Laylo Djuraeva

Feeling anxiety and technostress can have harmful effects on EFL learners' language learning. On the other hand, having high academic enjoyment (AE) can generate positive impacts on English language learning. Concerning the psychological variables' role in English language learning, this investigation inspected the effects of digital literacy (DL) on technostress, foreign language anxiety (FLA), AE, and language achievement. To meet this objective, 70 intermediate subjects were selected and divided into an experimental group (EG) and a control group (CG). Next, the pre-tests of technostress, FLA, AE, and language learning achievement were administered to both groups. After that, the EG was taught the coursebook using technology. DL was also focused on it, and the students were trained to apply technology when learning new English materials. On the other hand, the same lessons were taught to the CG applying a traditional method. After teaching all classes, the mentioned scales were administered as the posttests of the study. The results of the One-way ANCOVA test indicated that the EG outdid the CG in the four posttests, implying that using DL reduced the anxiety and technostress of the EG but increased their AE. The ramifications of this investigation can inspire both EFL learners and educators to apply digital tools and technology in English learning and teaching.

Cloze tests are commonly used in language testing as a quick measure of overall language ability or reading comprehension. A problem for the analysis of cloze tests with item response theory models is that cloze test items are locally dependent. This leads to the violation of the conditional or local independence assumption of IRT models. In this study, a new modeling strategy is suggested to circumvent the problem of local item dependence in cloze tests. This strategy involves identifying locally dependent items in the first step and combining them into polytomous items in the second step. Finally, partial credit model is applied to the combination of dichotomous and polytomous items. Our findings showed that the new strategy results in a better model-data fit than the dichotomous model where dependence is ignored but with a lower reliability. Results also indicated that the person and item parameters from the two models highly correlate. The findings are discussed in light of the literature on managing local dependence in educational tests.