Measuring Personalized Learning in the Smart Classroom Learning Environment

Development and Validation of an Instrument

Datos Bibliográficos

ID22047331
AutoresPan Tuo (Shaanxi Normal University), Mehmet Bıçakçı (0000-0001-6865-9328, Friedrich-Alexander-Universität Erlangen-Nürnberg), Albert Ziegler (0000-0002-2122-0673, Friedrich-Alexander-Universität Erlangen-Nürnberg), Baohui Zhang (0000-0002-2523-2049, Shaanxi Normal University, autor de correspondencia)
Año2025
Volumen15
Número5
Páginas620
Fecha de publicación2025-05-19
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEducation Sciences (JOURNAL)
Identificadores de la revistaISSN: 2227-7102 • E-ISSN: 2227-7102
EditorialMDPI AG (PUBLISHER • IT)
DOI10.3390/educsci15050620
OpenAlexW4410523711
IdiomaEN
Citas recibidas2
Referencias citadas70

Smart classrooms leverage intelligent and mobile technologies to create highly interactive, student-centered environments conducive to personalized learning. However, measuring students’ personalized learning experiences in these technologically advanced spaces remains a challenge. This study addresses the gap by developing and validating a Smart Classroom Environment–Personalized Learning Scale (SCE-PL). Drawing on a comprehensive literature review, content-expert feedback, and iterative item refinement, an initial pool of 48 items was reduced to 39 and subsequently to 34 following item-level analyses. Two datasets were collected from Chinese middle-school students across three provinces, capturing diverse socio-economic contexts and grade levels (7th, 8th, and 9th). EFA on the first dataset (n = 424) revealed a nine-factor structure collectively explaining 78.12% of the total variance. Confirmatory factor analysis (CFA) on the second dataset (n = 584) verified an excellent model fit. Internal consistency indices (Cronbach’s α > 0.87, composite reliability > 0.75) and strong convergent and discriminant validity evidence (based on AVE and inter-factor correlations) further support the scale’s psychometric soundness. The SCE-PL thus offers researchers, policymakers, and practitioners a robust, theory-driven instrument for assessing personalized learning experiences in smart classroom environments, paving the way for data-informed pedagogy, optimized learning spaces, and enhanced technological integration

Human–computer interaction · Mathematics education · Multimedia · Computer Science · Digital literacy in education · E-Learning and Knowledge Management · Innovative Teaching and Learning Methods · Psychology