scholarly journals Analysis of Facebook in the Teaching-Learning Process about Mathematics Through Data Science

2021 ◽  
Vol 47 (2) ◽  
Author(s):  
Ricardo-Adán Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Mathematics Education ◽  
Learning Process ◽  
Data Science ◽  
Financial Mathematics ◽  
Communication Tool ◽  
Instructional Videos ◽  
Teaching Learning ◽  
The Impact

The aim of this quantitative research is to analyze the impact of Facebook in the teaching-learning process in financial mathematics education, using data science, machine learning, and neural networks. The sample is composed of 46 students from the Bachelor of Administration, Commerce and Marketing program at La Salle University. The results of machine learning (linear regression) indicate that sending messages, watching instructional videos, and publishing exercises on Facebook supports the teaching-learning process in financial mathematics. Likewise, data science identified six predictive models for the use of Facebook in the educational context, by means of the decision tree technique. Analysis using neural networks identified the influence of sending messages, watching instructional videos, and publishing exercises on Facebook during the assimilation of knowledge and development of mathematical skills. Finally, Facebook is a technological and communication tool that transforms the organization of teaching and learning activities in financial mathematics education.

Impact of the WampServer application in Blended learning considering data science, machine learning, and neural networks

2020 ◽  
Vol 17 (3) ◽  
pp. 199-217 ◽  
Author(s):  
Ricardo-Adán Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Blended Learning ◽  
Learning Process ◽  
Data Science ◽  
Educational Process ◽  
School Activities ◽  
Web Programming ◽  
Teaching Learning ◽  
The Impact

This quantitative research aims to analyze the impact of the WampServer application in Blended learning during the educational process of computing through data science, machine learning, and neural networks. WampServer is a free application that allows the creation of websites considering the use of the database. This research proposes the use of Blended learning in the Development of applications subject in order to facilitate the teaching–learning process in the Database unit. The students discuss and reflect the concepts on the database in the classroom and carry out various school activities on the construction of websites at home through the use of the WampServer application. The sample consists of 28 students who took the Development of applications subject during the 2016 school year. The results of machine learning (linear regression) with 50, 60, and 70% of training indicate that the use of PHP, HTML, and SQL languages in the WampServer application positively influences the assimilation of knowledge and development of skills on web programming. Data science identifies six predictive models about the use of WampServer in the educational process of computing. On the other hand, neural networks determine the factors that influence the assimilation of knowledge and development of skills on web programming. This research recommends the incorporation of the WampServer application in the school activities related to the computer field to create new educational spaces and facilitate the teaching–learning process. Teachers can transform the educational context through the organization and realization of creative activities inside and outside the classroom. Finally, the use of WampServer in Blended learning allows creating new spaces for teaching and learning of computer science because this application favors the assimilation of knowledge and development of skills on web programming.

scholarly journals Design, Construction and Evaluation of a Web Application for the Teaching-Learning Process on Financial Mathematics

2020 ◽  
Vol 15 (08) ◽  
pp. 100
Author(s):  
Ricardo-Adán Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Learning Process ◽  
Web Application ◽  
Quantitative Research ◽  
Data Science ◽  
Active Role ◽  
Financial Mathematics ◽  
Design Development ◽  
Teaching Learning ◽  
The Impact

This quantitative research aims to analyze the impact of the Web Application for the Teaching-Learning process on Simple Discount (WATLSD) through data science and machine learning (linear regression). The sample is composed of 42 students of the careers in Administration and Marketing who attended the Financial Mathematics course during the 2018 school year. The ADDIE model allows organizing the construction of the WATLSD through the stages of Analysis, Design, Development, Implementation and Evaluation. The results of machine learning indicate that the use of the WATLSD during the learning process positively influences the motivation, active role and development of mathematical skills. Likewise, data science establishes 3 predictive models on the use of the WATLSD in the educational field. Finally, advances in technology such as the WATLSD allow the creation of new virtual spaces for learning and teaching.

scholarly journals Flipped classroom: Pedagogical model necessary to improve the participation of the students during the learning process

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Ricardo-Adán Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Learning Process ◽  
Flipped Classroom ◽  
Data Science ◽  
School Activities ◽  
School Year ◽  
Business Course ◽  
And Performance ◽  
Teaching Learning ◽  
The Impact

Nowadays, teachers can transform the organization and realization of school activities before, during and after the face-to-face sessions through the flipped classroom. The objective of this mixed research is to analyze the impact of the flipped classroom in the teaching-learning process on statistics considering data science and machine learning (linear regression). The sample consists of 61 students who took the Statistical Instrumentation for Business course during the 2018 school year. This research proposes the consultation of the YouTube videos before the class, performance of the collaborative exercises and use of the spreadsheet to check the results during the class and performance of the laboratory practices through the spreadsheet after the class. The results of machine learning (70%, 80% and 90% of training) indicate that the participation of the students before, during and after the class positively influences the assimilation of knowledge and development of mathematical skills about the frequencies and measures of central tendency. On the other hand, the decision tree technique identifies 6 predictive models on the use of the flipped classroom. Also, the students of the Statistical Instrumentation for Business course are motivated and satisfied to use the technological tools in the Introduction to statistics Unit. Finally, the flipped classroom allows the construction of new educational spaces and creation of creative activities before, during and after the class that favor the participation of the students during the learning process.

scholarly journals USE OF THE FLIPPED CLASSROOM TO DESIGN CREATIVE AND ACTIVE ACTIVITIES IN THE FIELD OF COMPUTER SCIENCE

2020 ◽  
Vol 13 (1) ◽  
pp. 136-151
Author(s):  
Ricardo Adán Salas Rueda
Keyword(s):  
Machine Learning ◽  
Computer Science ◽  
Quantitative Research ◽  
Flipped Classroom ◽  
Data Science ◽  
Collaborative Work ◽  
Educational Process ◽  
School Activities ◽  
Teaching Learning ◽  
The Impact

The objective of this quantitative research is to analyze the impact of the flipped classroom in the educational process on computer science considering data science and machine learning. This study proposes the consultation of YouTube videos (before class), collaborative work through MySQL software (during class) and individual work through MySQL software (after class) in the database subject. The results of machine learning (linear regression) indicate that school activities before, during and after the class positively influence the assimilation of knowledge and development of skills on the administration of the database. Likewise, data science identifies 6 predictive models on the use of the flipped classroom in the educational process by means of the decision tree technique. Finally, the flipped classroom improves the teaching-learning conditions through the performance of creative and active activities.

scholarly journals Diseño y uso de una aplicación web para el campo de la estadística considerando el modelo ASSURE y la ciencia de datos

2019 ◽  
Vol 12 (1) ◽  
pp. 48-71
Author(s):  
Ricardo-Adán Salas-Rueda ◽  
Érika-Patricia Salas-Rueda ◽  
Rodrigo-David Salas-Rueda
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Data Science ◽  
Hypothesis Test ◽  
Web Design ◽  
Positive Influence ◽  
Educational Process ◽  
Data Simulation ◽  
Teaching Learning ◽  
The Impact

RESUMEN: Esta investigación mixta tiene como objetivo analizar el impacto de laAplicación web para el Proceso Educativo sobre la Prueba de Hipótesis (APEPH)en la asignatura Instrumentación estadística para los negocios durante el ciclo escolar 2018. El modelo instruccional ASSURE permite la organización, construcción e implementación de la aplicación APEPH. Los resultados del aprendizaje automático (regresión lineal) con 60%, 70% y 80% de entrenamiento indican que el contenido, el diseño web y la simulación de datos en la aplicación APEPH influyen positivamente en el aprendizaje y la motivación del estudiante. Asimismo, la ciencia de datos permite la construcción de 6 modelos predictivos sobre el uso de la aplicación APEPH en el proceso educativo por medio de la técnica árbol de decisión. Por último, la aplicación APEPH facilita el proceso de enseñanza-aprendizaje sobre la estadística por medio del contenido, el diseño web y la simulación de datos. PALABRAS CLAVE: tecnología educativa; modelo ASSURE; enseñanza superior; ciencia de datos; aprendizaje automático.   ABSTRACT: This mixed research aims at analyzing the impact of the Web Application for the Educational Process on the Hypothesis Test (APEPH) in the Statistical Instrumentation for Business subject during the 2018 school year. ASSURE model allows the organization, construction and implementation of the APEPH application. The results of machine learning (linear regression) with 60%, 70% and 80% of training indicate that the content, web design and simulation of data in the APEPH application have a positive influence on the student's learning and motivation. Likewise, data science allows the construction of 6 predictive models on the use of the APEPH application in the educational process by means of the decision tree technique. Finally, the APEPH application facilitates the teaching-learning process on statistics through the content, web design and data simulation. KEYWORDS: educational technology; ASSURE model; higher education; data science; machine learning.

scholarly journals Representing Deep Neural Networks Latent Space Geometries with Graphs

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 39
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Objective Function ◽  
Learning Process ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The Core ◽  
Learning Tasks ◽  
Latent Space

Deep Learning (DL) has attracted a lot of attention for its ability to reach state-of-the-art performance in many machine learning tasks. The core principle of DL methods consists of training composite architectures in an end-to-end fashion, where inputs are associated with outputs trained to optimize an objective function. Because of their compositional nature, DL architectures naturally exhibit several intermediate representations of the inputs, which belong to so-called latent spaces. When treated individually, these intermediate representations are most of the time unconstrained during the learning process, as it is unclear which properties should be favored. However, when processing a batch of inputs concurrently, the corresponding set of intermediate representations exhibit relations (what we call a geometry) on which desired properties can be sought. In this work, we show that it is possible to introduce constraints on these latent geometries to address various problems. In more detail, we propose to represent geometries by constructing similarity graphs from the intermediate representations obtained when processing a batch of inputs. By constraining these Latent Geometry Graphs (LGGs), we address the three following problems: (i) reproducing the behavior of a teacher architecture is achieved by mimicking its geometry, (ii) designing efficient embeddings for classification is achieved by targeting specific geometries, and (iii) robustness to deviations on inputs is achieved via enforcing smooth variation of geometry between consecutive latent spaces. Using standard vision benchmarks, we demonstrate the ability of the proposed geometry-based methods in solving the considered problems.

scholarly journals Jupyter: Thinking and Storytelling with Code and Data

2021 ◽  
Author(s):  
Brian Granger ◽  
Fernando Pérez
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Community Of Practice ◽  
Scientific Computing ◽  
Data Science ◽  
Climate Science ◽  
Three Dimensions ◽  
Open Source Project ◽  
Interactive Computing ◽  
The Impact

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: interactive computing, computational narratives, and  the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in Earth and climate science.

scholarly journals Machine Learning Methods Applied for Modeling the Process of Obtaining Bricks Using Silicon-Based Materials

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7232
Author(s):  
Costel Anton ◽  
Silvia Curteanu ◽  
Cătălin Lisa ◽  
Florin Leon
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Raw Materials ◽  
Optimization Procedure ◽  
Exhaust Emission ◽  
Energy Potential ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Emission Changes ◽  
The Impact

Most of the time, industrial brick manufacture facilities are designed and commissioned for a particular type of manufacture mix and a particular type of burning process. Productivity and product quality maintenance and improvement is a challenge for process engineers. Our paper aims at using machine learning methods to evaluate the impact of adding new auxiliary materials on the amount of exhaust emissions. Experimental determinations made in similar conditions enabled us to build a database containing information about 121 brick batches. Various models (artificial neural networks and regression algorithms) were designed to make predictions about exhaust emission changes when auxiliary materials are introduced into the manufacture mix. The best models were feed-forward neural networks with two hidden layers, having MSE < 0.01 and r2 > 0.82 and, as regression model, kNN with error < 0.6. Also, an optimization procedure, including the best models, was developed in order to determine the optimal values for the parameters that assure the minimum quantities for the gas emission. The Pareto front obtained in the multi-objective optimization conducted with grid search method allows the user the chose the most convenient values for the dry product mass, clay, ash and organic raw materials which minimize gas emissions with energy potential.

Computer-Based Mathematics Education

2021 ◽  
pp. 28-51
Keyword(s):  
Mathematics Education ◽  
Programming Languages ◽  
Technological Diffusion ◽  
Electronic Technology ◽  
Chronological Order ◽  
Computer Based ◽  
Teaching Learning ◽  
The Impact ◽  
Modern Technologies

In this chapter, the authors mention, briefly, the attempts made from the 1970s to today to insert modern technologies in the teaching/learning of mathematics. They start with the first pocket calculators in the 1970s, which had a rapid technological diffusion that still exists. They focus on the impact that digital electronic technology has had on teaching/learning math. They will not follow a strictly chronological order, preferring to dwell on what, in their opinion, are the fundamental stages. So, the advent of the PC and programming languages—Logo, Basic, Pascal—CAI programs, DGS software, CAS. They conclude with their MatCos Project, after mentioning the new coding languages, including Scratch.

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