of technical words are lacking exact synonyms and do
no appear frequently in texts. On the contrary, non-technical terms do have synonyms and frequently occur in all kinds of texts. Nevertheless, the functions of some vocabulary are neither technical nor completely general. Baker (1988:91) explained that such a term
“covers a whole range of items that are neither highly technical nor specific to a certain field of knowledge nor obviously general in the sense of being everyday words which are not used in a distinctive way in specialized texts.” Mudraya (2006) defined these words as sub-technical vocabulary.
Both technical and sub-technical terms require reader’s content schemata in order to fully comprehend the applications of specific words. Cohen et al. (1988) indicated the non-technical words might also convey some technical meaning in academic texts. Thus, readers had to expand upon the meaning of every word when reading academic texts.
When readers checked the dictionary for comprehension, they had to remember that meaning is flexible. It is up to the reader to adopt a meaning suitable to the content. Cheng (1995) pointed out that vocabulary annoyed readers’ academic study, and understanding unknown words was the most difficult part in academic reading:
“The nature of their academic studies required them not only to know the words with common meaning but also with specialized meaning in their particular areas. In some extent, the more they know about their subject area, the less difficulties they would have with their subject terminology.” (p. 14)
Some scholars researched the words most used in academic texts and made corpus.
Coxhead (2000) created a new academic word list (AWL) by collecting 414 academic texts
from more than 400 authors. The texts involved four sub-corpora: arts, commerce, law, and science. Mudraya (2006) built another corpus—Student Engineering English Corpus (SEEC) for engineering readers. SEEC was built according to 13 English engineering textbooks. Those corpora are beneficial for academic readers and show the meaning of words that most appear in academic texts. 游淑儀, 張瑞華, and 蘇秀妹 (2004) stated it is necessary for academic reader to choose some specific academic vocabulary to learn.
Moreover, students in different levels should have different objectives to learn vocabulary.
When facing a new word they have not yet encountered in their studies again and again, readers are suggested to guess the meaning of the unfamiliar word. While it is good for readers when they guess correctly, it may also confuse them when they guess
incorrectly. Especially, readers with poor language proficiency also usually have poor guessing capabilities. 周碩貴 and 吳庶任 (2005) indicated that guessing the meanings of unfamiliar vocabulary was an required ability for reading and that it could increase readers’
vocabulary knowledge. Nevertheless, incorrect guessing might make readers memorize the wrong meaning if the guessing was not corrected. They recommended readers not to guess every new vocabulary immediately. Readers could skip unfamiliar vocabulary and keep reading to gain more information before guessing.
Alderson (1984) indicated that second language readers rely heavily on vocabulary knowledge. A lack of vocabulary knowledge is the first problem readers have to overcome
for comprehension. Readers are often recommended to guess the meaning of unfamiliar vocabulary by applying clues found within the text. Kruse (1979) illustrated that for English native speakers, guessing was the most common method to handle unfamiliar words. Kruse indicated that only knowing the meaning of enough words in a sentence could lead to full comprehension and suggested readers to guess at the unknown words.
Block (1992) brought up that competent readers can skip the unknown words and utilize context clues to guess unknown words without losing an overall comprehension. 陳秋蘭 (1992) encouraged readers to make use of available clues to predict the meaning of unknown words instead of using dictionaries.
Sprenge (1975) argued that Taiwanese students focused on learning grammar and vocabulary from their junior high school education; however, they still did not have enough vocabulary for English reading. Most Taiwanese students preferred consulting a dictionary to find the meaning of unfamiliar words, even though many scholars suggested that this step was not necessary for comprehension. Chinese L2 readers often used dictionaries, and this kind of reader focused more on details than overall comprehension. Nambiar-Gopal (2005) expressed that readers heavily relied on dictionaries because they were not confident without it. Li and Munby (1996) emphasized that some readers would feel insecure without
dictionary. They guessed specific words that frequently appear in the text but still referred to the dictionary after guessing in order to confirm the guessing was accurate.
Guessing the correct meaning of a word is more difficult for low-proficiency readers.
Particularly if the context is more complex, then guessing would become a much more difficult process. Thus, readers with low language proficiency should not make
unnecessary guessing. Graduates with poor language proficiency can apply other strategies or tools to comprehend the academic texts, and thereby increase their proficiency
continuously. On the other hand, graduates with good language proficiency should be confident to predict the meaning of unknown words.