Increasing Students Vocabulary in Efl Class

After reviewing the literature, this work tries to show the importance of teaching vocabulary fõr students’ literacy skills, especially, reading comprehension. Many researchers suggest that the greatest amount of vocabulary growth occurs through incidental word learning in wide reading, and, research indicates that vocabulary instruction is an important vehicle for vocabulary learning. (Anderson& Nagy, as cited in Harmon, 1992, p.306). Word knowledge is one of the best ways of successful reading and comprehension. “Reading enhancement correlates with reader’s vocabulary” (Im, 1994, p.12). Therefore, today’s language teachers and researchers have realized the important role of vocabulary in reading comprehension. A survey carried out on 10th, 11th and 12th grade students, regarding their reading comprehension, shows that unknown words is one of the factors which influences their ability to read and comprehend a passage. It also shows that students feel the need to be instructed on strategy when encountering new words and consequently improving their vocabulary. This inhibits their understanding of a reading selection. As a result it is crucial that teachers equip students with methodological tools to be employed when they encounter unknown words. There are a lot systematic approaches for discerning which skills and words a teacher should focus on and meaningful classroom activities to reinforce the words and strategies that teachers can use to help students increase their word knowledge. Finally research indicates that developing students’ vocabulary correlates with success in all areas of curriculum (Edger, 1999, p.14). The success of vocabulary development depends on students’ active process of learning and strategies used by teachers.

One-sided tolerance interval in a two-way balanced nested model with mixed effects

In many research areas (such as public health, environmental contamination, and others) one deals with the necessity of using data to infer whether some proportion (%) of a population of interest is (or one wants it to be) below and/or over some threshold, through the computation of tolerance interval. The idea is, once a threshold is given, one computes the tolerance interval or limit (which might be one or two - sided bounded) and then to check if it satisfies the given threshold. Since in this work we deal with the computation of one - sided tolerance interval, for the two-sided case we recomend, for instance, Krishnamoorthy and Mathew [5]. Krishnamoorthy and Mathew [4] performed the computation of upper tolerance limit in balanced and unbalanced one-way random effects models, whereas Fonseca et al [3] performed it based in a similar ideas but in a tow-way nested mixed or random effects model. In case of random effects model, Fonseca et al [3] performed the computation of such interval only for the balanced data, whereas in the mixed effects case they dit it only for the unbalanced data. For the computation of twosided tolerance interval in models with mixed and/or random effects we recomend, for instance, Sharma and Mathew [7]. The purpose of this paper is the computation of upper and lower tolerance interval in a two-way nested mixed effects models in balanced data. For the case of unbalanced data, as mentioned above, Fonseca et al [3] have already computed upper tolerance interval. Hence, using the notions persented in Fonseca et al [3] and Krishnamoorthy and Mathew [4], we present some results on the construction of one-sided tolerance interval for the balanced case. Thus, in order to do so at first instance we perform the construction for the upper case, and then the construction for the lower case.