Spatial skills can predict mathematics performance, with many researchers investigating how and why these skills are related. However, a literature review on spatial ability revealed a multiplicity of spatial taxonomies and analytical frameworks that lack convergence, presenting a confusing terrain for researchers to navigate. We expose two central challenges: (1) many of the ways spatial ability is defined and subdivided are often not based in well-evidenced theoretical and analytical frameworks, and (2) the sheer variety of spatial assessments. These challenges impede progress in designing spatial skills interventions for improving mathematics thinking based on causal principles, selecting appropriate metrics for documenting change, and analyzing and interpreting student outcome data. We offer solutions by providing a practical guide for navigating and selecting among the various major spatial taxonomies and instruments used in mathematics education research. We also identify current limitations of spatial ability research and suggest future research directions.

Augmented Reality (AR) technologies allow for holograms to be layered over the real-world, “augmenting” human vision by adding technical information or illustrations onto 3D space. Although AR-based applications are showing positive effects in many systematic reviews and meta-analyses, well-designed, rigorous studies with strong control conditions are lacking. Further, many experimental studies lack process data to illuminate what is happening as students engage with AR. In this pre-registered study, we conducted an experiment where n = 120 high school students were assigned to reason about identical geometry simulations collaboratively either using tablets or AR head‐mounted displays (HMDs). We look at their learning and how it was impacted by the dimensionality (2D or 3D) of the shapes they explored, as well as how they engaged with virtual objects using gestures and epistemic actions. AR HMDs were more effective for students getting an initial sense of 3D shapes, but less effective for 2D shapes. For gaining insights into the workings of shapes and formulating justifications of conjectures, we see no evidence AR is more effective, and trends indicating AR may be detrimental to eliciting generalizations. Further, process data showed that students using tablets are more likely to manipulate the geometric shapes in the simulations, while students using the AR HMDs are more likely to use dynamic gestures that simulate these manipulations, which are less constrained by the objects’ actual properties. Implications for the future design and use of AR in education are given.

Integrating data literacy into K-12 education in an increasingly data-driven society is imperative. Data literacy is conceptualized as an interdisciplinary competence that extends beyond traditional statistical understanding, encompassing skills in accessing, analyzing, interpreting, and effectively communicating insights derived from data. The paper argues for a paradigm shift in educational approaches, advocating for incorporating contextual, inquiry-based methodologies over the traditional formalisms-first approach. This shift is essential for enhancing students' ability to apply data literacy skills in real-world contexts. The limitations of a formalisms-first pedagogical approach are discussed, highlighting its potential to restrict students' practical application of theoretical knowledge. In contrast, the article advocates for inquiry-driven educational strategies like project-based and problem-based learning to foster deeper engagement and understanding of data literacy. These strategies may be more effective in connecting theoretical concepts with students' lived experiences and real-world applications. Additionally, the paper argues that data literacy should be framed as language. Designers of data literacy learning progressions should draw on examples from mathematics and science domains and research to build students' understanding of the transformation processes from data to evidence and subsequently to models and explanations. Further, the article explores the integration of technology in data literacy education. It underscores the role of digital tools and platforms in facilitating interactive, hands-on experiences with complex data sets, enriching the learning process, and preparing students for the challenges of the digital era. In conclusion, the article calls for a comprehensive, interdisciplinary approach to data literacy education underpinned by technology-enhanced learning environments. This approach is essential for developing both the technical skills for data manipulation and a critical mindset for data evaluation and interpretation, thereby cultivating a responsible, data-literate citizenry capable of informed decision-making in a data-rich world.

Integrating data literacy into K-12 education in an increasingly data-driven society is imperative. Data literacy is conceptualized as an interdisciplinary competence that extends beyond traditional statistical understanding, encompassing skills in accessing, analyzing, interpreting, and communicating insights from data. Drawing on sociocultural theory, this paper argues that the formalisms-first pedagogical approach is theoretically limited, rooted in a conception of learning that strips away the social, cultural, and material conditions through which meaningful data reasoning develops. In contrast, we advocate for inquiry-driven strategies like project-based and problem-based learning, which restore the contextual, collaborative conditions that make data literacy learnable. Central to this argument is the claim that data literacy is best understood as a disciplinary language, a set of representational, argumentative, and interpretive practices acquired through socially scaffolded participation. Learning progressions should draw on contexts across STEM, humanities, and social sciences, recognizing that the language of data is relevant wherever students reason with evidence. The article also examines technology integration in data literacy education. Rather than treating digital tools as neutral delivery mechanisms, we frame them as mediating artifacts that shape how students think, represent, and argue with data. We discuss equitable access to these tools, the promise of emerging technologies such as augmented and virtual reality, and the value of unplugged approaches where access to technology is limited. In conclusion, we call for an explicitly sociocultural and interdisciplinary approach to data literacy education, essential for developing both technical skills for data work and a critical mindset for evaluating data as a social and civic practice that fosters a responsible, data-literate citizenry capable of informed participation in a data-rich world. Future empirical work is needed on the effectiveness of inquiry-driven approaches for data literacy outcomes and on what equitable technology integration looks like across diverse learning contexts.