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Adaptive learning mindset in digital learning environments: development and psychometric validation of the TEALMS
Yusra Jadallah Abed Khasawneh1, Mohammad Nayef Ayasrah2, Abdulaziz Mohammed Alismail3
1Faculty of Educational Sciences, Department of Educational Administration, Ajloun National University, Ajloun, Jordan.
Background:
Adaptive learning in digital environments involves learners' growth-oriented beliefs and their capacity to reflect on and adjust learning strategies in response to challenge and feedback. However, psychometrically validated instruments specifically designed to assess this adaptive learning orientation among secondary school students remain scarce, particularly in non-Western contexts.
Methods:
This study reports the development and validation of the Technology-Enhanced Adaptive Learning Mindset Scale (TEALMS) using a cross-sectional psychometric design. Participants were 752 Jordanian secondary school students. Exploratory factor analysis and confirmatory factor analysis were conducted to examine the factorial structure. Internal consistency was evaluated using Cronbach's alpha and McDonald's omega, while convergent and discriminant validity were assessed using composite reliability, average variance extracted, and inter-factor correlations.
Results:
Analyses supported a five-factor model comprising Innovative Integrative Thinking with Learning Technologies, Dynamic Growth Orientation, Cognitive Flexibility, Metacognitive Self-Regulation, and Resilient Adaptation, accounting for 65.22% of the total variance. The first-order model demonstrated acceptable fit (χ²/df = 2.67, CFI = 0.919, RMSEA = 0.067), with strong internal consistency (α = 0.925-0.943; ω = 0.904-0.930) and satisfactory evidence of convergent and discriminant validity.
Conclusions:
The TEALMS provides a reliable and valid instrument for assessing secondary school students' adaptive learning orientation in technology-enhanced educational contexts. Its contribution lies in offering a context-sensitive measure of growth-oriented and self-regulatory responses to challenge and feedback in digital learning settings.
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