DIAGNOSTIC ANALYSIS OF GENERAL ENGLISH READING COMPREHENSION'S ITEMS OF PHD ENTRANCE EXAM USING NON-COMPENSATORY FUSION MODEL

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MOGHADAM AZAM; FALSAFI NEJAD MOHAMMAD REZA; FARROKHI NOOR ALI; ESTAJI MASUME

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Journal: Educational Measurement Year:2016 | Volume:6 | Issue:22 Page(s): 41-68

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Title

DIAGNOSTIC ANALYSIS OF GENERAL ENGLISH READING COMPREHENSION'S ITEMS OF PHD ENTRANCE EXAM USING NON-COMPENSATORY FUSION MODEL

Author(s)

MOGHADAM AZAM | FALSAFI NEJAD MOHAMMAD REZA | FARROKHI NOOR ALI | ESTAJI MASUME | Issue Writer Certificate

Keywords

COGNITIVE DIAGNOSTIC MODELS (CDMS)Q1

NON-COMPENSATORY FUSION MODEL (FM)Q1

READING COMPREHENSION'S SKILLSQ1

AND ENGLISH LANGUAGEQ2

Abstract

Traditional approaches in educational measurement have some practical and theoretical challenges in demonstrating language competencies and their abilities in assessment candidates' skills and selecting them have been questioned. In order to overcome these restrictions cognitive diagnostic models (CDMs) have been introduced and applied.Objective: The purpose of this study was diagnostic analysis of reading comprehension items of a general English language test (PhD entrance exam) to investigate underlying skills of a given test, inspection of model convergence and its fit, diagnostic power of the test and the mastery status of examinees.Method: The study conducted in cognitive diagnostic modeling. The population was all PhD candidates which majored in English teaching, linguistics, translation, and English literature. 2754 examinees were used as a sample. Task analysis, coding and verbal reports were applied to determine underlying skills of the test.Results: In qualitative section, 6 skills including using vocabulary knowledge, using syntactic knowledge, extracting explicit information or scan, drawing inference, connecting and synthesizing and using pragmatic knowledge were investigated. Also, quantitative analyses using non-compensatory reduced fusion model (FM) based on a Monte Carlo Markov chain (MCMC) indicated MCMC convergence and model fit and possibility of application of fusion model in English language's tests. The ability parameters were low for all skills. Using vocabulary knowledge was the simplest skill. The mean of item proportion-correct scores was. 42 and the test did not have a high diagnostic power.Discussion and conclusion: Using cognitive diagnostic models in general and fusion model in particular results in achieving more information about tests and examinees' responses and it helps to reach the goal of assessment for learning and classify examinees as masters or non-masters correctly.

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APA: Copy

MOGHADAM, AZAM, FALSAFI NEJAD, MOHAMMAD REZA, FARROKHI, NOOR ALI, & ESTAJI, MASUME. (2016). DIAGNOSTIC ANALYSIS OF GENERAL ENGLISH READING COMPREHENSION'S ITEMS OF PHD ENTRANCE EXAM USING NON-COMPENSATORY FUSION MODEL. TRAINING MEASUREMENT, 6(22 ), 41-68. SID. https://sid.ir/paper/214542/en

Vancouver: Copy

MOGHADAM AZAM, FALSAFI NEJAD MOHAMMAD REZA, FARROKHI NOOR ALI, ESTAJI MASUME. DIAGNOSTIC ANALYSIS OF GENERAL ENGLISH READING COMPREHENSION'S ITEMS OF PHD ENTRANCE EXAM USING NON-COMPENSATORY FUSION MODEL. TRAINING MEASUREMENT[Internet]. 2016;6(22 ):41-68. Available from: https://sid.ir/paper/214542/en

IEEE: Copy

AZAM MOGHADAM, MOHAMMAD REZA FALSAFI NEJAD, NOOR ALI FARROKHI, and MASUME ESTAJI, “DIAGNOSTIC ANALYSIS OF GENERAL ENGLISH READING COMPREHENSION'S ITEMS OF PHD ENTRANCE EXAM USING NON-COMPENSATORY FUSION MODEL,” TRAINING MEASUREMENT, vol. 6, no. 22 , pp. 41–68, 2016, [Online]. Available: https://sid.ir/paper/214542/en

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