Review Article
Alez Lagos-Castillo, Andrés Chiappe, María-Soledad Ramirez-Montoya, Diego Fernando Becerra Rodríguez
CONT ED TECHNOLOGY, Volume 17, Issue 1, Article No: ep543
ABSTRACT
It may seem that learning platforms and systems are a tired topic for the academic community; however, with the recent advancements in artificial intelligence, they have become relevant to both current and future educational discourse. This systematic literature review explored platforms and software supporting personalized learning processes in the digital age. The review methodology followed PRISMA guidelines, searching Scopus and Web of Science databases. Results identified three main categories: artificial intelligence, platforms/software, and learning systems. Key findings indicate artificial intelligence plays a pivotal role in adaptive, personalized environments by offering individualized content, assessments, and recommendations. Online platforms integrate into blended environments to facilitate personalized learning, retention, and engagement. Learning systems promote student-centered models, highlight hybrid environments’ potential, and apply game elements for motivation. Practical implications include leveraging hybrid models, emphasizing human connections, analyzing student data, and teacher training. Future research directions involve comparative studies, motivational principles, predictive analytics, adaptive technologies, teacher professional development, cost-benefit analyses, ethical frameworks, and diverse learner impacts. Overall, the dynamic interplay between artificial intelligence, learning platforms, and learning systems offers a mosaic of opportunities for the evolution of personalized learning, emphasizing the importance of continuous exploration and refinement in this ever-evolving educational landscape.
Keywords: improving classroom teaching, data science applications in education, human-computer interface, learning communities, distributed learning environments
Research Article
Yiyun Fan, Kathlyn Elliott
CONT ED TECHNOLOGY, Volume 14, Issue 3, Article No: ep373
ABSTRACT
Educators have increasingly turned to social media for their instructional, social, and emotional needs during the COVID-19 pandemic. In order to see where support and professional development would be needed and how the educational community interacted online, we sought to use existing Twitter data to examine potential educators’ networking and discourse patterns. Specifically, this mixed-methods study explores how educators used Twitter as a platform to seek and share resources and support during the transition to remote teaching around the start of massive school closures due to the pandemic. Based on a public COVID-19 Twitter chatter database, tweets from late March to early April 2020 were searched using educational keywords and analyzed using social network analysis and thematic analysis. Social network analysis findings indicate that the support networks for educators on Twitter were sparse and consisted of mainly small, exclusive communities. The networks featured one-on-one interactions during the early pandemic, highlighting that there were few large conversations that most educators were part of but rather many small ones. Thematic analysis findings further suggest that both informational and nurturant support were relatively equally present on Twitter among educators, particularly pedagogical content knowledge and gratitude. This study adds to an understanding of the educational networks as a means of professional and personal support. Additionally, findings present the discourse featured in educator networks at the onset of an educational emergency (i.e., COVID-19) as decentralized as well as desiring pedagogical content knowledge and emotional sharing.
Keywords: data science applications in education, emergency online learning, Twitter, teacher professional development, social network analysis
Research Article
Ricardo-Adán Salas-Rueda, Ricardo Castañeda-Martínez, Ana-Libia Eslava-Cervantes, Clara Alvarado-Zamorano
CONT ED TECHNOLOGY, Volume 14, Issue 1, Article No: ep343
ABSTRACT
Technological advances such as Massive Open Online Courses (MOOCs) and Information and Communication Technologies (ICT) allow the construction of new spaces where students consult the information at any time, take the online exams and communicate with the participants of the educational process from anywhere. This quantitative research analyzes the perception of the teachers about the organization of the school activities in MOOCs and use of ICT considering machine learning and decision tree techniques (data science). The participants are 122 teachers (58 men and 64 women) from the National Autonomous University of Mexico who took the “Innovation in University Teaching 2020” Diploma. The academic degree of these educators is Bachelor (n = 35, 28.69%), Specialty (n = 4, 3.28%), Master (n = 58, 47.54%) and Doctorate (n = 25, 20.49%). The results of machine learning (linear regressions) indicate that the organization of the school activities in MOOCs positively influences the motivation, participation and learning of the students. Data science identifies 3 predictive models about MOOCs and ICT through the decision tree technique. According to the teachers of the National Autonomous University of Mexico, the organization of the school activities in MOOCs and use of ICT play a fundamental role during the COVID-19 pandemic. The implications of this research promotes that educators use MOOCs and ICT to improve the educational conditions, create new remote school activities and build new virtual learning spaces. In conclusion, universities with the support of technological tools can improve the teaching-learning process and update the course during the COVID-19 pandemic. In particular, MOOCs represent a technological alternative to transform the school activities in the 21st century.
Keywords: MOOCs, teaching, data science, machine learning, ICT, COVID-19
Research Article
Ricardo-Adán Salas-Rueda, Jesús Ramírez-Ortega, Ana-Libia Eslava-Cervantes
CONT ED TECHNOLOGY, Volume 13, Issue 1, Article No: ep286
ABSTRACT
This mixed research analyzes the use of the Collaborative Wall to improve the teaching-learning conditions in the Bachelor of Visual Arts considering data science and machine learning (linear regression). The sample is made up of 46 students who took the Geometric Representation Systems course at the National Autonomous University of Mexico (UNAM) during the 2019 school year. The Collaborative Wall is a web application that facilitates the organization and dissemination of ideas through the use of images and text. In the classroom, the students formed teams and used mobile devices to access this web application. The results of machine learning indicate that the organization of ideas in the Collaborative Wall positively influences the participation of students, motivation and learning process. Data science identifies 3 predictive models about the use of this web application in the educational field. Also, the Collaborative Wall facilitates the learning process in the classroom through the comparison and discussion of information. Finally, technological advances allow organizing creative activities that favor the active role of students.
Keywords: collaborative wall, bachelor, technology, learning, data science, teaching