Identification of the Factors That Influence University Learning with Low-Code/No-Code Artificial Intelligence Techniques

Evaluation of influencing factors of China university teaching quality based on fuzzy logic and deep learning technology

Nowadays, colleges and universities focus on the assessment model for considering educational offers, suitable environments, and circumstances for students' growth, as well as the influence of Teaching Quality (TQ) and the applicability of the skills promoted by teaching to life. Teaching excellence is an important evaluation metric at the university level, but it is challenging to determine it accurately due to its wide range of influencing factors. Fuzzy and Deep Learning (DL) approaches must be could to build an assessment model that can precisely measure the teaching qualities to enhance accuracy. Combining fuzzy logic and DL can provide a powerful approach for assessing the influencing factors of college and university teaching effects by implementing the Sequential Intuitionistic Fuzzy (SIF) assisted Long Short-Term Memory (LSTM) model proposed. Sequential Intuitionistic Fuzzy (SIF) can be used sets to assess factors that affect teaching quality to enhance teaching methods and raise the standard of education. LSTM model to create a predictive model that can pinpoint the primary factors that influence teaching quality and forecast outcomes in the future using those influencing factors for academic growth. The enhancement of the SIF-LSTM model for assessing the influencing factors of teaching quality is proved by the accuracy of 98.4%, Mean Square Error (MSE) of 0.028%, Tucker Lewis Index (TLI) measure for all influencing factors and entropy measure of non-membership and membership degree correlation of factors related to quality in teaching by various dimensional measures. The effectiveness of the proposed model is validated by implementing data sources with a set of 60+ teachers' and students' open-ended questionnaire surveys from a university.

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 of a Method for the Analysis of Sentiments in Social Networks with the Use of R

Decision making is vital for the management of all organizations. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. However, data generation is not limited to traditional sources. On the contrary, emerging technologies and social networks have become non-traditional sources that provide large volumes of data that can be exploited using different data analysis methods. Here, the objective is to determine the feelings of the population toward a brand, a product, or a service and to even identify the reactions of people to events and trends generated in their environment. Sentiment analysis, for organizations and social groups, has become a necessity that must be covered to identify the acceptance of an idea or its management. Therefore, this work proposes a method for the analysis of sentiment in social networks in such a way that it adapts to the needs of organizations or sectors, and the acceptance or rejection of the population can be efficiently identified from what is exposed in a social network.

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.

Bridging Theory and Practice Using Facebook: A Case Study

In the past few years, Facebook has been increasingly studied for academic purposes due to its potential benefits to undergraduate education. Problems commonly found in university education include the lack of course engagement and the gap between theory and practice. This research contributes to the literature by investigating the impact of incorporating a non-mandatory Facebook group on learning outcomes. The analysis was done using a Taguchi method design, conducted with three two-level controlled factors (term, Facebook, and teacher). Results indicated that the students who participated in Facebook groups were more engaged with the course and applied theoretical knowledge to real-life problems better than students who learned under traditional instructional designs. Moreover, the use of Facebook groups led to better evaluation of teachers by the students. Furthermore, the student academic impact (knowledge and competence) was higher even though this complementary activity was not included in the course grade. We concluded that Facebook groups are excellent support tools that boost student engagement and their understanding of theoretical concepts and applying them in practice.

OLP—A RESTful Open Low-Code Platform

Low-code is an emerging concept that transforms visual representations into functional software, allowing anyone to be a developer. However, building a low-code platform from scratch can be challenging concerning the scarce available literature about the topic. In this sense, this paper proposes an Open Low-Code Platform (OLP), a low-code solution that enables regular users to create applications. Furthermore, it presents low-code’s functional and nonfunctional requirements, as well as its similarities and its differences with the no-code concept. The experience obtained while developing OLP was translated into a pipeline that details how code was transformed from the visual representations into a fully fledged application. The paper demonstrates the solution’s viability and is especially useful for building a low-code platform from scratch or improving an existing one.