Multidisciplinary Journal of Educational Research

Evaluation of Emotional Competence the ComunicARTE Program in Language and Literature

Concha Iriarte — 2024-10-15

The present study evaluates the emotional contents of the Spanish Language and Literature program ComunicARTE and the impact they have on the development of the socioemotional competence of students in the second cycle of Primary Education. The contents of the program were analyzed based on a theoretical model of Socioemotional Competence and a 2-year study was carried out with a pre-posttest evaluation in 5 experimental groups and 2 control groups (n=170). From the qualitative analysis of the program, it can be seen that emotional competence and through it the development of linguistic competence is explicitly worked on. Linear regression analysis indicates that the ComunicARTE program has a significant impact on the development of emotional competence of the students in the experimental groups, counteracting its starting point, which is lower than the measure, especially in boys. Although the improvement is observed in all dimensions of emotional competence, emotional regulation, emotional autonomy and life and wellbeing competences are especially improved. The Program turns out to be a good material to integrate the development of emotional competence in the school timetable through the subject of Language and Literature.

Effectiveness of Artificial Intelligence Models for Predicting School Dropout: A Meta-Analysis

Jacobo Roda-Segarra — 2024-10-15

School dropout is a major concern in the educational systems of all countries. In recent years, artificial intelligence is playing an important role in predicting school dropout in the different educational stages of formal education. In this context, it is crucial to know that these predictions are accurate and understandable. This meta-analytic study aims to investigate the effectiveness of dropout prediction models conducted until May 2022. The databases used are Web of Science, Scopus, PubMed, ERIC, PsyInfo, Dialnet and Scielo. 15 studies with a sample size of 199,015 participants are analyzed. The meta-analysis uses a random-effects proportions model with 95% confidence interval. Statistical evidence indicates that artificial intelligence models performed well (91%; 95% CI = 89-93%) in predicting dropout; specifically, the Decision Tree model significantly (95.3%; 95% CI = 93-98%) predicts dropout better than other models such as Random Forest, Artificial Neural Network, Support Vector Machines, Logistic Regression and Stacking Ensemble. Consequently, more models should be applied in the dropout field with larger numbers of participants to confirm these findings and improve the quality of education.

Influence of Self-Regulation of Learning on the Academic Performance of Secondary Students

Pedro Ricardo Álvarez-Pérez — 2024-10-15

This work analyzes the capacity for self-regulation of learning in secondary and high school students and its influence on the completion of their academic tasks and academic performance. One of the key aspects of the student training process, at all educational stages, is self-regulation in learning, since at stake is the achievement of one's own educational goals. Self-regulation constitutes, from this perspective, a mediating element in the process of academic adaptation and in the prevention of early school leaving. Following an ex post-facto empirical-analytical method, the objective of the study was to analyze whether the self-regulation process followed by ESO and Baccalaureate students influenced their academic performance. Starting from a socio-constructivist perspective and taking into consideration the phases of Zimmerman's (2002) cyclical model for the self-regulation of learning, a questionnaire was developed and applied to a sample of 309 secondary and high school students. The results obtained showed that there is a relationship between self-regulation of learning and performance in studies. Specifically, those students who obtained better academic grades achieved high levels of self-regulation of learning, in its three main dimensions: planning, execution and self-evaluation.

Measurement of Education Effectiveness in the BRICS countries and Turkey

Süreyya İmre-Bıyıklı — 2024-10-15

The high literacy rate in a country shows that educational attainment is progressing well and efficiently. In this study, we aim to compare the effectiveness of education in the BRICS countries - Brazil, India, Russia, China, South Africa - and Turkey. We use the Stochastic Frontier Analysis method to measure the technical efficiency of education. The study utilizes 2017 and 2018 data, and linear functions to examine technical efficiency. The dataset comprises cross-sectional data. As a result, South Africa and India show the lowest technical efficiency scores, followed by China. Russia, Brazil, and Turkey scored high on technical efficiency for 2017 and 2018. This means that education levels in South Africa and India are unequal and shared among all residents. The 2017 and 2018 productivity scores are the same at 0.99% in Turkey, Russia, and Brazil. India's productivity score increased by 0.10% to 0.55% in 2018. Likewise, the productivity score calculated for China decreased by an average of 0.07 percent to 0.70 percent. However, South Africa's productivity score fell by 0.08 percent to 0.01 percent in 2018.