Research Article
Emilio Flores-Mamani, Arcelia-Olga Rojas-Salazar, Pedro-Basilio Tapia-Espinoza, Constantino-Miguel Nieves-Barreto, Juan Inquilla-Mamani, Ányela-Yésica Flores-Yapuchura, Gilberto Vilca-Cutipa
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep682
ABSTRACT
The use of artificial intelligence (AI) in higher education pedagogy has gained increasing relevance; however, there remains a shortage of psychometrically validated instruments capable of assessing, in a multidimensional and context-specific manner, faculty adoption of these technologies. The aim of this study was to identify the factorial structure and evaluate the psychometric properties of an instrument designed to measure AI use among university faculty. The instrument was developed based on the unified theory of acceptance and use of technology and was administered to a convenience sample of 330 university instructors. Exploratory factor analysis revealed a robust seven-factor structure explaining 66.90% of the total variance, allowing the refinement and optimization of the instrument from 50 to 30 items. The final scale demonstrated excellent overall internal consistency (Cronbach’s alpha = 0.913). It is concluded that the developed instrument is a valid and reliable tool for higher education institutions to assess and diagnose faculty levels of AI adoption, thereby facilitating the design of context-specific institutional policies and effective professional development programs in AI-enhanced pedagogy.
Keywords: artificial intelligence, university pedagogy, exploratory factor analysis, reliability
Research Article
Jesús Valverde-Berrocoso, Mario Hidalgo, Ana María Rodríguez
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep683
ABSTRACT
Teacher digital competence (TDC) is widely recognized as a key enabler of educational digitalization, yet many professional development initiatives do not lead to sustained changes in classroom practice. This study examines the contextual and organizational determinants that should inform the design and implementation of TDC programs in school education from an implementation science (IS) perspective. Drawing on the consolidated framework for implementation research 2.0, a three-round Delphi study was conducted with 23 specialists in educational technology, including teacher educators, school leaders, and system-level advisers. Through qualitative and quantitative analyses, the study developed a model that organizes the main determinants across five domains: intervention characteristics, outer setting, inner setting, characteristics of individuals, and implementation process. The findings suggest that effective TDC programs must extend beyond a narrow focus on individual skills, as their success also depends on leadership, organizational conditions, external support, contextual adaptation, and ongoing evaluation. The model provides a structured basis for designing, implementing, and evaluating programs that are realistic, context-sensitive, and sustainable. Overall, the study demonstrates the value of IS for strengthening teacher professional development in digital education.
Keywords: teacher digital competence, teacher education, implementation science, consolidated framework for implementation research 2.0, professional development
Review Article
Oksana V. Vashetina, Tatyana Shoustikova, Natalia A. Zaitseva, Yelizaveta V. Chereshneva, Mariia S. Pavlova, Zhanna M. Sizova, Andrey V. Suslov
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep684
ABSTRACT
Educational technology (ET) plays an important role in improving student learning experiences, yet no prior study has systematically examined research trends among its most influential publications. This bibliometric study analyzed the 100 most-cited articles on ET published between 2005 and 2024, drawn from the Scopus database. Results show that articles published in 2020 received the highest total citations, with the USA, Taiwan, and Australia as the most productive countries and Computers and Education as the leading source journal. Analysis of the top 20 most-cited articles identified three themes: interactive learning environments, the role of digital technologies, and the impact of the COVID-19 pandemic on ET use. Keyword co-occurrence analysis further revealed four emerging research clusters spanning technology acceptance, learning-process monitoring, distance-learning pedagogy, and post-pandemic online/blended learning models. Co-citation analysis showed that researchers most frequently drew on studies addressing individual learner characteristics and technology acceptance frameworks (TAM/UTAUT). These findings offer researchers and practitioners a consolidated overview of the field’s most impactful contributions and emerging directions.
Keywords: educational technology, most cited studies, bibliometric analysis, 100 most cited, TAM, UTAUT
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