Research Article
Manuel Alejandro Concha-Huarcaya, Antonio Serpa-Barrientos, Luis Alberto Sosa-Aparicio, Enrique Giovanni Pérez-Flores, Jacksaint Saintila
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep685
ABSTRACT
Online learning strategies refer to the methods and approaches students use to organize study activities, manage course content, and regulate participation in virtual learning environments. These strategies are relevant to educational technology because they provide measurable indicators that can inform instructional design, learning analytics, adaptive learning systems, and student support in online and blended courses. This study examined the psychometric properties of the online learning strategies scale (OLSS) using a psychometric network analysis approach in Peruvian university students. The sample included 520 university students (389 women = 74.8%; 131 men = 25.2%) aged 18 to 40 years. Descriptive analyses were conducted to evaluate item distributions. The psychometric network structure of the OLSS was then estimated using exploratory graph analysis with bootstrap procedures, followed by assessment of community stability and structural consistency. The results identified a four-community structure corresponding to motivation, self-control, Internet literacy, and Internet anxiety. The network showed high structural stability, with bootstrap replication values close to 1.00 for most items and high structural consistency across communities. These findings support the internal structure and reliability of the OLSS in the studied population. From an educational technology perspective, the OLSS may help instructors and instructional designers identify students requiring motivational, self-regulatory, digital literacy, or affective support in technology-enhanced learning environments.
Keywords: online learning, online learning strategies, educational technology, network analysis, psychometrics, university students
Review Article
Tatyana Shoustikova, Zulfiya F. Zaripova, Gasangusein Ibragimov, Natalia A. Zaitseva, Olga V. Pashanova, Alisa V. Lobuteva
CONT ED TECHNOLOGY, Volume 18, Issue 1, Article No: ep628
ABSTRACT
The aim of the study is to research based on artificial intelligence (AI) literacy in the context of education with the bibliometric analysis method. Study identifies trends in studies on AI literacy and reveals the main disciplines, methodologies, and thematic focal points of this field. During the data collection process, a comprehensive search of the Web of Science and Scopus databases was carried out. A total of 154 articles published as of February 2025 were included in the analysis. The year of publication, database, research area, country of publication, journal in which it was published, university in which it was conducted, author distribution, method, abstract and keyword trends, and citation network information were analyzed. VOSviewer software was used for data visualization and network mapping. According to the results; It was seen that the first article on the subject was published in 2019 and the highest number of articles were published in 2024. In the researches, it was seen that AI literacy focused on teacher education, student skills, reflection on programs, ethical concerns and technological infrastructure. It was observed that the most phase research was conducted in China and the USA and the quantitative method was predominantly used. The journals in which the researches are published the most are Education and Information Technologies and Computers and education: Artificial Intelligence. As an institution, Education University of Hong Kong is the university with the most research on the subject. It was observed that the words AI, literacy, Higher education and teacher competency were used extensively as keywords and abstracts. In terms of the results, it was suggested to the researchers that comparative studies examining how AI literacy is perceived and applied in different cultural and educational contexts and research that includes multifaceted evaluations in which mixed methodology is put to work can be conducted.
Keywords: artificial intelligence literacy, bibliometric analysis, AI in education, research trends, citation network analysis
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