The Effects of Augmented Reality: A Comparative Study in an Undergraduate Physics Laboratory Course

Kapp, Sebastian; Thees, Michael; Beil, Fabian; Weatherby, Thomas; Burde, Jan-Philipp; Wilhelm, Thomas; Kühn, Jochen

doi:10.5220/0009793001970206

Cited by 14 publications

(8 citation statements)

References 32 publications

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“…Accordingly, the only difference between the two groups was the spatial arrangement of the virtual real-time measurement displays. Further information on the technical implementation of the learning environment was described by Altmeyer et al (2020) and Kapp et al (2020). Both adapted subjective rating scales were applied as shown in Tables 1, 2 in order to measure cognitive load in a differentiated way.…”

Section: Methodsmentioning

confidence: 99%

“…These are the cognitive load scale (CLS; 10-item questionnaire) developed by Leppink et al (2013) and the (second version of the) naïve rating scale (NRS; 8-item questionnaire) by Klepsch et al (2017). Both scales were applied in various learning contexts (Leppink et al, 2014;Altmeyer et al, 2020;Andersen and Makransky, 2021a;Andersen and Makransky, 2021b;Becker et al, 2020;Kapp et al, 2020;Seufert, 2020, Klepsch andSeufert, 2021;Skulmowski and Rey, 2020;Thees et al, 2020), while the reliability of the subscales and the valid measurement of the three load types were confirmed multiple times (Klepsch et al, 2017;Becker et al, 2020;Klepsch and Seufert, 2020;Thees et al, 2020;Andersen and Makransky, 2021a;Andersen and Makransky, 2021b). However, their application in different contexts usually requires moderate adaptations.…”

Section: Introductionmentioning

confidence: 99%

“…The other group received an integrated format where the data were grouped together on a single display. Former studies in the context of hands-on electricity laboratory courses have emphasized that measurement values, which have to be compared and related to each other in order to learn successfully, should be presented in spatial proximity (Altmeyer et al, 2020;Kapp et al, 2020;Thees et al, 2020) to avoid the well-known split-attention effect (Schroeder and Cenkci, 2018). Hence, the split-source format was expected to trigger unnecessary search processes, and the corresponding group was expected to rate higher ECL than the group with the integrated format.…”

Section: Introductionmentioning

confidence: 99%

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Comparing Two Subjective Rating Scales Assessing Cognitive Load During Technology-Enhanced STEM Laboratory Courses

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparing Two Subjective Rating Scales Assessing Cognitive Load During Technology-Enhanced STEM Laboratory Courses

et al. 2021

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“…To evaluate the usability of our newly developed sensors, an initial field study with students conducting typical experiments on basic electrical circuits was conducted. The experimental tasks used in this study are based on previous studies utilizing a previous generation of the sensor boxes presented in this paper [ 56 , 57 , 58 , 59 ].…”

Section: Discussionmentioning

confidence: 99%

Smart Sensors for Augmented Electrical Experiments

Kapp

Lauer

Beil

et al. 2021

Sensors

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With the recent increase in the use of augmented reality (AR) in educational laboratory settings, there is a need for new intelligent sensor systems capturing all aspects of the real environment. We present a smart sensor system meeting these requirements for STEM (science, technology, engineering, and mathematics) experiments in electrical circuits. The system consists of custom experiment boxes and cables combined with an application for the Microsoft HoloLens 2, which creates an AR experiment environment. The boxes combine sensors for measuring the electrical voltage and current at the integrated electrical components as well as a reconstruction of the currently constructed electrical circuit and the position of the sensor box on a table. Combing these data, the AR application visualizes the measurement data spatially and temporally coherent to the real experiment boxes, thus fulfilling demands derived from traditional multimedia learning theory. Following an evaluation of the accuracy and precision of the presented sensors, the usability of the system was evaluated with n=20 pupils in a German high school. In this evaluation, the usability of the system was rated with a system usability score of 94 out of 100.

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“…Moreover, unlike handheld display-devices, HMD-AR-devices allow for freehand interaction with physical as well as virtual objects [ 18 ]. This becomes particularly useful for learning settings based on physical activities, like laboratory work, which requires leaners to use both of their hands [ 19 ]. Theories of embodied cognition suggest that bodily interactions with a learning task, such as hand and finger gestures, can support cognitive processes [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children

Lauer

Altmeyer

Malone

et al. 2021

Sensors

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Augmenting reality via head-mounted displays (HMD-AR) is an emerging technology in education. The interactivity provided by HMD-AR devices is particularly promising for learning, but presents a challenge to human activity recognition, especially with children. Recent technological advances regarding speech and gesture recognition concerning Microsoft’s HoloLens 2 may address this prevailing issue. In a within-subjects study with 47 elementary school children (2nd to 6th grade), we examined the usability of the HoloLens 2 using a standardized tutorial on multimodal interaction in AR. The overall system usability was rated “good”. However, several behavioral metrics indicated that specific interaction modes differed in their efficiency. The results are of major importance for the development of learning applications in HMD-AR as they partially deviate from previous findings. In particular, the well-functioning recognition of children’s voice commands that we observed represents a novelty. Furthermore, we found different interaction preferences in HMD-AR among the children. We also found the use of HMD-AR to have a positive effect on children’s activity-related achievement emotions. Overall, our findings can serve as a basis for determining general requirements, possibilities, and limitations of the implementation of educational HMD-AR environments in elementary school classrooms.

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The Effects of Augmented Reality: A Comparative Study in an Undergraduate Physics Laboratory Course

Cited by 14 publications

References 32 publications

Comparing Two Subjective Rating Scales Assessing Cognitive Load During Technology-Enhanced STEM Laboratory Courses

Comparing Two Subjective Rating Scales Assessing Cognitive Load During Technology-Enhanced STEM Laboratory Courses

Smart Sensors for Augmented Electrical Experiments

Investigating the Usability of a Head-Mounted Display Augmented Reality Device in Elementary School Children

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