The level of writing ability is an important indicator to measure students' language application achievements. Therefore, in order to enable students to better complete the learning of knowledge, teachers should make corresponding changes to the teaching work mode, so that students can ensure the rise of learning quality. Therefore, the author mainly analyzes how to ensure the high-quality development of English writing classes in colleges and universities under the background of ideological and political courses, hoping that the writing division can be carried out smoothly, and the comprehensive ability of students can be comprehensively improved.
Do undergraduates whose native language is not English have writing deficiencies leading to academic struggles? The present study showed that the answer to this question was “no” at an American West Coast public university. This university’s nonresident undergraduates on average earned B- to B+ in their colleges’ English intensive-writing programs’ classes, C in community college English classes, and term grade point averages between 2.5 (C+ to B-) and 3.2 (B) in the fall term of the five most recent academic years. Hierarchical linear modeling analyses showed that the predictors with the largest effect sizes were English writing programs and class level; however, each predictor accounted for less than 25% of the total variance.
This paper reviews and strengthens the data on cultural transfer by Arab Muslim students writing in English and adds the significant element of the cultural impact of Islam on such writing. This qualitative study examines the writing of 18 teacher trainees at an Arab language teacher training college in Israel. Results point to a strong cultural influence appearing in the students' writing. It is suggested that greater consideration should be given to the first and the target culture when designing the curricula for writing classes for Arab L1 students in English writing instruction.
Nonresident Undergraduates’ Performance in English Writing Classes— Hierarchical Linear Modeling Analysis