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The understanding of graphs and extraction of relevant information from graphs plays a major role in physics education and is also important in several related fields. Recently, Susac et al. [Phys. Rev. Phys. Educ. Res. 14, 020109 (2018)] compared physics and psychology students' understanding of graphs in the contexts of physics and finance. They showed that physicists scored significantly higher in both domains and that all students solved the slope problems better than the area problems. Moreover, eye-tracking data revealed that physics students spent more time on problems associated with the area under the graph and focused longer on the axis labels of finance graphs, indicating higher cognitive demands. In this eyetracking study, we aim for a generalization of the results obtained by Susac et al. by comparing physics students to another nonphysics sample, viz., economics students. The findings broadly confirm the results of Susac et al.; that is, physics students perform better than nonphysics students. While economics students likely have better prior knowledge on finance context than psychology students, the physics students still outperform them on the finance questions. In contrast to the work by Susac et al., both groups of students had the same visit duration on the graphs, consequently proving total dwell time to be an inadequate predictor of performance. Instead, we identify that attention on concept-specific areas of interest within the graphs discriminates the correct from the incorrect performers. Furthermore, we analyzed the confidence level of the two student groups and found that physics students have a higher ability to correctly judge their own performance compared to economics students. Overall, our results highlight the importance of an instructional adjustment towards a more mathematical-and graphical-based education.

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Learning with hands-on experiments can be supported by providing essential information virtually during lab work. Augmented reality (AR) appears especially suitable for presenting information during experimentation, as it can be used to integrate both physical and virtual lab work. Virtual information can be displayed in close spatial proximity to the correspondent components in the experimentation environment, thereby ensuring a basic design principle for multimedia instruction: the spatial contiguity principle. The latter is assumed to reduce learners' extraneous cognitive load and foster generative processing, which supports conceptual knowledge acquisition. For the present study, a tablet-based AR application has been developed to support learning from hands-on experiments in physics education. Real-time measurement data were displayed directly above the components of electric circuits, which were constructed by the learners during lab work. In a two group pretest-posttest design, we compared university students' (N = 50) perceived cognitive load and conceptual knowledge gain for both the AR-supported and a matching non-AR learning environment. Whereas participants in both conditions gave comparable ratings for cognitive load, learning gains in conceptual knowledge were only detectable for the AR-supported lab work.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.