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Digital literacy profiles of pre-service social studies teachers in Turkey: a factor mixture modeling approach
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DOI：10.1007/s10639-026-14117-7.png)
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Abstract
En 中文
This study examined the digital literacy (DL) profiles of pre-service social studies teachers in Turkey and investigated how background characteristics predict profile membership. Data were collected from 534 participants across six universities using the Digital Literacy Scale. Applying factor mixture modeling, a three-class structure was identified: high (15.9%), moderate (70.2%), and low (13.9%) DL groups. Multinomial logistic regression using the three-step approach revealed that gender, number of siblings, long-term residence, and number of languages spoken significantly predicted membership in the identified DL groups. Female students and those from larger families were more likely to belong to lower DL groups, whereas rural-origin students were associated with the moderate level of DL. Similarly, multilingual students tended to be in the low DL group in this context. Focusing on social studies, a discipline central to digital citizenship education, this research provides the first field-specific latent profile study of pre-service teachers in Turkey. It advances understanding of DL in a developing-country context and offers evidence-based insights for equitable, context-sensitive teacher education programs.
Keywords:
Digital literacy
21st-century skills
Social studies education
Factor mixture modeling
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