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Several studies have been published on disengaged test respondents, and others have analyzed disengaged survey respondents separately. For many large-scale assessments, students answer questionnaire and test items in succession. This study examines the percentage of students who continuously engage in disengaged responding behaviors across sections in a low-stakes assessment. The effects on calculated scores of filtering students, based on their responding behaviors, are also analyzed. Data of this study came from the 2015 administration of PISA. For data analysis, frequencies and percentages of engaged students in the sessions were initially calculated using students' response times. To investigate the impact of filtering disengaged respondents on parameter estimation, three groups were created, namely engaged in both measures, engaged only in the test, and engaged only in the questionnaire. Next, several validity checks were performed on each group to verify the accuracy of the classifications and the impact of filtering student groups based on their responding behavior. The results indicate that students who are disengaged in tests tend to continue this behavior when responding to the questionnaire items in PISA. Moreover, the rate of continuity of disengaged responding is non-negligible as can be seen from the effect sizes. On the other hand, removing disengaged students in both measures led to higher or nearly the same performance ratings compared to the other groups. Researchers analyzing the dataset including achievement tests and survey items are recommended to review disengaged responses and filter out students who are continuously showing disengaged responding before performing further statistical analysis.

In this study, we investigated factors predictive of disconnection, or not being in education, employment, or training (NEET), among young adults with at least a 2-year college degree. We also explored the extent to which disconnection influences civic participation and well-being among NEETs with and without college degrees. The authors used 2012 and 2015 Organisation for Economic Co-operation and Development (OECD) data from the Survey of Adult Skills in the Program for the International Assessment of Adult Competencies (PIAAC) for 29 countries, including the United States, along with US 2012 data from the Education Longitudinal Study of 2002 (ELS:2002), collected by the National Center for Education Statistics (NCES). Results highlight that college-educated individuals whose parents have low levels of educational attainment actually have a higher likelihood of becoming NEET relative to college-educated individuals whose parents are more highly educated. Study findings also emphasize the influence of economic and geographic differences (country-level for OECD and county-level for United States) on NEET rates, in addition to the extent to which mothers have a higher likelihood and fathers have a lower likelihood of being NEET relative to their childless peers and the influence of country-level family leave policies on the odds of being NEET across the OECD. College field of study also emerges as an important influence on disconnection across the 29 OECD countries in the study, but not in the United States separately. Finally, comparing results for college-educated NEETs and NEETs without degrees, we found that higher education appears to reduce the likelihood of community disengagement and reports of poor health among NEETs across the OECD countries. However, this is not the case within the United States where NEETs are less likely to be engaged in their communities and more likely to describe themselves as in poor health regardless of their educational attainment.

Education is the core of the factors that improved people for a better lifestyle and increases the level of society' development. Quality education is one of the most vital goals of Sustainable Development Goals (SDGs) due to actualizing these factors. Using relational network data envelopment analysis (DEA), which have three interrelated substages, this current paper computes the educational economy efficiency of the Organisation for Economic Co-operation and Development (OECD) countries bearing in mind the characteristics related to SDGs. The contribution of our study is the use of a novel approach to computing the educational economy efficiency using relational network DEA with GAMS. Even though some interesting differences reveal in the efficiency of the countries, the findings show that countries with high-efficiency scores are clustered around countries like Latvia, Slovenia, and Korea.

This article focuses on analysing the effect of educational differentiation policies of OECD educational systems on socioeconomically disadvantaged students, based on data from PISA 2009. The analysis is conducted on the basis of a definition of two subgroups of disadvantaged students: those that achieve high scores, and those obtaining scores that are significantly below the average for their country. Educational systems are classified in four models following the classification proposed by N. Mons. Results show that the more integrated the educational system, the greater the number of disadvantaged high achievers, and the lower the number of disadvantaged low achievers.

A recent compilation of educational indicators from 20 industrialized countries in North America, the Pacific, Europe, and Scandinavia shows rates and trends in college enrollment and graduation, education-related earning power for men and women, and public spending on education. Educational attainment rates are charted for the 20 nations. (MSE)