The main aim of this study was to investigate whether certain variables namely school attachment, perceived social support, cognitive flexibility and gender can predict the resilience of senior high school students that are economically disadvantaged. Another aim of the research is to reveal the relation between the level of academic resilience and gender variable. This study also seeks to examine the differences between academically resilient students with different levels of resilience in terms of the stated variables. For these purposes, a quantitative research method, a correlational research model, was used. The participants consisted of 304 senior students enrolled in 18 Anatolian High Schools during the 2014-2015 academic year in the three districts of the Ankara province, Turkey. Data was collected using ‘the Resilience Scale for Adults’, ‘School Attachment Scale for Children and Adolescents’, ‘the Multidimensional Scale of Perceived Social Support’, ‘Cognitive Flexibility Inventory’ and the student information form which was designed by the authors. To analyze the collected data, a Standard Multi Regression Analysis, a Multivariate Analysis of Variance (MANOVA) and a Chi-square Test were employed. The results of the Multi Regression Analysis revealed that cognitive flexibility and perceived social support significantly predicted the level of academic resilience of economically disadvantaged high school students. However, school attachment was not found to be a significant predictor. The three variables given above accounted for approximately 41% of variance in the resilience scores of the participants. Furthermore, the results obtained from MANOVA showed no difference between students with high and low academic resilience in terms of cognitive flexibility, school attachment and perceived social support. Moreover, there was a significant relationship between the two groups in terms of gender according to the results of the Chi-square Test.

The Woodcock-Johnson IV Tests of Cognitive Abilities (WJ IV COG) is a comprehensive assessment battery designed to assess broad and narrow cognitive abilities, as defined by the Cattell-Horn-Carroll (CHC) theory of intelligence. Previous studies examined the invariance of the WJ assessments across sex and age groups using factor analytic methods. Psychometric network modeling is an alternative methodology that can address both direct and indirect relationships among the observed variables. In this study, we employed psychometric network modeling to examine the invariance of the WJ IV COG across sex and age groups. Using a normative sample (n = 4212 participants) representative of the United States population, we tested the extent to which the factorial structure of the WJ IV COG aligned with CHC theory for the school-aged sample. Next, we used psychometric network modeling as a data-driven method to investigate whether the network structure of the WJ IV COG remains similar across different sex and age (age 6 to 19, inclusively) groups. Our results showed that the WJ IV COG maintained the same network structure across all age and sex groups, although the network structure at younger ages indicated weaker relationships among some subtests. Overall, the results provide construct validity evidence for the WJ IV COG, based on both theoretical and data-driven methods.

The purpose of this study was to carry out a content analysis on the papers published in high-impact educational journals between 2009 and 2014 and to identify the trends over the selected years. The criteria for the analysis were; number of authors, time between the submission and publication of the papers, keywords, the field and rationale of the study, sample size, descriptions of participants, data collection tools and analysis, and software. The current study was designed as a descriptive content analysis study and utilized a purposive sampling technique. A total of 789 papers were selected from the “Journal of Educational Psychology”, “Educational Psychologist”, “Educational Researcher” and “American Education Research Journal”. Content analysis was employed to analyze the collected data. The results of the analyses showed that the most commonly studied fields were educational psychology, linguistic properties and mathematics in the four journals. In terms of the content, the rationales for writing the papers were generally related to gaps in the literature and theoretical discussion. It was found that generally, studies involving research were conducted with elementary/high school students and data was collected from large samples (larger than 10000) using achievement tests and questionnaires. It was revealed that concerning the trends in data analysis methods, there was a similar pattern from 1970s to date, and generally, multilevel modelling was used when appropriate to the data sets. This situation indicates standard data analyses are essential for researchers. From the findings of the current study, it is recommended for researchers to work with heterogenous sample and various types of participants together (family, teacher, peer, etc.).

This study aimed to assess the relationships between response times (RTs), the number of actions taken to solve a given item, and student performance. In addition, the interaction between the students’ information and communications technology (ICT) competency, reading literacy, and log data (time and number of actions) were examined in order to gain additional insights regarding the relations between student performance and log data. The sample consisted of 2 348 students who participated in the triennial international large-scale assessment of the Programme for International Student Assessment (PISA). For the current study, 18 items in the one cluster of the 91st booklet were chosen. To achieve the aim of the study, explanatory item response modeling (EIRM) framework based on generalized linear mixed modeling (GLMM) was used. The results of this study showed that students who spent more time on items and those that took more actions on items were more likely to answer the items correctly. However, this effect did not have variability across items and students. Moreover, the interaction only with reading and the number of actions was found to have a positive effect on the students’ overall performance.

Educational data mining (EDM) has been a rapidly growing research field over the last decade and enabled researchers to discover patterns and trends in education with more sophisticated methods. EDM offers promising solutions to complex educational problems. Given the rapid increase in the availability of big data in education and software programs to analyze big data, the demand for user-friendly, free software programs to implement EDM methods also continues to increase. The R programming language has become a popular environment for data mining due to its availability and flexibility. The rattle package in R contains a set of functions to implement data mining with a graphical user interface. This study demonstrates three widely used data mining algorithms (classification and regression tree, random forest, and support vector machine) in EDM using real data from the 2015 administration of the Programme for International Student Assessment (PISA). First, a brief introduction to EDM is provided along with the description of the selected data mining algorithms. Then, how to perform data mining analysis using the rattle’s graphical user interface is demonstrated. The study concludes by comparing the results of the selected data mining algorithms and highlighting how those algorithms can be utilized in the context of educational research.