Comparative Judgement for Linking Two Existing Scales

This article describes an efficient way of using comparative judgement to calibrate scores from different educational assessments against one another (a task often referred to as test linking or equating). The context is distinct from other applications of comparative judgement as there is no need to create a new achievement scale using a Bradley-Terry model (or similar). The proposed method takes advantage of this fact to include evidence from the largest possible number of examples of students’ performances on the separate assessments whilst keeping the amount of time required from expert judges as low as possible. The paper describes the method and shows, via simulation, how it achieves greater accuracy than alternative approaches to the use of comparative judgement for test equating or linking.

The comparison of the scores obtained by Bayesian nonparametric model and classical test theory methods

Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.

English as a Foreign Language (EFL) learning assessment in single-sex and co-educational classrooms

This study investigates the effects of Single-Sex and Co-educational classrooms on English learning outcomes. This study is a causal-comparative study in ex post facto design. Three classes consisting of 73 students (boy and girl-single-sex classes and a co-educational class) from a private secondary school in Central Java, Indonesia, are the sample. An integrated English test, equating the material from the 2013 curriculum and CEFR for English, was used. It tested four skills in English consisting of Listening, Speaking, Reading and Writing. Expert judgments already checked the instrument through face validity. Item internal consistency from all skills was good, and the reliability was too in a good category. This study indicates that organizing a Single-sex classroom in the English learning process has a positive and significant effect on English achievement. Being in a Single-Sex classroom benefitted the students in their outcomes in learning English. This study also implies that teachers, especially English teachers, must understand their students' learning strategies to implement the appropriate learning strategies. It is because male and female students learn something in different ways.

Asymptotic Standard Errors of Parameter Scale Transformation Coefficients in Test Equating Under the Nominal Response Model

Researchers have developed a characteristic curve procedure to estimate the parameter scale transformation coefficients in test equating under the nominal response model. In the study, the delta method was applied to derive the standard error expressions for computing the standard errors for the estimates of the parameter scale transformation coefficients. This brief report presents the results of a simulation study that examined the accuracy of the derived formulas and compared the performance of this analytical method with that of the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion standard errors as well as those yielded by the multiple imputation method under all the simulation conditions.