Aisha Mohammed

Aisha Mohammed


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A Cognitive Diagnostic Assessment Study of the Reading Comprehension Section of the Preliminary English Test (PET)

کلید واژه ها: B1 Preliminary English test reading attributes G-DINA Compensatory non-compensatory

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تعداد بازدید : ۱۴۲ تعداد دانلود : ۱۳۰
Cognitive diagnostic models (CDMs) have received much interest within the field of language testing over the last decade due to their great potential to provide diagnostic feedback to all stakeholders and ultimately improve language teaching and learning. A large number of studies have demonstrated the application of CDMs on advanced large-scale English proficiency exams, such as IELTS, TOEFL, MELAB, and ECPE. However, too little attention has been paid to the utility of CDMs on elementary and intermediate high-stakes English exams. The current study aims to diagnose the reading ability of test takers in the B1 Preliminary test, previously known as the Preliminary English Test (PET), using the generalized deterministic input, noisy, “and” gate (G-DINA; de la Torre, 2011) model. The G-DINA is a general and saturated model which allows attributes to combine in both compensatory and non-compensatory relationships and each item to select the best model. To achieve the purpose of the study, an initial Q-matrix based on the theory of reading comprehension and the consensus of content experts was constructed and validated. Item responses of 435 test takers to the reading comprehension section of the PET were analyzed using the “G-DINA” package in R. The results of attribute profiles suggested that lexico-grammatical knowledge is the most difficult attribute, and making an inference is the easiest one.

Distractor Analysis in Multiple-Choice Items Using the Rasch Model

کلید واژه ها: Distractor analysis Item response theory Multiple-choice items Rasch model

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تعداد بازدید : ۱۱۴ تعداد دانلود : ۸۰
Multiple-choice (MC) item format is commonly used in educational assessments due to its economy and effectiveness across a variety of content domains. However, numerous studies have examined the quality of MC items in high-stakes and higher education assessments and found many flawed items, especially in terms of distractors. These faulty items lead to misleading insights about the performance of students and the final decisions. The analysis of distractors is typically conducted in educational assessments with multiple-choice items to ensure high quality items are used as the basis of inference. Item response theory (IRT) and Rasch models have received little attention for analyzing distractors. For that reason, the purpose of the present study was to apply the Rasch model, to a grammar test to analyze items’ distractors of the test. To achieve this, the current study investigated the quality of 10 instructor-written MC grammar items used in an undergraduate final exam, using the items responses of 310 English as a foreign language (EFL) students who had taken part in an advanced grammar course. The results showed the acceptable fit to the Rasch model and high reliability. Malfunctioning distractors were identified.

Detecting Measurement Disturbance: Graphical Illustrations of Item Characteristic Curves

کلید واژه ها: Graphical displays item characteristic curves measurement disturbances model-data fit

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تعداد بازدید : ۱۰۰ تعداد دانلود : ۷۰
Measurement disturbances refer to any conditions that affect the measurement of some psychological latent variables, which result in an inaccurate interpretation of item or person estimates derived from a measurement model. Measurement disturbances are mainly attributed to the characteristics of the person, the properties of the items, and the interaction between the characteristics of the person and the features of the items. Although numerous researchers have detected measurement disturbances in different contexts, too little attention has been devoted to exploring measurement disturbances within the context of language testing and assessment, especially using graphical displays. This study aimed to show the utility of graphical displays, which surpass numeric values of infit and outfit statistics given by the Rasch model, to explore measurement disturbances in a listening comprehension test. Results of the study showed two types of outcomes for examining graphical displays and their corresponding numeric fit values: congruent and incongruent associations. It turned out that graphical displays can provide diagnostic information about the performance of test items which might not be captured through numeric values.

Evaluating Measurement Invariance in the IELTS Listening Comprehension Test

کلید واژه ها: Differential Item Functioning IELTS measurement invariance Rasch model

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تعداد بازدید : ۹۱ تعداد دانلود : ۷۵
Measurement invariance (MI) refers to the degree to which a measurement instrument or scale produces consistent results across different groups or populations. It basically shows whether the same construct is measured in the same way across different groups, such as different cultures, genders, or age groups. If MI is established, it means that scores on the test can be compared meaningfully across different groups. To establish MI mostly confirmatory factor analysis methods are used. In this study, we aim to examine MI using the Rasch model. The responses of 211 EFL learners to the listening section of the IETLS were examined for MI across gender and randomly selected subsamples. The item difficulty measures were compared graphically using the Rasch model. Findings showed that except for a few items, the IELTS listening items exhibit MI. Therefore, score comparisons across gender and other unknown subgroups are valid with the IELTS listening scores.

Examining Attribute Relationship Using Diagnostic Classification Models: A Mini Review

کلید واژه ها: Diagnostic Classification Models Attribute Relationship GDINA DINO DINA

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تعداد بازدید : ۹۱ تعداد دانلود : ۸۲
Diagnostic classification models (DCMs) have recently become very popular both for research purposes and for real testing endeavors for student assessment. A plethora of DCM models give researchers and practitioners a wide range of options for student diagnosis and classification. One intriguing option that some DCM models offer is the possibility of examining the nature of the interactions among the attributes underlying a skill. Attributes in second/foreign language (L2) may interact with each other in a compensatory/non-compensatory manner. Subskill/attribute relationship has been studied using diagnostic classification models. The present study provides a mini review of the DCM studies on the attribute relationships in L2 reading, listening, and writing. The criteria based on which interaction between the attributes have been inferred are reviewed. The results showed that the majority of DCM studies have investigated reading comprehension and more studies are required on the productive skills of writing and speaking. Furthermore, suggestions for future studies are provided.

Multidimensional IRT Analysis of Reading Comprehension in English as a Foreign Language

کلید واژه ها: Bifactor model Multidimensional IRT Reading Comprehension Unidimensional IRT

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تعداد بازدید : ۱۱۶ تعداد دانلود : ۶۸
Unidimensionality is an important assumption of measurement but it is violated very often. Most of the time, tests are deliberately constructed to be multidimensional to cover all aspects of the intended construct. In such situations, the application of unidimensional item response theory (IRT) models is not justified due to poor model fit and misleading results. Multidimensional IRT (MIRT) models can handle several dimensions simultaneously and yield person ability parameters on several dimensions which is helpful for diagnostic purposes too. Furthermore, MIRT models use the correlation between the dimensions to enhance the precision of the measurement. In this study a reading comprehension test is modelled with the multidimensional Rasch model. The findings showed that a correlated 2-dimensional model has the best fit to the data. The bifactor model revealed some interesting information about the structure of reading comprehension and the reading curriculum. Implications of the study for the testing and teaching of reading comprehension are discussed.

Psychometric Modelling of Reading Aloud with the Rasch Model

کلید واژه ها: Rasch partial credit model Reading aloud speaking test Validation

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تعداد بازدید : ۱۱۰ تعداد دانلود : ۷۳
Reading aloud is recommended as a simple technique to measure speaking ability (Hughes & Hughes, 2020; Madsen, 1983). Reading aloud is currently used in the Pearson Test of English and a couple of other international English as a second language proficiency tests. Due to the simplicity of the technique, it can be used in conjunction with other techniques to measure foreign and second language learners’ speaking ability. One issue in reading aloud as a testing technique is its psychometric modelling. Because of the peculiar structure of reading aloud tasks, analysing them with item response theory models is not straightforward. In this study, the Rasch partial credit model (PCM) is suggested and used to score examinees’ reading aloud scores. The performances of 196 foreign language learners on five reading aloud passages were analysed with the PCM. Findings showed that the data fit the RPCM well and the scores are highly reliable. Implications of the study for psychometric evaluation of reading aloud or oral reading fluency are discussed.

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