This study comprehensively analyzes the alignment of the Technique and Technology curriculum for grades 5–8 in Serbia’s primary education system with key competencies for lifelong learning, following the 2018 Council of the European Union Recommendation. Using a mixed-method approach, specifically a convergent design as outlined by Creswell, this study combines qualitative content analysis with descriptive statistics to assess the distribution of competencies across grades. The integration of qualitative and quantitative data provides a more comprehensive understanding of the curriculum’s alignment with key competencies. Findings reveal an imbalance between theoretical and practical outcomes, particularly in digital literacy, where practical skills are prioritized over theoretical understanding. Similarly, entrepreneurial modules lack activities promoting critical thinking and initiative. The curriculum emphasizes practical skills but needs to improve its theoretical framework, especially in programming and entrepreneurial finance. It also lacks activities that develop positive attitudes, such as innovation and teamwork, which are crucial for lifelong learning. Recommendations include increasing practical Science, Technology, and Engineering (STE) projects, enhancing theoretical content in digital and entrepreneurial modules, and fostering activities that build positive attitudes. Further research is needed to track students’ attitudes towards technology and entrepreneurship across grades and to assess the impact of continuous professional development for educators on effectively integrating these competencies.

Achieving Sustainable Development Goal 4 (Quality Education) requires innovative and inclusive approaches that sustain attention, foster engagement, and build digital literacy in primary education. This study examined the impact of Scratch on sustaining attention among third- and fourth-grade students (ages 9–11). A total of 89 students participated, divided into control and experimental groups, to evaluate the effects of integrating Scratch into classroom activities. Data were collected through anonymous surveys with parental consent and analyzed using nonparametric statistical tests. The results showed that Scratch-based instruction enhanced sustained attention, particularly in interactive and digital activities such as active participation, video, and web-based tasks. In contrast, traditional textbook and workbook tasks showed weaker effects. Scratch also reduced common reasons for attention loss, with significant decreases in perceptions of excessive lesson length and loss of interest. Gender differences were minimal, confirming Scratch’s inclusivity, and strong correlations between problem-solving activities indicated that engagement, collaboration, and multimedia tasks mutually reinforce attention. These findings provide empirical support for Scratch as a tool that sustains attention, reduces disengagement, and promotes equity in learning. Although limited by reliance on self-reported data and a single educational context, the study offers evidence that Scratch advances innovative pedagogy aligned with SDG 4.

This paper presents a dashboard-oriented visual analytics workflow for classification-related exploratory analysis based on Information Gain (IG), histogram segmentation, and complementary localized interpretation through the Precise Piecewise Correlation (PPC) method. The workflow is designed to support the construction of a primary dashboard view by prioritizing attributes with stronger relevance to the decision variable and inspecting their class-related behavior within segmented histogram intervals. Rather than introducing a new standalone feature-selection metric, this study formalizes how established analytical components can be integrated into a coherent dashboard framework for structured visual inspection. The proposed workflow was examined on three datasets from different application domains: the Iris dataset, an educational performance dataset, and an Oil and Gas dataset. Across these cases, IG-based prioritization identified attributes that provided clearer class-related structure in the primary dashboard view, while histogram segmentation supported interval-level interpretation of class concentration and overlap. A compact quantitative evaluation further showed that top-ranked IG subsets retained strong discriminative information under standard classification models, whereas lower-ranked subsets generally performed less favorably. Entropy-based segment analysis additionally indicated lower local class uncertainty for higher-ranked attributes. A small user study provided preliminary user-centered support for the interpretability and practical usefulness of the proposed dashboard structure. The results suggest that the proposed workflow can support dashboard-based inspection of class-related patterns across different contexts.