An integrative framework for student adaptation under mass higher education conditions, developed by G. K. Baimuldinova and published in 2026. The dataset likely contains validated psychometric scores for academic, social, and psychological adaptation, transversal skills, and an integral Adaptation Quotient (AQ). Empirical analysis draws on several independent samples from linguistically and culturally diverse student populations in public universities in Kazakhstan.
Use Cases
- Analyze the relationship between organizational constraints (e.g., cohort overcrowding, bureaucratic density) and adaptation outcomes based on the described framework.
- Identify heterogeneous adaptation profiles and potentially vulnerable student groups using the Adaptation Quotient (AQ) metric.
- Investigate how contextual moderators like language of instruction, cultural background, and gender influence adaptation patterns.
- Assess the role of transversal non-cognitive skills as compensatory resources in navigating massified educational environments.
- Model the impact of faculty accessibility on student adaptive profiles.
Strengths
- Dataset is 27.9 KB, indicating a compact and focused collection.
- Validated instruments were used to assess adaptation, transversal skills, and an Adaptation Quotient.
- Psychometric analyses demonstrated satisfactory reliability and factorial validity across measurement models.
- Empirical analysis draws on several independent samples, allowing for cross-sample triangulation.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- Data may reflect geographic bias inherent to the samples collected in Kazakhstan.
Provenance
- Source
- G. K. Baimuldinova
- Collection Method
- Likely contains survey or assessment data from several independent samples collected in public universities in Kazakhstan.
- Freshness
- Last updated 2026-04-10 06:03:58; freshness should be verified.
- Geography
- Kazakhstan