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.

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Cognitive load theory is considered universally applicable to all kinds of learning scenarios. However, instead of a universal method for measuring cognitive load that suits different learning contexts or target groups, there is a great variety of assessment approaches. Particularly common are subjective rating scales, which even allow for measuring the three assumed types of cognitive load in a differentiated way. Although these scales have been proven to be effective for various learning tasks, they might not be an optimal fit for the learning demands of specific complex environments such as technology-enhanced STEM laboratory courses. The aim of this research was therefore to examine and compare the existing rating scales in terms of validity for this learning context and to identify options for adaptation, if necessary. For the present study, the two most common subjective rating scales that are known to differentiate between load types (the cognitive load scale by Leppink et al. and the naïve rating scale by Klepsch et al.) were slightly adapted to the context of learning through structured hands-on experimentation where elements such as measurement data, experimental setups, and experimental tasks affect knowledge acquisition. N = 95 engineering students performed six experiments examining basic electric circuits where they had to explore fundamental relationships between physical quantities based on the observed data. Immediately after the experimentation, the students answered both adapted scales. Various indicators of validity, which considered the scales’ internal structure and their relation to variables such as group allocation as participants were randomly assigned to two conditions with a contrasting spatial arrangement of the measurement data, were analyzed. For the given dataset, the intended three-factorial structure could not be confirmed, and most of the a priori-defined subscales showed insufficient internal consistency. A multitrait–multimethod analysis suggests convergent and discriminant evidence between the scales which could not be confirmed sufficiently. The two contrasted experimental conditions were expected to result in different ratings for the extraneous load, which was solely detected by one adapted scale. As a further step, two new scales were assembled based on the overall item pool and the given dataset. They revealed a three-factorial structure in accordance with the three types of load and seemed to be promising new tools, although their subscales for extraneous load still suffer from low reliability scores.

BackgroundRepresentational competence is commonly considered to be a prerequisite for the acquisition of conceptual knowledge, yet little exploration has been undertaken into the relation between these two constructs. Using an assessment of representational competence with vector fields that functions without confounding topical context, we examined its relation with N = 515 undergraduates’ conceptual knowledge about electromagnetism.ResultsApplying latent variable modeling, we found that students’ representational competence and conceptual knowledge are related yet clearly distinguishable constructs (r = .71). The relation was weaker for female than for male students, which could not be explained by measurement differences between the two groups. There were several students with high representational competence and low conceptual knowledge, but only few students with low representational competence and high conceptual knowledge.ConclusionsThese results support the assumption that representational competence is a prerequisite, yet insufficient condition for the acquisition of conceptual knowledge. We provide suggestions for supporting learners in building representational competence, and particularly female learners in utilizing their representational competence to build conceptual knowledge.

Multiple external representations (MERs) play an important role in the learning field of mathematics. Whereas the cognitive theory of multimedia learning and the integrative text and picture comprehension model assume that the heterogeneous combination of symbolic and analogous representations fosters learning; the design, functions, and tasks framework holds that learning benefits depend on the specific functions of MERs. The current paper describes a conceptual replication study of one of the few studies comparing single representations, heterogeneous, and homogeneous MERs in the context of mathematics learning. In a balanced incomplete block design, the participants were provided single representations (a graphic, text, or formula) or a heterogeneous (e.g., text + graphic) or homogeneous (text + formula) combination of these to solve linear system of equations problems. In accordance with previous research, performance was superior in conditions providing MERs compared to single-representation conditions. Moreover, heterogeneous MERs led to time savings over homogeneous MERs which triggered an increase in cognitive load. Contrary to previous research, text was the least fixated representation whereas the graphical representation proved to be most beneficial. With regard to practical implications, experts should be fostered through more challenging homogeneous MERs whereas novices should be supported through the accessible graphic contained in heterogeneous MERs.