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Copyright of Revista Electrónica Interuniversitaria De Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The assumption of cause-effect relationships in ex post facto research is a widely known issue in the field of research methods in social sciences. To address this important limitation, the use of causal inference techniques has become widespread in recent years. Causal inference establishes a set of statistical procedures for drawing causal conclusions in non-experimental research. Despite its wide popularity and diffusion in the social and health sciences, its use in educational research is still marginal. Thus, this paper introduces the main causal inference techniques available to the educational researcher when observational panel data are available. After addressing the key features and potential of propensity score matching, instrumental variables, and regression discontinuity design, we present an example application of each of these techniques. We used the available databases from the PISA 2018 assessments. We included the mathematical competence as the dependent variable in all the three models implemented. Given the different characteristics of each of these techniques, the independent variable used is different in the three models applied: attendance to early childhood education in propensity score matching; student academic expectations in instrumental variables; and size of the community in which the school is located in regression discontinuity design. The article concludes by discussing the potential of this set of techniques, taking into account the needs and methodological procedures most commonly applied in educational research. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Journal of Educational Research, Assessment & Evaluation / Revista Electrónica de Investigación y Evaluación Educativa is the property of RELIEVE - Revista Electonica de Investigacion y Evaluacion Educativa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Professional development for teachers is fundamental in the configuration and functioning of smart schools. This paper aims to characterize professional development with the participation of compulsory secondary teachers in Spain and to detect key factors in the functioning and organization of schools associated with higher levels of ongoing teacher training. A cross-cutting non-experimental design was used to conduct a secondary analysis of data from PISA 2018 tests, including over 20,000 teachers and more than 1000 schools in Spain. Descriptive results show great variability in teachers' commitment to their professional development; this variability is not associated with the grouping of teachers by school. The decision tree model completed with data mining tools shows that intensive professional teacher development in schools is associated with a better school climate and higher levels of innovation, cooperation, taking on shared goals and responsibilities, and leadership distributed among the education community. The conclusions highlight the importance of ongoing teacher training and how this improves educational quality in schools. [ABSTRACT FROM AUTHOR]

Background: Computer-based programs have been implemented from a psychosocial approach for the care of people with dementia (PwD). However, several factors may determine adherence of older PwD to this type of treatment. The aim of this paper was to identify the sociodemographic, cognitive, psychological, and physical-health determinants that helped predict adherence or not to a "GRADIOR" computerized cognitive training (CCT) program in people with mild cognitive impairment (MCI) and mild dementia. Method: This study was part of a randomized clinical trial (RCT) (ISRCTN: 15742788). However, this study will only focus on the experimental group (n = 43) included in the RCT. This group was divided into adherent people (compliance: ≥60% of the sessions and persistence in treatment up to 4 months) and non-adherent. The participants were 60–90 age and diagnosed with MCI and mild dementia. We selected from the evaluation protocol for the RCT, tests that evaluated cognitive aspects (memory and executive functioning), psychological and physical health. The CCT with GRADIOR consisted of attending 2–3 weekly sessions for 4 months with a duration of 30 min Data analysis: Phi and Biserial-point correlations, a multiple logical regression analysis was obtained to find the adherence model and U Mann–Whitney was used. Results: The adherence model was made up of the Digit Symbol and Arithmetic of Wechsler Adult Intelligence Scale (WAIS-III) and Lexical Verbal Fluency (LVF) -R tests. This model had 90% sensitivity, 50% specificity and 75% precision. The goodness-of-fit p-value of the model was 0.02. Conclusions: good executive functioning in attention, working memory (WM), phonological verbal fluency and cognitive flexibility predicted a greater probability that a person would be adherent. [ABSTRACT FROM AUTHOR]

Copyright of Revista Española de Documentación Científica is the property of Consejo Superior de Investigaciones Cientificas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Interuniversitaria de Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Alteridad: Revista de Educación is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Electrónica Interuniversitaria De Formación del Profesorado is the property of Asociacion Universitaria de Formacion del Profesorado (AUFOP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

International large-scale assessments, such as PISA, provide structured and static data. However, due to its extensive databases, several researchers place it as a reference in Big Data in Education. With the goal of exploring which factors at country, school and student level have a higher relevance in predicting student performance, this paper proposes an Educational Data Mining approach to detect and analyze factors linked to academic performance. To this end, we conducted a secondary data analysis and built decision trees (C4.5 algorithm) to obtain a predictive model of school performance. Specifically, we selected as predictor variables a set of socioeconomic, process and outcome variables from PISA 2018 and other sources (World Bank, 2020). Since the unit of analysis were schools from all the countries included in PISA 2018 (n = 21,903), student and teacher predictor variables were imputed to the school database. Based on the available student performance scores in Reading, Math, and Science, we applied k-means clustering to obtain a categorized (three categories) target variable of global school performance. Results show the existence of two main branches in the decision tree, split according to the schools' mean socioeconomic status (SES). While performance in high-SES schools is influenced by educational factors such as metacognitive strategies or achievement motivation, performance in low-SES schools is affected in greater measure by country-level socioeconomic indicators such as GDP, and individual educational indicators are relegated to a secondary level. Since these evidences are in line and delve into previous research, this work concludes by analyzing its potential contribution to support the decision making processes regarding educational policies. [ABSTRACT FROM AUTHOR]

The study of school effectiveness and the identification of factors associated with it are growing fields of research in the education sciences. Moreover, from the perspective of data mining, great progress has been made in the development of algorithms for the modeling and identification of non-trivial information from massive databases. This work, which falls within this context, proposes an innovative approach for the identification and characterization of educational and organizational factors associated with high school effectiveness. Under a perspective of basic research, our aim is to study the suitability of decision trees, techniques inherent to data mining, to establish predictive models for school effectiveness. Based on the available Spanish sample of the PISA 2015 assessment, an indicator of the school effectiveness was obtained from the application of multilevel models with predictor variables of a contextual nature. After selecting high- and low-effectiveness schools in this first phase, the second phase of the study was carried out and consisted of the application of decision trees to identify school, teacher, and student factors associated with high and low effectiveness. The C4.5 algorithm was calculated and, as a result, we obtained 120 different decision trees based on five determining factors (database used; stratification in the initial selection of schools; significance of the predictor variables of the models; use of items and/or scales; and use of the training or validated samples). The results show that the use of this kind of technique could be appropriate if mainly used with correctly pre-processed data that include the combined information available from all educational agents. This study represents a major breakthrough in the study of the factors associated with school effectiveness from a quantitative approach, since it proposes and provides a simple and appropriate procedure for modeling and establishing patterns. In doing so, it contributes to the development of knowledge in the field of school effectiveness that can help in educational decision-making. [ABSTRACT FROM AUTHOR]

Copyright of Profesorado: Revista de Currículum y Formación del Profesorado is the property of Profesorado: Revista de Curriculum y Formacion del Profesorado and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The statistical multivariate analysis of Likert response scales, given their widespread use, is a controversial issue in the scientific community, mainly from the specification of the problem of measurement. This work aims to study various conditions of these ordinal scales affect the calculation of the product-moment and tetrachoric-polychoric correlation coefficients. For this purpose, a simulation study was applied in which 90 databases with 10 items each were generated. In the estimation of the databases, the following variables were controlled: number of response categories, symmetrical or asymmetric distributions of data, sample size and level of relationship between items. Thus, 90 matrices (10x10) were obtained which included the difference between the product-moment and tetrachoric-polychoric correlations. The graphical and variance analysis show how the product-moment correlation coefficient significantly underestimates the relationship between variables mainly when the number of response categories of the ordinal scale is small and the relationship between the variables is large. On the other hand, the statistical estimation of both coefficients is very similar when the starting relationship between pairs of variables is small and/or when the number of response options of the variables is greater than 5. The study concludes by making a recommendation to the applied researcher on the most appropriate correlation coefficient depending on the type of data available. Finally, the results are discussed from the previous studies, which reach some similar conclusions. [ABSTRACT FROM AUTHOR]

Copyright of RIED: Revista Iberoamericana de Educación a Distancia is the property of Revista Iberoamericana de Educacion a Distancia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The multivariate characteristics of the IL-HUMASS scale are studied. This scale is formed by 26 Likert scale items and 4 dimensions and evaluate the information literacy self-perceived level of students. A sample of 402 subjects, all of them Spanish future teachers from secondary education, is obtained. It is implemented a multidimensional scaling technique by ALSCAL based on the distance matrix from the data. The results detected two empirical dimensions, which accounted for 95.9% of the variance. The first dimension is related to the level of familiarity with the content of the item and the second dimension is related to cognitive or instrumental aspect of the item. In the conclusions, two dimensions obtained are connected with the original scale, in relation to the four theoretical dimensions. On the other hand, the conclusions highlight the importance of considering the two dimensions obtained, in addition to the classical dimensions, in the design of assessment scales of the construct information literacy. [ABSTRACT FROM AUTHOR]

Copyright of Teoría de la Educación. Educación y Cultura en la Sociedad de la Información is the property of Ediciones Universidad de Salamanca and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study is to analyze the positive relationship between different dimensions (knowledge, attitudes, and ability) of the Climate-Change Competence in the participants of a Massive Open Online Course called "Awareness and Training on Climate Change for Primary and Secondary Teachers". This study describes the use of this competence to introduce Climate Change into formal education and provides an example of how it can be used to design educational interventions to mobilize the students through education. We carried out a correlational research design based on mediation and moderation models using a process macro for questionnaires about the Climate-Change Competence. In this study, we used a sample of 530 people from Spain and Latin America (52% female, mean age = 36.1 years). The findings revealed that knowledge about Climate Change is a good predictor of ability and attitude. Furthermore, we predicted that the relationship between knowledge and ability would be mediated by attitude. Likewise, we hypothesized that attitude is a moderating dimension between knowledge and ability. The results supported our prediction and showed that attitude is a strong mediator in the relationship between knowledge and ability. However, the interaction between knowledge and attitude did not improve the ability to cope with Climate Change. The Climate-Change Competence is an efficient tool to introduce Climate Change into formal education. It can also be used to investigate, for the first time, the relationship between knowledge, ability, and attitude, which is essential to transform education into a necessary tool for mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Objective: To validate Rowland Dementia Assessment Scale (RUDAS), as an instrument for the screening of people with dementia and cognitive impairment in Primary Health Care (PHC). RUDAS is a brief cognitive test, appropriate for people with minimum completed level of education and easily adaptable to multicultural contexts. For these reason it could be a good instrument for dementia screening in PHC., Design: Cross-sectional descriptive epidemiological study with a five-year follow up., Location: O Grove PHC centre, Galicia, Spain (covering a population of 10,650 individuals)., Outcome Measures: RUDAS; Mini Mental State Examination; Clinical Dementia Rating; Katz, Barthel and Lawton Indexes; MMSE and Yesavage Geriatric Depression Scale., Participants: A total of 150 older adults (mean age 76.35±7.12years) randomly selected, from a low sociocultural and economical background and mainly rural and semirural origin., Intervention: RUDAS viability in PHC was checked, and its psychometric properties assessed: reliability, sensitivity, specificity, positive and negative predictive values., Results: RUDAS application was brief (7.58±2.10min) and well accepted. RUDAS area under receiver operating characteristic (ROC) curve for the detection of dementia was 0.983 (95% confidence interval (CI): 0.97-1.00) for an optimal cut-off point of 22.5, with sensitivity of 89.3%, and a specificity of 100%. Area under ROC curve for discriminating dementia from mild cognitive impairment was 0.965 (95%CI: 0.91-1.00)., Conclusions: RUDAS test is fit for dementia screening in PHC and it is especially sensitive to discriminate PWD from people with MCI., (Copyright © 2021 The Authors. Publicado por Elsevier España, S.L.U. All rights reserved.)