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The levels of Self-Perceived and Observed Information Literacy for a sample of in-service teachers and future teachers of Primary and Secondary Education are analysed as a whole and by its components of Searching for, Evaluating, Processing and Communicating Information. To do so, two validated tools are used which enable us to obtain Self-Perceived Information Literacy levels (through a self-assessment questionnaire) and Observed Information Literacy levels (through performance measures) from 442 in-service teachers and future teachers of 7 educational institutions in 4 provinces of Castile and Leon (Spain). The results of the descriptive and inferential analyses show that the Self-Perceived Information Literacy is overestimated compared to the Observed Information Literacy for all groups, especially among future Primary Education teachers, with the in-service Secondary Education teachers showing the least difference between self-perception and performance. The Observed Information Literacy is at its best level among the Secondary Education teachers and lowest level among the future Primary Education teachers. At each educational level the performance of in-service teachers is always higher than the performance of future teachers, indicating the preponderance of experience versus the generational effect. The components of the Observed Information Literacy with the lowest values for all groups are Searching for and Evaluating Information and, therefore, specific training activities are recommended. [ABSTRACT FROM AUTHOR]

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental, cross-sectional design and a sample of 18,935 high school students from 99 educational institutions in Baja California state (Mexico). The information was collected from ENLACE tests and context surveys given to students in Baja Caclifornia. Decision trees were used to apply classification techniques, and the results indicate that personal factors are most indicative of academic performance, followed by school-related and social factors. In conclusion, the paper discusses the similarities between the results obtained and those shown in literature, highlighting how simple decision trees allow a greater explanation and interpretation than other models and techniques. [ABSTRACT FROM PUBLISHER]

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]

• Decision trees enable the detection of factors that predict school effectiveness. • The predictive model is more accurate for low effectiveness schools. • A single negative factor at school escalates the risk of low effectiveness. With the main goal of identifying the process factors associated with school effectiveness in secondary education, this work presents an innovative methodological proposal. Based on secondary data from the Spanish sample of PISA 2015, high- and low-effectiveness schools were selected by analysing the residuals of the school level (level 2) in multilevel models. Subsequently, decision trees were used to identify the process variables with a greater predictive power for the identification of high- and low-effectiveness schools. While the hierarchical linear models obtained show inter-school variance scores greater than 10%, decision trees achieve a precision greater than 90%. We conclude by analysing the suitability of using decision trees in data originating from large-scale assessments, and examining the obtained factors associated with school effectiveness. [ABSTRACT FROM AUTHOR]