Introduction

Practical experiences are essential to introduce, demonstrate, support, and develop students’ understanding of conceptual knowledge, inquiry skills, and perceptions of science (Darrah et al., 2014; Hofstein & Lunetta, 2004). Physical laboratories provide opportunities for students to investigate scientific phenomena by directly interacting with the physical materials and tools, whereas they require high costs of materials, potential hazards and risks, and liabilities (De Jong et al., 2013; Kapici et al., 2019). Several challenges are faced during practical sessions, such as a lack of materials, limited time to retry experiments due to human error or machinery failure, and limited access to laboratories due to the pandemic (Baladoh et al., 2017; Bonde et al., 2014; Darrah et al., 2014; Gamage et al., 2020; Preim & Saalfeld, 2018; Turney, 2007). These issues are potentially decreasing students’ knowledge, skills, learning outcomes, experiences, and motivation in learning as well (Franchi, 2020; Gamage et al., 2020; Nolen & Koretsky, 2018).

Virtual laboratory (VLab) simulations are promising advanced technologies to replicate experiment activities and manipulate tools, materials, and machines into two-dimensional (2D) and/or three-dimensional (3D) models (Azer & Azer, 2016; Heradio et al., 2016; Hernández-Sellés et al., 2019; Keller & Keller, 2005). These technologies allow learners to explore virtual environments and interact with virtual models, which helps them gain more experience and motivation (Bonde et al., 2014; Estriegana et al., 2019; Guzmán et al., 2020). VLab allows students to conduct experiments multiple times from any location before performing in the real laboratory (Alkhaldi et al., 2016; Estriegana et al., 2019). Moreover, VLab can potentially contribute as a supplementary tool to support learning methods (offline, online, or blended) by enhancing students’ practical experiences, improving academic achievement, and creating meaningful learning (Alkhaldi et al., 2016; Reeves & Crippen, 2021).

Human anatomy is a core learning subject for medical students in order to provide comprehensive knowledge in the morphology of structures, positions, and spatial relations (Preim & Saalfeld, 2018). In conventional learning methods, most students use atlases, textbooks, pictures or videos, and mannequins as learning resources before performing cadaver dissection (Layona et al., 2018). Today, the emerging technology of 3D virtual environments, such as computer-based technologies and immersive technologies, has become increasingly popular (Golenhofen et al., 2020; Iwanaga et al., 2021; Rosmansyah et al., 2021). These technologies allow students to comprehensively understand the anatomical structure from different points of view. Students are able to interact with the virtual model, which enhances interactivity and effectiveness in learning (Erolin et al., 2019). These technologies have become an alternative learning method during pandemic and post-pandemic (Iwanaga et al., 2021).

The advantages of immersive technologies have been studied to assist medical students in learning ear anatomy as well as training for surgical procedures. The combination of virtual reality (VR) and mixed-reality (MR) technology, called Holoyes XR, allows surgeons and medical students to have a deep understanding of 3D spatial relationships and structures. In addition, students are able to gain experience by repeatedly practicing lateral temporal bone resection procedures (Ito et al., 2021). Similarly, the use of MR technology with Microsoft HoloLens has been implemented to teach the anatomy of the middle and inner ear (Gnanasegaram et al., 2020). Despite the fact that these advanced technologies have the ability to provide users with a high level of immersion, the costs are undeniably high and may not be accessible to most students (Birt et al., 2018).

Mobile devices are considered an alternative tool for learning due to their accessibility, affordability, and most students generally own them (Birt et al., 2018). Prior studies have revealed potential benefits of mobile devices, including visual and auditory richness, user-friendliness, encouraging users with interactive content, and possibly complying with accredited curricula and syllabi (Kerimbayev et al., 2023; Mansouri et al., 2020). In the context of medical education, several studies have discovered the effectiveness of mobile-based learning in enhancing academic performance (Briz-Ponce et al., 2016; Noguera et al., 2013) and reducing anxiety levels (Bolatli & Kizil, 2022). The use of 3D visualization in mobile-based learning may have contributed to creating an enjoyable learning process (Noguera et al., 2013). However, there is limited research on the use of mobile devices to support medical education (Golenhofen et al., 2020) and training healthcare professionals (Tortorella & Kinshuk, 2017), especially for learning ear anatomy.

This study endeavors to explore the efficacy of VLab, an interactive 3D mobile application, as a supplementary tool to enhance students’ knowledge in ear anatomy. Additionally, the study aims to investigate students’ perceptions of utilizing VLab for learning purposes. The research is structured as follows: Section II presents an extensive review of the existing literature concerning virtual ear anatomy education; Section III describes the methodology employed for implementing VLab and details the data analysis procedures undertaken. Section IV and Section V discuss a comprehensive analysis of students’ perceptions and knowledge improvement through VLab. Finally, Section VI encompasses the concluding remarks, summarizing the key findings and implications of the study’s results.

Literature review

Prior studies have conducted comprehensive literature reviews on various technologies, visualization techniques, and interaction techniques to enhance students’ anatomical knowledge (Preim & Saalfeld, 2018; Tam et al., 2009). Notably, augmented reality and virtual reality are the most popular technologies for teaching anatomy (Duarte et al., 2020; Erolin et al., 2019; Falah et al., 2014; Izard et al., 2017; Kurul et al., 2020; Rosmansyah et al., 2021). Additionally, another literature review introduced simulation applications to train students in preoperative planning and the rehearsal of surgical procedures (Arora et al., 2014). Despite these advancements, it is noteworthy that only a limited number of studies have specifically focused on ear anatomy education.

Nicholson et al. (2006) introduced computer-generated 3D anatomical models as an innovative approach to impart middle and inner ear anatomy knowledge. The model’s construction involved utilizing a high-resolution magnetic resonance imaging scan of a human cadaver’s ear. To evaluate its efficacy, the researchers conducted a randomized controlled study involving 57 first-year medical school students, who were divided into intervention and control groups. In the intervention group, students underwent a web-based tutorial that incorporated an interactive 3D model. Conversely, the control group learned via an online tutorial without the inclusion of the 3D model. The impact of computer-generated 3D models on learning was evaluated based on a quiz administered to both groups. The results revealed that the intervention group achieved a notably higher average score of 83, while the control group achieved an average score of 65. This result suggests that the utilization of computer-generated 3D models positively enhanced students’ knowledge in the context of ear anatomy education.

Venail et al. (2010) conducted a study with the aim of assessing students’ perceptions regarding the usefulness and effectiveness of 3D reconstruction tutorials in comparison to conventional lectures that utilized 2D anatomical drawings for teaching temporal bone anatomy. The 3D reconstruction was developed using the 3D Model of the Visible Ear and the model of the Human Temporal Bone. The study involved 142 first-year undergraduate students who were enrolled in speech therapy or hearing aid practitioner programs, along with 19 otolaryngology residents. The results indicated a significant increase in the average correct answers in the post-test as compared to the pre-test, indicating improved knowledge acquisition through the utilization of the 3D models. A majority of the students expressed that the incorporation of 3D models had positively contributed to their understanding and knowledge of the lecture material. Likewise, the otolaryngology residents also reported that the 3D models proved valuable in enhancing their knowledge, and they expressed a preference for using the application as a self-teaching tool. These findings suggest that the implementation of 3D reconstruction tutorials can be an effective method for enhancing learning outcomes and fostering a deeper understanding of temporal bone anatomy.

Ng et al. (2015) proposed an innovative approach to facilitate students’ learning of epitympanum anatomy using a 3D model that offered a comprehensive view from all angles. To assess its effectiveness, the researchers conducted a randomized controlled study involving 72 graduate medical students. These students were divided into two groups: the 2D group and the 3D group. Both groups were provided with textbooks and journal articles on epitympanum anatomy. Additionally, the 2D group received supplemental materials with 2D pictures, while the 3D group had access to the 3D model. Subsequently, participants were requested to provide subjective feedback on their learning experiences. The feedback indicated that the utilization of the 3D model significantly contributed to their learning process. Furthermore, a quiz was administered to both groups to evaluate the impact of the 3D model. The results revealed that the 3D group achieved a significantly higher average score of 65.1 compared to the 2D group, which obtained an average score of 32.4. These findings suggest that the incorporation of the 3D model proved to be valuable in enhancing the short-term recall of epitympanum anatomy among the participants.

Jang et al. (2017) introduced a VR system that generated a stereoscopic 3D environment viewable through stereoscopic glasses. This system allowed interactive manipulation and rotation of virtual models using a joystick. The study involved 76 medical students who had not previously received formal anatomy instruction on the inner ear and had no prior experience with the VR machine. Participants were paired off and assigned to different learning conditions: manipulation and viewing. In the manipulation condition, one student used the VR machine, while the other student observed their partner’s manipulations through recorded sessions of VR interactions. After the learning sessions, a post-test was conducted to assess knowledge improvement. The results showed that students who learned through the manipulation condition achieved a significantly higher average score of 72.5 compared to the average score of 60.8 obtained by those in the viewing condition. These findings suggest that active engagement and hands-on learning with the VR system enhanced knowledge gains when compared to passive observation of VR interactions.

Gnanasegaram et al. (2020) conducted a comparative investigation of three distinct pedagogical modes for teaching medical students. These modes included traditional didactic lectures with static images, a web-based computer module utilizing presentation materials, and computer-assisted learning through 3D holographic-controlled technology using Microsoft HoloLens. The study used a randomized controlled design in which 29 medical students were allocated to one of three learning modes. After the intervention, a post-assessment was administered, and the results demonstrated that all three learning modes significantly improved students’ knowledge when compared to their pre-intervention scores. However, the study also revealed that the students preferred to learn using 3D holographic technology. They perceived this modality as more effective in conveying spatial learning, engaging their attention, and fostering motivation compared to the other pedagogical methods.

Research gap and study objectives

With technological advancements, 3D-based applications have demonstrated potential for enhancing learning through interactive and engaging visualization. However, such technology is mostly accessible only within classrooms or training sessions. This technology is relatively expensive and may be beyond the reach of most students. Moreover, the use of alternative technologies such as mobile applications is still limited in previous research to support medical education.

This study fills this gap by developing an interactive VLab 3D mobile-based learning application, which students can use as supplementary tool to support their learning of anatomy, specifically human ear anatomy. The 3D model is constructed from various learning materials, expert input from otolaryngologists and human anatomy lecturers, and aligned with anatomy and physiology of hearing system course. The application provides two modes, namely exploration mode and laboratory mode. Exploration mode allows students to interact with the 3D model, while laboratory mode provides practical exercises. To evaluate the efficacy of VLab, this study seeks to: 1) assess the impact of VLab on students’ knowledge in understanding human ear anatomy compared to conventional learning approaches, and 2) analyze students’ perception of utilizing VLab.

Materials and methods

Overview of the VLab human ear anatomy application

During the planning phase of the VLab Human Ear Anatomy, the predicted outcomes can be elucidated using an impact model illustrated in Fig. 1, derived from Design Research Methodology (DRM). This model shows how the learning application provides students with interactive 3D models through mobile technology, assisting as a supplementary educational tool. The primary goal is to enhance students’ comprehension of ear anatomy, thereby allowing them to gain a more profound understanding.

Fig. 1
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Impact model

To measure the success of the study, two success criteria have been established: motivation and knowledge. Motivation can be evaluated by assessing students’ experiences during the application testing phase using a structured questionnaire. This feedback allows for a deeper insight into the motivational aspect of the learning process. Furthermore, knowledge is assessed through a comprehensive evaluation involving pre-test, mid-test, and post-test assessments. This rigorous evaluation ensures a comprehensive understanding of students’ knowledge levels.

The development of the VLab of Human Ear Anatomy involved programming through Unreal EngineFootnote 1 4.24, leading to its deployment on the iOSFootnote 2 platform. The conceptualization of this application followed the guidelines outlined in the Smart Learning Environment Establishment Guideline (SLEEG) tool based on the ISO 21001:2018 standard and ADDIE (Rosmansyah et al., 2022). The sequential steps of the SLEEG process are visually illustrated in Fig. 2. The application was seamlessly integrated with the Learning Management System (LMS) associated with the Bandung Institute of Technology, namely Edunex.Footnote 3 This integration served to facilitate user authentication and display leaderboards. The architectural blueprint of the application is elucidated in Fig. 3, providing a comprehensive depiction of its underlying structure and functional components.

Fig. 2
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Establishment process of SLE instance

Fig. 3
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Architecture of VLab human ear anatomy

Furthermore, the development of the 3D models was a meticulous process, beginning from scratch and executed using the Blender 3DFootnote 4 application. Expert input from otolaryngologists and human anatomy lecturers, along with recommended resources such as a human anatomy atlas (Rohen et al., 2018), instructional videos, anatomical mannequins, and mobile anatomy applications, guided the construction process. To ensure educational relevance, the development was aligned with the anatomy and physiology of hearing system course, incorporating key learning objectives and anatomical concepts essential for medical education. By adhering to this course, VLab provides a structured pedagogical learning experience, enabling students to engage with content that directly supports their coursework. By leveraging these resources, the 3D models were systematically categorized into five distinct regions: the outer ear, middle ear, inner ear, inner vestibule, and inner cochlea, as detailed in Table 1. This systematic classification ensured a comprehensive and accurate representation of the diverse anatomical components of the human ear within the VLab application.

Table 1 Categories of 3D human ear anatomy model
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The flow of the VLab Human Ear Anatomy is divided into two modes. The exploration mode allows lecturers and students to interact with the 3D models of each structure. Users can control the models with several features, including zooming in and out, rotating and tilting, focusing on selected structures, changing languages (Indonesia, English, and Latin), and showing information from selected objects. The second mode is laboratory mode. The laboratory mode is restricted to access, which means only students who are participating in a lecturer’s course in LMS can join this mode. Moreover, this mode contains four activities: pre-test, exploration, quiz (mid-test), and post-test. In this mode, the lecturers can create new classes, enable and disable activities, and set the duration and score percentage of each activity. Lecturers can also control students to start each activity simultaneously. Figure 4 depicts the screen flow diagram and Fig. 5 shows captures of the VLab application during exploration mode (top) and post-test (bottom) in laboratory mode.

Fig. 4
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Screen flow diagram

Fig. 5
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Preview of the VLab human ear anatomy application. (Top) view of exploration mode. (Bottom) view of post-test in laboratory mode

Study design and procedure

A cross-over repeated measures design was carried out to ensure all students have experience in both types of learning methods (conventional and virtual laboratory), which were modified from previous studies (Bonde et al., 2014; Thisgaard & Makransky, 2017). In this study, all activities during the experiment were performed via an online meeting application due to the pandemic situation. A total of 40 students participated in this study, who were randomly divided into two groups, namely Group A and Group B. This experiment consisted of two periods, as seen in Fig. 6. Each period had several activities with different learning environments.

Fig. 6
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Flow of study design using cross-over repeated measures

The first period conducted three main activities: a pre-test, learning activities, and a mid-test. Group A learned using the LMS, whereas Group B used the VLab Human Ear Anatomy application. The pre-test consisted of 20 questions related to the names of ear parts from all regions, with a duration of 10 min. The next activity lasted about 40 min. Group A learned about ear anatomy through discussion and reading materials provided in the LMS, while Group B learned through exploration of 3D ear anatomy via the VLab application. Additionally, the mid-test was performed to assess students’ knowledge of ear anatomy and its functionality based on materials given in the previous activity. The students needed to answer 15 questions in 10 min.

Two activities were carried out in the second period: learning activities and a post-test. The second period was conducted a week after the first period. In this period, Group A used the VLab application, whereas Group B learned using a conventional method. Similar to the first period, the learning activity took 40 min to learn ear anatomy, followed by 10 min of answering 20 post-test questions. Further, all students filled out a questionnaire to give their perceptions about both types of learning methods.

Participants

Forty students from two universities in Indonesia, Bandung Institute of Technology (ITB) and Universitas Airlangga, took part in this study. The students were selected from four different majors who are taking or have already taken an anatomy and physiology course. The four majors consisted of an undergraduate in electrical engineering (ITB), an undergraduate and a master’s in biomedical engineering (ITB), and an undergraduate in medicine (Universitas Airlangga). Table 2 illustrates the demographics of the participants.

Table 2 Demographic of participants
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Instrument for data analysis

The Kirkpatrick Model of Training Evaluation (Kirkpatrick & Kirkpatrick, 2006) was utilized to assess the learning effectiveness of both conventional and VLab approaches. The Kirkpatrick model has four levels of evaluation, including reaction (level 1), learning (level 2), behavior (level 3), and results (level 4). This study specifically focused on analyzing students’ perceptions and achievement after learning; hence, evaluation levels 1 and 2 were determined using SPSS Statistics.

In evaluation level 1, a Hedonic-Motivation System Adoption Model (HMSAM) (Lowry et al., 2013) was adopted to investigate students’ acceptance of the application. Seven variables comprise this model: Heightened Enjoyment (HE), Behavioral Intention to Use (BI), Perceived Usefulness (PU), Curiosity (CU), Control (CO), Perceived Ease of Use (PE), and Focused Immersion (FI). Table 3 shows the variables and question items, while Fig. 7 depicts the relationship between all seven variables. With respect to the model, our hypotheses are: (1) PE has a positive impact on PU; (2) PE has a positive impact on CU; (3) PE has a positive impact on HE; (4) PE has a positive impact on CO; (5) PU, CU, and HE have a positive impact on BI; and (6) CU, HE, and CO have a positive impact on FI.

Table 3 Variables of the evaluation Level 1
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Fig. 7
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Proposed model

A total of 22 questions with a 5-point Likert scale (point 1: strongly disagree; point 5: strongly agree) were used to analyze students’ perceptions based on HMSAM variables. Prior to conducting analysis, the Rank-Spearman correlation method and Cronbach’s alpha were used to evaluate the validity and reliability of all question items. Based on the validity test, the authors concluded that all question items were valid (Spearman Correlation Coefficient > R-Table (0.404), p-value < 0.005). In addition, the reliability test showed that all variables were deemed reliable (α > 0.8).

The outcome values of HMSAM variables were expected to be analyzed. Due to the limitations of the sample size (N = 40), multiple linear regression was selected to predict the outcome of response (dependent) variables based on their relationship with the explanatory (independent) variables. Several statistical tests were performed, including a normality test, an autocorrelation test, a simultaneous F-test, and a partial t-test.

Evaluation level 2 was established to assess students’ performance in both learning environments based on pre-test, mid-test, and post-test scores. Before performing the evaluation, the Shapiro–Wilk and Mauchly sphericity tests were carried out to determine the normality and homogeneity of students’ scores. Referring to the results, all data were considered normally distributed, and the variances of the differences were equal with a p-value > 0.05. Furthermore, various statistical tests were conducted to analyze students’ performance, such as repeated measures ANOVA and paired sample t-test. All test results are summarized in the next section.

Results

Students’ perception

A descriptive statistical analysis was conducted on the data (N = 40) to summarize students’ responses in general based on HMSAM variables. As seen in Table 4, the majority of students enjoyed learning with the application (HE: M = 4.61; SD = 0.45) and planned to use it in the future (BI: M = 4.52; SD = 0.67). Most students generally accepted the application based on the results of other variables, such as PU (M = 4.43; SD = 0.54), CU (M = 4.41; SD = 0.49), CO (M = 4.39; SD = 0.43) and PE (M = 4.10; SD = 0.59), which all received average scores higher than 4.00. However, the results revealed that the application did not fully impact immersion factors (FI: M = 3.90; SD = 0.73).

Table 4 Summary of Descriptive Analysis per Variables
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Figure 8 depicts the relationship between the HMSAM variables. Each dependent variable had a large variance, which was explained by the R-square (\({R}^{2}\)). The PE accounted for 22% (\({R}^{2}\)= 0.222) of PU, 31% (\({R}^{2}\) = 0.310) of CU, 26.4% (\({R}^{2}\) = 0.264) of HE, and 38.7% (\({R}^{2}\) = 0.387) of CO, according to the statistical results. Based on PU, CU, and HE, the PE affected 58.7% (\({R}^{2}\) = 0.587) to the BI. Furthermore, PE affected 40.6% (\({R}^{2}\) = 0.406) of FI based on CU, HE, and CO.

Fig. 8
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Final model

The relationship between PE and PU (H1: \(\beta\) = 0.471, p-value < 0.05) and CU (H2: \(\beta\) = 0.557, p-value < 0.01) was found to have a significant direct effect. Similarly, the results of PE directly on HE (\(\beta\) = 0.514, p-value < 0.01) and CO (\(\beta\) = 0.622, p-value < 0.01) showed a positive effect based on their coefficient values. With respect to the results, the effects of CU and CO yielded less than anticipated results. The relationships of CU to BI (\(\beta\) = 0.170, p-value > 0.1) and CO to FI (\(\beta\) = 0.024, p-value > 0.1) showed no significant effect. PU and HE, on the other hand, had a positive effect on BI. In addition, CU and HE appeared to have a positive effect on FI. Table 5 summarizes all the tests and findings.

Table 5 Summary of multiple linear regression analysis
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Learning achievement

The findings of statistical analysis using a paired-sample t-test were used to compare significant differences between pre-mid-test and mid-post-test scores from Group A and Group B. The mean value of the pre-mid-test of Group A was not significantly different during conventional mode (M = 3.55; SD = 20.40; t (19) = 0.77; p-value = 0.44), while the mid-post-test showed a better effect after using VLab (M = 36.10; SD = 16.86; t(19) = 9.57; p-value < 0.01). In contrast, there was a significant effect on students’ scores in Group B when using VLab (M = 25.50; SD = 18.69; t (19) = 6.09; p-value < 0.01) and the conventional mode (M = 31.00; SD = 20.05; t(19) = 6.91; p-value < 0.01).

Furthermore, paired-sample effect sizes were carried out to analyze the effect of VLab and conventional modes based on pre-mid-test and mid-post-test using Cohen’s d measure. The effect sizes of pre-mid-test and mid-post-test for Group A were 0.17 and 2.14. These findings indicated that during the first period, the effectiveness of learning was considered low (Cohen’s d < 0.2) while in the second period, it was high (Cohen’s d > 0.8). Furthermore, both effect sizes in Group B were significantly high (Cohen’s d > 0.8) during the first and second periods, with values of 1.36 and 1.54, respectively.

Another finding showed significant results in learning achievement for Group A and Group B based on descriptive analysis (see Fig. 9). In the first period, the results of the pre-test in Group A (conventional mode) on average (M = 49.60; SD = 16.87) were higher than in Group B (VLab mode) (M = 34.75; SD = 13.52). Compared to the mid-test, Group A’s average score was slightly higher than the previous test (M = 53.15; SD = 15.54), while Group B achieved a better average score (M = 60.25; SD = 17.18). Furthermore, the mean score in the second period unexpectedly increased to around 90. Thus, the average score of the post-test in Group B achieved a higher mean score (M = 91.25; SD = 7.04) compared to Group A (M = 89.25; SD = 8.15).

Fig. 9
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Comparison average students’ score of group A and group B

Discussion and limitations

This study aimed to evaluate students’ acceptance and learning achievement in ear anatomy using a 3D interactive mobile learning application in comparison to conventional learning approaches. The improvement in students’ knowledge of ear anatomy was demonstrated by statistical analysis of pre-test, mid-test, and post-test results. In addition, students’ perceptions of the application were measured by a questionnaire that was completed by all students.

A statistically significant effect showed that the application has generally fulfilled students’ intrinsic motivation based on seven variables of HMSAM. Overall, students were satisfied with the mobile VLab. The application positively enhanced students’ enjoyment; they found it useful as a supplementary learning material and plan to use it in the future. The use of 3D models allowed students to visualize and interact with each part of the anatomic structure (Layona et al., 2018; Preim & Saalfeld, 2018; Rosmansyah et al., 2021).

Furthermore, combining conventional learning approaches with VLab has positively improved students’ learning performance. According to the results, scores from both groups improved significantly from pre-test to post-test. As in the previous study, this combination of learning methods has improved learning outcomes (Reeves & Crippen, 2021). In addition, the prior study discovered that there was no significant difference in scores between the post-test and retention test, which was held 40 days after the main test (Bonde et al., 2014). Long-term retention of learning through the application is expected to be analyzed in the future.

Despite the positive results, this study has several limitations. For instance, the experiment was conducted at the end of the semester, when most medical students had exams and/or coursework. This may have had an impact on the limited number of medical students participating. To address this issue, students from different majors who were enrolled in human anatomy and physiology courses were recruited. Another limitation was communication issues between experts (otolaryngologists and human anatomy lecturers), programmers, and 3D modelers. This problem was mostly found during the development of the application. The 3D modelers took almost a year to create the model assets as closely as possible to references and expert suggestions. Furthermore, due to a lack of information about real laboratory situations while learning ear anatomy, gameplay was developed by adopting the laboratory flow of undergraduate engineering courses. Validation from medical associations is expected before the application can be commercialized.

A small but notable minority of students experienced some level of discomfort due to technical and application problems. Some students had difficulty installing the application due to its large size. Moreover, students found the 3D model less responsive when trying to rotate, tilt, or resize the assets, which was mostly discovered in older mobile versions. To overcome these issues, some students had to run the application through a mobile emulator.

Finally, students discovered that the application needs more improvement. First, students suggested adding various types of multimedia, such as audio and video. Prior studies have found that the use of voiceover helps students follow the application flow (Erolin et al., 2019). Second, more learning materials are expected to be included in the application, such as physiology, symptoms, and diseases. These learning materials are essential for preparing students for clinical medicine and surgery. Third, the variety of elements of gamification might increase students’ interest, enjoyment, and motivation. Some popular gamification elements, such as leaderboards, badges, and collaboration, could possibly be added to the application (Subhash & Cudney, 2018). In addition, the use of adaptive personalization approaches might help students to ensure that the learning materials are suitable for learning (Karpinskyj et al., 2014).

Conclusion

This study presents an interactive 3D mobile-based learning application called VLab Human Ear Anatomy. The aim of this application is to help students learn and increase their knowledge of human ear anatomy. The concept of this application relies on DRM and SLEEG tools, which serve as guidelines to ensure optimal pedagogy and engagement for learners and enhance learning effectiveness and efficiency. The 3D model was constructed based on expert guidance, learning materials, and aligned with the anatomy and physiology of hearing system course. Additionally, the application underwent rigorous testing and iterative refinement based on feedback from experts, ensuring both accuracy and usability. Furthermore, community collaboration with medical professionals and educators was conducted to compare the VLab experience with conventional teaching method, providing valuable insights into its applicability in real-world education.

To evaluate the efficacy of this application, forty students participated to ensure that all students had experience in two learning methods, including VLab and conventional learning approaches. Based on the students’ responses, it was found that the application is useful, easy to use, and enjoyable to learn with. The application helped them to be more focused on learning and could be beneficial in the future. In addition, students’ scores showed that sequencing VLab with conventional learning methods is a promising teaching and learning method for improving students’ learning achievements. Future research could explore the application of VLab in other areas of medical education and investigate its long-term impact on student performance and motivation. Additionally, the development of similar applications for other anatomical systems could further enhance the overall quality of medical education.

To conclude, the VLab application has the potential to enhance practical experience and student motivation. Validation from medical associations is expected before the application can be commercialized. With further improvements, such as the addition of multimedia, supplementary learning materials, and adaptive personalization, this application can become an even more effective tool in supporting human ear anatomy education.