Minah Sintian
Universitas Pendidikan Sultan Idris

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Learning Agility Measurement Models in Technology-Driven Learning Environments: A Systematic Literature Review Nurhayi Musdira; Mukhlisin; Minah Sintian
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.472

Abstract

Purpose - This study systematically examines how learning agility has been measured in recent literature by identifying dominant measurement models, dimensional structures, and psychometric approaches across contexts. Methods - This study employed a Systematic Literature Review (SLR) guided by the PRISMA 2020 statement. A structured search was conducted in the Scopus database using predefined keywords. The selection process followed identification, screening, eligibility, and inclusion stages based on criteria such as publication year (2020–2025), document type (peer-reviewed journal articles), language (English), and full-text accessibility. A total of 24 articles were included and appraised using the Mixed Methods Appraisal Tool (MMAT) and analyzed through systematic synthesis. Findings - Learning agility is predominantly measured using self-report Likert-scale instruments, with validation through exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The four-dimensional model cognitive, people, change, and results agility remains the most widely used, with variations influenced by context. Analytical techniques are dominated by Structural Equation Modeling (SEM) and Partial Least Squares SEM (PLS-SEM), with learning agility often positioned as a mediating variable. Research Implications - Studies show overreliance on single-source data and limited use of behavioral, longitudinal, and multimethod approaches, indicating the need for more comprehensive, context-sensitive, and technology-integrated measurement models. Originality - This study synthesizes learning agility measurement approaches across contexts, clarifies dominant models, and identifies methodological gaps, highlighting the need for more robust and diversified measurement strategies