Teacher-made tests (TMT) are the most used instruments for assessment and evaluation. This study investigates the cognitive requirements, test construction errors, and item types of TMTs. Content analysis technique is used in order to analyze and classify TMT items based on TIMSS-2019 assessment framework and based on criteria that is constructed to determine test construction errors. The data is consisted of 548 items in 30 exam papers of 18 mathematics teachers from 13 distinct schools. The distribution of TIMSS-2019 cognitive demands of all TMTs indicates that there is a strong emphasis on knowing or applying cognitive domains, with a total percentage of 93. Since 83% of all questions are of multiple choice and 17% are constructed-response type, teachers mostly prefer multiple choice item type. Findings also reveal that except face validity, there are errors concerning test constructions. Consequently, it is suggested that teachers should give more care on preparing items of higher cognitive levels, on tests of mixed type items, and on tests that involve lesser construction errors for more reliable tests. Finally, it is also suggested that measurement and evaluation specialists should be employed in each school or in each local Ministry of National Education Authority at least, in order to support teachers, but if this is not possible in a close time, there must be in-service training programs on measurement and evaluation for teachers to participate in.

Pearson product–moment correlation coefficient between item g and test score X, known as item–test or item–total correlation (Rit), and item–rest correlation (Rir) are two of the most used classical estimators for item discrimination power (IDP). Both Rit and Rir underestimate IDP caused by the mismatch of the scales of the item and the score. Underestimation of IDP may be drastic when the difficulty level of the item is extreme. Based on a simulation, in a binary dataset, a good alternative for Rit and Rir could be the Somers’ D: it reaches the ultimate values +1 and –1, it underestimates IDP remarkably less than Rit and Rir, and, being a robust statistic, it is more stable against the changes in the data structure. Somers’ D has, however, one major disadvantage in a polytomous case: it tends to underestimate the magnitude of the association of item and score more than Rit does when the item scale has four categories or more.

Kelley’s Discrimination Index (DI) is a simple and robust, classical non-parametric short-cut to estimate the item discrimination power (IDP) in the practical educational settings. Unlike item–total correlation, DI can reach the ultimate values of +1 and ‒1, and it is stable against the outliers. Because of the computational easiness, DI is specifically suitable for the rough estimation where the sophisticated tools for item analysis such as IRT modelling are not available as is usual, for example, in the classroom testing. Unlike most of the other traditional indices for IDP, DI uses only the extreme cases of the ordered dataset in the estimation. One deficiency of DI is that it suits only for dichotomous datasets. This article generalizes DI to allow polytomous dataset and flexible cut-offs for selecting the extreme cases. A new algorithm based on the concept of the characteristic vector of the item is introduced to compute the generalized DI (GDI). A new visual method for item analysis, the cut-off curve, is introduced based on the procedure called exhaustive splitting.

Humans have had to immigrate from one country to another throughout the history because of economic problems, warfare, safety, etc. Warfare and migration definitely bring about traumatic incidents for all humanity. However, they are much more destructive for children. The current study aims to review the warfare-and-migration-themed drawings of the Syrian and Palestinian children living in Turkey as refugees in comparison to the Turkish children’s drawings. A case study design was employed as a qualitative methodology. 19 Syrian, 6 Palestinian and 25 Turkish children aged between 6-10 years studying in various primary schools in City of Istanbul made up the study sample. Children were asked to draw pictures of warfare and migration, observed and interviewed for data collection. Content analysis was used to determine the themes present in the qualitative data. The themes identified in the drawings were discussed referring to the literature and recommendations were made.