Information | Free Full-Text | TrainAR: An Open-Source Visual Scripting-Based Authoring Tool for Procedural Mobile Augmented Reality Trainings
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Additionally, although TrainAR was originally envisioned as a holistic solution combining an interaction concept, didactic framework, and an authoring tool (see
Figure 1), as already discussed in [
17], each of the three components can also be used separately. It has to be noted that the scenarios shown in this section do not necessarily use each of the components. While all of them use the TrainAR interaction concept, the training of preparing a tocolytic injection was the starting point for the TrainAR framework abstraction and therefore was developed from scratch, not utilizing the TrainAR authoring tool. The denomination of the female pelvis, a game exploring the sourness of fruits, and the game for exploring ripeness all use the interaction concept and authoring tool but not the didactic framework, as they are envisioned more as rule-based learning games than strictly procedural trainings. Only the titration experiment utilizes all three components, though it also has to be noted that the authoring tool utilized in all trainings was in early preliminary stages, e.g., not including visual scripting and still requiring programming (see
Table 1). The main focus in this stage was the evaluation of the created trainings, not the authoring tool, which was evaluated separately (see
Section 7).
For the usability, a SUS study score of 72.8 (SD = 14.0) [
40] was reported (see
Figure 9), which would indicate above average or “Good” usability on the Adjective contextualization scale proposed by Bangor et al. [
36] and is an acceptable usability score [
41]. According to Tullis et al. [
38], this result is only between 75–80% conclusive, based on the small sample size of
n = 9 participants. The desired utility of the training was an increased understanding of the users’ knowledge of acid–base titration concepts. Likely because of the small sample size, the initial study failed to show statistically significant learning effects, but results from larger studies are forthcoming [
40]. Nonetheless, Dominguez Alfaro et al. [
40] could observe that the app was “well-received by the users”, and they were able to independently download and utilize it in a remote experiment setting without an experimenter present.
Table 2.
The average Task Completion Rate (TCR) for each of the three authoring tasks, grouped by the participants’ study program and reported on 4 levels: without any help, with a hint of where in the documentation the solution is described, with a predefined solution hint, or with explicit help.
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