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