The aim of this study is to compare 2018 Science Course Curriculum (SCC), 2015 Trends in International Mathematics and Science Study (TIMSS) and 2018 High School Entrance Examination (HSE) in terms of content domains, cognitive domains and learning objectives. Qualitative research method, was used in this study. Data were analyzed using document review matrices to determine the similarities and differences between the objectives of SCC, TIMSS and HSE. SCC outcomes and HSE science questions were also classified according to TIMSS cognitive domains. Results show that the learning objectives of the fields of Physics, Biology and Earth Sciences of TIMSS are compatible with those of all grade levels of SCC and that the objectives of Chemistry are compatible with those of the seventh and eighth grades. Most of HSE questions are compatible with the objectives of SCC, however, the latest revision in the curriculum has introduced some eighth grade objectives to other grade levels. HSE science questions measure higher-level skills than TIMSS science questions. The subject domain of the “Organisms and Life” of SCC has the most learning objectives in the levels of “knowing” and “reasoning” while the subject domain of the “Physical Events” has the most learning objectives in the levels of “applying.” Besides, the seventh-, fifth- and eighth-graders have the most objectives in the levels of “knowing,” “applying,” and “reasoning,” respectively. It is hoped that the results will contribute the literature in improvement of science curricula and interpretation of national and international exams.

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.

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.

Due to the pandemic in many countries, schools were closed in 2020. Therefore, education was suspended, and distance education was started. During the Coronavirus disease (COVID-19) pandemic, teachers gave lessons online in virtual classrooms. In this study, Scale of Attitudes Towards Online Formative Assessment (S-AOFA) for teachers conducting online and distance courses was developed, and the teachers' attitudes were examined with respect to demographic variables. In the study conducted in the mixed-design method, qualitative and quantitative data were collected for the scale development and survey. Data were obtained from 369 teachers (science teacher, mathematics teacher, classroom teacher, and teachers in other fields) working in school in Turkey. S-AOFA was made up of 20 items and two factors as a five-point Likert-type. When the teachers' attitudes towards online formative assessment (AOFA) was examined, it was found that the mean for the factor of "Assessment Systems" was lower than that of the "Assessment Approaches". In addition, the findings revealed that there was no significant difference in the teachers' AOFA in terms of gender and that no significant difference existed in AOFA with respect to the school levels of the teachers (elementary, secondary and high school). Moreover, the results demonstrated that there was a low negative significant relationship between the teachers' AOFA and the number of students in which virtual lessons were given online. Lastly, there was a low level positive significant relationship between the teachers' AOFA and the in-class participation percentages of the students who were taught online in virtual classrooms. S-AOFA could be used by researchers in different studies in future.

This study reviews 60 papers using a Likert scale and published between 2012 – 2021. Screening for literature review uses the PRISMA method. The data analysis technique was carried out through data extraction, then synthesized in a structured manner using the narrative method. To achieve credible research results at the stage of the data collection and data analysis process, a group discussion forum (FGD) was conducted. The findings show that only 10% of studies use a measurement scale with an even answer choice category (4, 6, 8, or 10 choices). In general, (90%) of research uses a measurement instrument that involves a Likert scale with odd response choices (5, 7, 9, or 11) and the most popular researchers use a Likert scale with a total response of 5 points. The use of a rating scale with an odd number of responses of more than five points (especially on a seven-point scale) is the most effective in terms of reliability and validity coefficients, but if the researcher wants to direct respondents to one side, then a scale with an even number of responses (six points) is possible. more suitable. The presence of response bias and central tendency bias can affect the validity and reliability of the use of the Likert scale instrument.

Anger is a topic that requires intervention from teachers, counsellors, psychologists, parents, and all communities. The expressions of anger are subjective and sometimes hard to identify. Thus, anger should be measured more objectively, while the expressions need to be examined closely. The purpose of this study is to provide valid confirmation for development of an online instrument to measure the types of anger expression among adolescents. Data were collected from 935 adolescents from nine schools in northern Malaysia and the theoretical literature search. The data were analysed to provide evidence of construct validity in terms of item factor analysis, reliability estimates, and correlation between the types of anger expressions. Findings were used to develop an online Adolescent Anger Instrument. It measures five types of anger expressions, namely, physical, verbal, intrinsic, extrinsic, and passive. The results showed that the instrument is internally consistent with high evidence of construct validity. Exploratory factor analysis, with varimax rotation, suggested the existence of five distinct types of anger as conceptualised. Meanwhile, the correlation between types of anger expressions indicates the strength of the relationship between them. Discussions on the findings are provided, while suggestions for future research are also described.

The role of artificial intelligence (AI) in education remains incompletely understood, demanding further evaluation and the creation of robust assessment tools. Despite previous attempts to measure AI's impact in education, existing studies have limitations. This research aimed to develop and validate an assessment instrument for gauging AI effects in higher education. Employing various analytical methods, including Exploratory Factor Analysis, Confirmatory Factor Analysis, and Rasch Analysis, the initial 70-item instrument covered seven constructs. Administered to 635 students at Nueva Ecija University of Science and Technology – Gabaldon campus, content validity was assessed using the Lawshe method. After eliminating 19 items through EFA and CFA, Rasch analysis confirmed the construct validity and led to the removal of three more items. The final 48-item instrument, categorized into learning experiences, academic performance, career guidance, motivation, self-reliance, social interactions, and AI dependency, emerged as a valid and reliable tool for assessing AI's impact on higher education, especially among college students.