The research's objective is to evaluate the differential effect that a metacognitive scaffolding for information web searches has on learning achievement of high school students with different cognitive style in the field dependence and independence dimension and on learning style in the dimension proposed by Honey and Alonso known as CHAEA. One hundred and four students from a school in the city of Bogotá, Colombia participated in the study. The research was quasi-experimental and was conducted with three 10th-grade groups, which worked with three scaffolding versions: fixed, optional, and without scaffolding. A multivariate analysis of covariance established that the fixed scaffolding favored learning achievement. Regarding cognitive style in the field dependence and independence dimension, the findings allow to conclude that the field independent students exhibited better

The use of imagined speech with electroencephalographic (EEG) signals is a promising field of brain-computer interfaces (BCI) that seeks communication between areas of the cerebral cortex related to language and devices or machines. However, the complexity of this brain process makes the analysis and classification of this type of signals a relevant topic of research. The goals of this study were: to develop a new algorithm based on Deep Learning (DL), referred to as CNNeeg1-1, to recognize EEG signals in imagined vowel tasks; to create an imagined speech database with 50 subjects specialized in imagined vowels from the Spanish language (/a/,/e/,/i/,/o/,/u/); and to contrast the performance of the CNNeeg1-1 algorithm with the DL Shallow CNN and EEGNet benchmark algorithms using an open access database (BD1) and the newly developed database (BD2). In this study, a mixed variance analysis of variance was conducted to assess the intra-subject and inter-subject training of the proposed algorithms. The results show that for intra-subject training analysis, the best performance among the Shallow CNN, EEGNet, and CNNeeg1-1 methods in classifying imagined vowels (/a/,/e/,/i/,/o/,/u/) was exhibited by CNNeeg1-1, with an accuracy of 65.62% for BD1 database and 85.66% for BD2 database.

No abstract

During the last two decades, interpersonal regulation in natural and digital learning environments has gained importance. Ever since the first conceptual and methodological precisions regarding collaborative learning were made, educational psychology has focused its interest on analyzing collective regulation of motivation, cognition, and behavior. Despite the fact that the body of research on co-regulation has grown, emerging epistemological frameworks evidence a lack of conceptual and theoretical clarity. In response to this situation, the authors propose a conceptual approach in order to address interpersonal regulation in four aspects: first, they describe three learning theories which have been used to study co-regulation. Second, the authors recommend a conceptual delimitation of terms regarding the learning theories on social regulation. Third, they highlight diffuse boundaries between theoretical approaches and terms used in the literature on co-regulation. Finally, the authors suggest some challenges the researchers in this field face.

No abstract