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Machine learning using linear regression shows that sending messages, viewing instructional videos, and posting exercises support teaching-learning outcomes. Data science identifies six predictive models via decision trees, and neural network analysis highlights effects on knowledge assimilation and mathematical skill development.",{"@graph":14,"@context":77},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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Teaching-Learning Process about Mathematics Through Data Science  \nAnalyse de Facebook dans le processus d'enseignement-apprentissage des mathématiques par la science des données  \nRicardo-Adán Salas-Rueda, Universidad Nacional Autónoma de México  \nAbstract  \nThe aim of this quantitative research is to analyze the impact of Facebook in the teachinglearning 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.  \nKeywords: Facebook; social network ; learning ; data science ; neural networks  \nRésumé  \nL’objectif de cette recherche quantitative est d’analyser l’impact de Facebook dans le processus éducatif sur les mathématiques financières à travers la science des données, l’apprentissage automatique et les réseaux de neurones. L'échantillon est composé de 46 étudiants du baccalauréat en administration, commerce et marketing à l'Université La Salle. Les résultats de l'apprentissage automatique (régression linéaire) indiquent que l'envoi de messages, la consultation de vidéos et la publication d'exercices sur Facebook favorisent le processus d'enseignement-apprentissage en mathématiques financières. Demême, la science des données a identifié six modèles prédictifs sur l'utilisation de Facebook dans le  \nCJLT/RCAT Vol. 47 (2)  \ncontexte éducatif à l'aide de la technique de l'arbre de décision. Les réseaux de neurones ont identifié l'influence de l'envoi de messages, de la consultation de vidéos et de la publication d'exercices sur Facebook lors de l'assimilation des connaissances et du développement des compétences en mathématiques. Enfin, Facebook est un outil technologique et de communication qui transformel'organisation des activités scolaires dans le domaine des mathématiques financières.  \nMots-clés : Facebook ; réseau social ; apprentissage ; data science ; réseaux de neurones  \nIntroduction  \nInformation and communication technologies (ICTs) are causing radical changes in the planning and organization of the educational activities (Ben-Chayim & Offir, 2019; Hannigan & Gonzalez, 2019; Salas-Rueda, 2019) . Therefore, teachers seek, select, and use technological applications to achieve the development of competences among their students (Alizadeh et al., 2019; Arteaga-Sánchez et al., 2014; Salas-Rueda et al., 2019) .  \nInteractive web tools are modifying the behaviour and functions of students in educational contexts (Hershkovitz & Forkosh-Baruch, 2019; Llorens & Capdeferro, 2011; Reed, 2013) . Social networks improve participation, communication, and interaction during the teaching-learning process (Maheshwari & Mukherjee, 2021; Stankov et al., 2012). Nowadays, teachers use social networking sites to build learning spaces on the Internet (Rambe, 2012; Teo et al., 2018; Vivakaran & Maraimalai, 2019) . For example, Facebook facilitates interaction and communication among students during the educational process through discussion, analysis, and reflection (Akcaoglu & Lee, 2018; Mukhlif & Challob, 2021; Rambe & Ngambi, 2014). Within","cbCailelt2pA7hMR","https://ap.wps.com/l/cbCailelt2pA7hMR","pdf",317660,"English","# Introduction\n## ICTs and social networks in education\n## Facebook in higher education\n## Study aim and research questions","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To analyze the impact of Facebook in the teaching-learning process in financial mathematics education using data science, machine learning, and neural networks.\"},{\"question\":\"How many students participated, and from which program?\",\"answer\":\"The sample includes 46 students from the Bachelor of Administration, Commerce and Marketing program at La Salle University.\"},{\"question\":\"Which Facebook activities were found to support teaching-learning in financial mathematics?\",\"answer\":\"Sending messages, watching instructional videos, and publishing exercises were shown to support the teaching-learning process.\"},{\"question\":\"What modeling methods were used to evaluate Facebook’s role?\",\"answer\":\"The study used linear regression for machine learning, decision-tree techniques for data science predictive models, and neural networks to analyze influences on knowledge assimilation and mathematical skills.\"}]","Analysis of Facebook in the Teaching-Learning Process about Mathematics Through Data Science - Abstract | PDF",1792086649]