# Transforming Interactive Educational Content into Immersive Virtual Reality Learning Objects
## 7 Experiment Results  
The experiment procedure was completed by 32 participants. All participants completed the developed educational IVR application's assignment. On average, each participant spent 6 min 57 s, used supplementary learning material 2.5 times, and made 12.5 mistakes while completing the task. The time spent completing the task within the application varied from 3 min 31 s to 12 min 3 s and was mostly influenced by the experience in using IVR technology and the general knowledge of the human skeleton anatomy. Note that 93% of the participants indicated that they had little to no virtual reality technology experience. The low number of supplementary material usage was influenced by general knowledge of human skeleton. The high number of mistakes can be accredited to the participants' overconfidence in the topic material and inexperience with IVR technology. Estimates from the WBLT scale of both tested objects (IVR educational application prototype and 2D desktop interactive LO) were first compared, then a more detailed analysis of the developed application was performed. The WBLT scale scores for both objects are presented in the box and whisker graph below (Figure 7). The educational IVR application developed for the study received higher evaluations in all constructs: learning, design, and engagement. The most significant differences between the evaluation of the studied objects were found in engagement (0.943 vs. 0.472) and learning (0.894 vs. 0.557). Figure 7: IVR application and 2D interactive LO WBLT scale scores comparison in the domains of learning, design, and engagement. The relatively small difference in the design field (0.917 vs. 0.779) can be explained by the fact that both the IVR application and the 2D interactive LO cover the same learning material and aim to achieve similar learning outcomes, since the core of the IVR application is simply an adaptation of the material from the 2D interactive LO into an IVR environment. The only statement in the 2D interactive LO's WBLT questionnaire that received a higher rating than the corresponding statement from the IVR application's questionnaire is item No. 8, in the design construct: \"The learning object was easy to use\" (0.919 vs. 0.926). This is not surprising, as IVR applications are generally more difficult to use compared to standard desktop applications, especially for users lacking experience in virtual reality technologies. However, the highest rating for a single statement in the IVR application's WBLT questionnaire was received by item No. 12, in the engagement construct: \"The learning object made learning fun\" (0.971 vs. 0.412). This reveals that even though the IVR application was more difficult to use, overall, the users were more engaged, which should increase motivation and participation, potentially leading to higher grades. The \(p\)-value calculated using the Wilcoxon Signed Ranks test for all tested fields is less than 0.0001, signifying that the comparative results are of high statistical significance (Table 2). Analyzing the results of the IPQ scale by each subscale, the highest score for the developed IVR application was for the overall general estimate (4.72), and the lowest for realism (2.56). When compared to the raw IPQ data from the igroup database [41] (n = 619), the IVR application performs better than average in all fields: general (4.72 vs. 3.40), sense of presence (4.138 vs. 3.751), involvement (3.452 vs. 2.992), and realism (2.56 vs. 2.10). When compared to only the IPQ data of game applications using stereo head-mounted displays for video (n = 29), the results stay consistent: general (4.7 vs. 2.93), sense of presence (4.138 vs. 3.475), involvement (3.452 vs. 2.560), and realism (2.56 vs. 2.068). After calculating the SUS results from the study according to the SUS methodology, an estimated score of 85.88 was obtained (Figure 8). This is a high score, indicating that the developed prototype in this area is superior to over 96 percent of evaluated systems, with the average score being 68.