Torang Siregar

Mathematical reasoning is a fundamental competency in contemporary mathematics education, yet many junior high school students continue to experience difficulties in constructing logical arguments, connecting mathematical concepts, and solving complex problems. This study examined changes in junior high school students' mathematical reasoning following the implementation of Problem-Based Learning (PBL) integrated with a Deep Learning approach. A quantitative one-group pretest–posttest quasi-experimental design was employed involving 30 Grade VIII students from a public junior high school in North Sumatra, Indonesia. The instructional intervention was implemented across four mathematics learning sessions on Systems of Linear Equations in Two Variables to promote meaningful learning, reflective thinking, and active problem solving. Data were collected using a validated mathematical reasoning test administered before and after the intervention. Descriptive statistics, the Kolmogorov–Smirnov normality test, paired-sample t-test, and N-Gain analysis were used. The findings revealed a statistically significant improvement in students' mathematical reasoning following the instructional intervention, with mean scores increasing from 41.73 in the pretest to 79.40 in the posttest (t(29) = 18.063, p < .001, Cohen's d = 3.30). Because the study employed a one-group pretest–posttest design, these findings indicate substantial within-group improvement following the intervention rather than definitive evidence of causal effectiveness. The mean N-Gain score of 0.64 indicated a moderate level of improvement. The results suggest that integrating PBL with a Deep Learning approach is associated with improvements in mathematical reasoning. The study contributes to innovative mathematics instruction and offers practical implications for improving teaching and learning in education.

Artificial intelligence (AI) literacy has emerged as a critical competency in elementary education as learners increasingly engage with AI-mediated and technology-rich learning environments. This study developed and validated a multidimensional AI literacy instrument for elementary school students using a research and development approach grounded in the ADDIE framework and informed by AI literacy, constructivist learning, and digital citizenship theories. The instrument comprised five dimensions: AI recognition, conceptual understanding, functional application, evaluation and creation, and AI ethics. Content validation by four experts demonstrated strong theoretical relevance across all dimensions. Empirical testing involving 250 Indonesian elementary school students examined the instrument’s psychometric properties. Exploratory factor analysis supported a five-factor structure with acceptable factor loadings and sampling adequacy (KMO > .80; Bartlett’s test, p < .001), explaining a substantial proportion of total variance. Reliability analysis indicated satisfactory internal consistency across retained subscales (Cronbach’s α > .70), although conceptual understanding required minor item revision. Descriptive findings revealed stronger competencies in ethical AI use and functional application than in conceptual understanding of AI processes. The study contributes a psychometrically grounded and developmentally appropriate instrument supporting AI literacy assessment, curriculum innovation, instructional evaluation, and responsible AI integration within primary education contexts for early twenty-first-century educational transformation.