Analysis of Educational Data in the Current State of University Learning for the Transition to a Hybrid Education Model

Data Analysis Model for the Evaluation of the Factors That Influence the Teaching of University Students

Currently, the effects of the pandemic caused by the Coronavirus disease discovered in 2019 are the subject of numerous studies by experts in labor, psychological issues, educational issues, etc. The universities, for their continuity, have implemented various technological tools for the development of their activities, such as videoconference platforms, learning management systems, etc. This experience has led the educational sector to propose new educational models, such as hybrid education, that focus on the use of information technologies. To carry out its implementation, it is necessary to identify the adaptability of students to a technological environment and what the factors are that influence learning. To do this, this article proposes a data analysis framework that identifies the factors and variables of a hybrid teaching environment. The results obtained allow us to determine the level of influence of educational factors that affect learning by applying data analysis algorithms to profile students through a classification based on their characteristics and improve learning methodologies in these educational models. The updating of educational systems requires a flexible process that is aligned with the needs of the students. With this analysis framework, it is possible to create an educational environment focused on students and allows for efficient change with the granular analysis of the state of the learning.

Proposal for the Analysis of the State of Learning in University Students with the Inclusion of ICT in the Classroom

The inclusion of information and communication technologies in education has become a priority for all universities. To meet this need, there are several research works that have dealt with the subject for several decades. However, for its inclusion, the analysis of each institution is necessary since the needs of the university population and the resources for its application change according to each situation. This work seeks to create a method that allows establishing the needs and doubts of students about the use of educational technologies in the classroom without affecting their performance. For this, a process has been designed that identifies learning needs, through the validation of data obtained from surveys and the monitoring of the academic efficiency and learning of a cohort of students. The follow-up includes a period of four years from 2019 to 2022. This follow-up allowed establishing three different realities, in 2019 the academic data was analyzed in a face-to-face education model, from 2020 to 2021 the follow-up was included in a remote model with the use of technologies as a communication channel and in 2022 these were included as a learning component, which marked an in-depth analysis of student performance and how technology affected their learning.

Identification of the Consequences of COVID-19 through the Analysis of Data Obtained in Surveys of a Specific Population

The pandemic caused by the 2019 coronavirus disease has marked a total change in the development of society. Since then, its effects have been visible in people, both in work, education and psychological areas. There are many jobs and organizations that have set out to identify the reality of people after the pandemic and how the pandemic has affected their daily lives. To do this, countries have organized data and statistics collection campaigns that allow investigating the new needs of people. With this, instruments such as surveys have become more relevant and valid to know what these needs are. However, the analysis processes must guarantee answers that are able to determine the direct impact that each question has on people’s feelings. This work proposes a framework to determine the incidence values of surveys based on their categories and questions and how they capture the reality of people in areas such as education, the impact of work, family and the stress generated by the pandemic. With the results obtained, each element and category that the population considers a consequence of COVID-19 that affects the normal development of life has been identified.

Proposal of a remote education model with the integration of an ICT architecture to improve learning management

University education is at a critical moment due to the pandemic generated by the Coronavirus Disease 2019. Universities, to guarantee the continuity of education, have considered it necessary to modify their educational models, implementing a transition towards a remote education model. This model depends on the use of information and communication technologies for its execution and the establishment of synchronous classes as a means of meeting between teachers and students. However, moving from face-to-face classes to online classes is not enough to meet all the needs of students. By not meeting the needs and expectations of students, problems are generated that directly affect learning. In this work, Big data and artificial intelligence are integrated as a solution in a technological architecture that supports the remote education model. This integration makes it possible to identify the state of learning and recommend immediate actions to its actors. Teachers, knowing the variables that affect academic performance, have the ability to change the components of learning or the method used. Improving learning and validating the capacity of information technologies to generate digital environments suitable for the generation of knowledge. In addition to improving the functionality of educational models and their adaptability to the new normal.

Investigating the Attitudes of First-Year Students of the Faculty of Physical Education and Sports of Galati towards Online Teaching Activities during the COVID-19 Pandemic

The paper identifies the perceptions of first-year undergraduate students from the Faculty of Physical Education and Sports in Galati on online teaching activities, dominant and needful in the current pandemic context. The questionnaire used contains 23 items and was structured based on four distinctive factors, namely attractiveness, accessibility, motivation and efficiency; it was applied after the winter session of the academic year 2020–2021. The values of the internal consistency coefficient Cronbach’s alpha indicate for the four mentioned factors a high fidelity for the measurements of the investigated features. The results of the 147 completed questionnaires allowed the definition of the independent variables sex (boys and girls) and environment of origin (rural and urban) the identification of their influence on the scores of each item (dependent variables) by using the statistical technique MANOVA (multivariate and univariate analysis), besides the values of F and the corresponding significance thresholds; the magnitude of the effect, expressed by partial eta squared (η2p), was also calculated. Even if the averages of item scores differ between sexes and backgrounds, the differences noted are in few cases significant: attractiveness and socialization for those in urban areas; participation in activities and effective involvement for girls; technical deficiencies, platform logging and weak computer skills for those in rural areas; and an increase in free time for girls and students in urban areas. The study undertaken allows the identification of the favorable aspects and the shortcomings of online teaching activities, these being the premises for optimizing the teaching process in the following stages.