Digital ADLX as a Foundation for Modern Learning: A Systematic Review an Bibliometric Analysis
Hani Arie Rachmanie (1), Bunyamin (2), and Ishaq Nuriadin (3)

(1),(2),(3) University of Muhammadiyah Prof. DR. HAMKA

Abstract

The rapid digital transformation in education has driven the need for innovation in learning methods that are able to answer the challenges of the 21st century. Digital ADLX is seen as a potential model in improving student engagement, motivation, and learning outcomes through more dynamic and contextual interactions. This study aims to explore and map the development of scientific studies related to digital ADLX through two main methodological approaches, namely systematic review and bibliometric analysis. Data were collected from more than 200 publications obtained from reputable databases such as Scopus and Web of Science during the period 2015 to 2025. The selection process was rigorous with reference to Cochrane guidelines, while bibliometric analysis was facilitated using VOSviewer and Bibliometrix software to identify publication trends, citation frequency, collaboration between authors, as well as dominant keyword mapping. The results showed a significant increase in the number of publications addressing digital ADLX over the past decade. In addition, a strong network of research collaborations between institutions and countries was found, suggesting that the issue of digital ADLX is a global agenda in the future development of education. This study makes an important contribution by providing a conceptual and empirical map that can inform the development of policies, learning strategies and teacher training that are adaptive to the needs of digital education. This study recommends the importance of cross-sector collaboration between researchers, practitioners and policymakers to strengthen a more systematic and sustainable implementation of digital ADLX in the formal education system.

Keywords: Digital ADLX, active and deep learning, educational technology, student engagement, digital transformation, systematic review, bibliometric analysis

Topic: Computer Science