Construct Validity of an Assessment Instrument for Graduate Profile Dimensions in Deep Learning Based Mathematics Instruction
DOI:
https://doi.org/10.38114/riemann.v7i3.124Keywords:
construct validity, graduate profile dimensions, mathematics, learning, deep learningAbstract
This study aims to examine the validity of an assessment instrument for graduate profile dimensions in the context of deep learning–based mathematics instruction at the senior secondary school level. The instrument was developed to measure four core dimensions of the graduate profile, namely critical reasoning, creativity, collaboration, and learning autonomy. The study employed a research and development approach with a specific focus on the instrument development stage. Content validity was evaluated through expert judgment involving six experts using Aiken's V index. In contrast, construct validity was examined through Confirmatory Factor Analysis (CFA) based on Structural Equation Modeling applied to large-scale trial data from 216 senior secondary school students. The results indicate that all instrument items demonstrate high content validity. Furthermore, the CFA results show that the modified second-order measurement model meets most goodness-of-fit criteria and that all indicators have factor loadings exceeding the critical threshold. These findings provide empirical evidence that the developed instrument possesses adequate construct validity and is appropriate for assessing graduate profile dimensions in deep learning–based mathematics instruction.
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