Spatial reasoning has been shown to be an important, trainable cognitive skillset for developing successful engagement in science, technology, engineering and mathematics (STEM), but little empirical work has been conducted in the context of digitally mediated pedagogies. This paper reports on a study into the effectiveness of a technologically enhanced STEM program on the spatial reasoning of a cohort of year 7 students (n = 107). The students undertook five digitally based activities over the period of a week and were assessed on their spatial reasoning development and attitudes towards STEM prior and subsequent to the intervention. Results indicate that the week of learning activities had an impact on attitudes towards STEM and had a positive impact on overall spatial skills. However, no statistically significant improvements were found within the spatial sub-skills or in relation to specific activities.

The “Maker” movement is a cultural as well as educational phenomenon that has the potential to offer significant opportunities to students in conditions of social, economic and cultural disadvantage. The research reported in this paper, however, suggests that the simple provision of “Maker Spaces” for such activity is simplistic and not sufficient to realise this potential. The research involved a mixed methods study of a cohort of year 7 students (n = 26) in an Australian school located in a socio-economically disadvantaged outer-metropolitan region. The cohort undertook a range of Maker activities at a new “creativity centre” built at the school. Results indicate that the activities had positive impact on student attitudes towards science, technology, engineering and mathematics (STEM) overall, but that the impact was highly specific across attitudinal constructs. A strong ranging effect was also evident, suggesting that the impact of the experience was highly dependent on students’ initial attitudes. Reflecting on these results, the paper also offers a reference framework that may help keep equity in mind when designing different kinds of Maker experience.

Complete scene reconstruction from single view RGBD is a challenging task, requiring estimation of scene regions occluded from the captured depth surface. We propose that scene-centric analysis of human motion within an indoor scene can reveal fully occluded objects and provide functional cues to enhance scene understanding tasks. Captured skeletal joint positions of humans, utilised as naturally exploring active sensors, are projected into a human-scene motion representation. Inherent body occupancy is leveraged to carve a volumetric scene occupancy map initialised from captured depth, revealing a more complete voxel representation of the scene. To obtain a structured box model representation of the scene, we introduce unique terms to an object detection optimisation that overcome depth occlusions whilst deriving from the same depth data. The method is evaluated on challenging indoor scenes with multiple occluding objects such as tables and chairs. Evaluation shows that human-centric scene analysis can be applied to effectively enhance state-of-the-art scene understanding approaches, resulting in a more complete representation than single view depth alone.

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