فاطمه نیک مرد

Listening comprehension is a challenging skill in EFL contexts and it is necessary to research various aspects of this skill. Therefore, this study tried to investigate EFL learners’ progress on two types of listening comprehension items, receptive-response and productive-response, through diagnostic assessment (DIA) and dynamic assessment (DA). To do this, a Nelson proficiency test was administered among 120 EFL students in six classes, out of which, 90 students whose scores were in the acceptable range were selected. The classes were then randomly divided into three groups of control, DIA, and DA. As the pretest, a KET listening test in two sections of receptive-response and productive-response items was used to check the learners’ initial performance. Next, as for the treatment, in the DIA group, the learners took four listening tests with receptive-response and productive-response items in the form of test-feedback; in the DA group, the learners took the same four tests in the form of test-mediation-retest, and in the control group, the learners practiced receptive-response and productive-response items after each listening activity. At the end of the study, another KET listening test in two sections of receptive-response and productive-response items was administered to the participants to investigate the three groups’ progress from pretest to posttest. Finally, two repeated-measures two-way ANOVAs and one MANOVA were run, and the results showed that both DIA and DA resulted in significant improvements in the learners’ performance on both receptive-response and productive-response listening items; however, the difference between the two types of assessment was not significant.

To explore the characteristics of the items of the Teaching English as a Foreign Language (TEFL) MA Admission Test (henceforth TMAAT) as a high-stakes test in Iran, the current research utilized a three-parameter logistic Item Response Theory (IRT) calibration of the test items. The three-parameter logistic IRT model is the most comprehensive among the three models of IRT for it takes into account all the three effective parameters of item difficulty, item discrimination, and guessing simultaneously. The data were a random selection of 1000 TMAAT candidates taking the test in 2020 collected from Iran’s National Organization of Educational Testing (NOET). The software used to analyze the data was jMetrik (Version 4.1.1), which is the newest version so far. As the results indicated, the TMAAT worked well in discriminating the higher and lower ability candidates and preventing the candidates from guessing the responses by chance, but it was not much acceptable regarding the difficulty level of the items as the items were far too difficult for the test-takers. The most important beneficiaries of the present investigation are test developers, testing experts, and policy-makers in Iran since they are responsible to improve the quality of the items in such a high-stakes test.