Exploring the effectiveness of 2D images and 3D models to achieve cognitive levels of Bloom in first-year anatomy students

Exploring the effectiveness of 2D images and 3D models to achieve cognitive levels of Bloom in first-year anatomy students | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the effectiveness of 2D images and 3D models to achieve cognitive levels of Bloom in first-year anatomy students Marjan Maldoy, Tine van Daal, Vicky Vandenbossche, Ian Garcia, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8584889/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background There is a critical need for a defined pedagogical framework for using computer-based 2D images (2DI) and 3D models (3DM) in anatomy education. While 3DM are considered to be more or evenly effective as 2DI, the specific knowledge types they support remain unclear. The interactive nature of 3DM promotes engagement and spatial understanding, potentially aiding higher cognitive levels. This study aims to identify which cognitive levels of Bloom’s taxonomy benefit most from 2DI and where 3DM provide greater advantages. Methods 58 first-year physiotherapy students participated in a study with three phases: (1) a pre-test to capture sex, age, spatial ability, and prior knowledge, (2) a learning phase with pretraining and independent study using either 2DI or 3DM of the temporal bone, and (3) a post-test to measure knowledge gained at level 1 (Remembering), level 2 (Understanding) and level 3 (Applying). Results Students in the 3D condition are 5.07 times more likely to excel at "Remembering" (Level 1). Results for other cognitive levels are less clear: 2DI are as effective as 3DM for "Understanding" (Level 2), while 3DM may lead to better outcomes for "Applying" (Level 3), though results are uncertain due to mixed effects per score. Conclusion The findings suggest that 3DM enhance recognition and recall at Level 1 (the "Remembering" level) of Bloom’s Taxonomy, with mixed effectiveness at higher cognitive levels. This may be due to factors such as cognitive load and spatial ability. 3DM can be used effectively in anatomy education to enhance recognition and recall. anatomy education Taxonomy of Bloom 3D models 2D images cognitive levels Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Educators and students worldwide increasingly use two-dimensional (2D) and three-dimensional (3D) digital tools to visualize anatomy (Pettersson et al., 2024 ). Previous studies have shown that traditional human dissections, considered the gold standard in anatomy education, can be effectively enhanced with technology such as computer-based 2D images (2DI) and computer-based 3D models (3DM) (Casallas & Quijano, 2018 ; Codd & Choudhury, 2011 ; Zibis et al., 2021 ). Wickramasinghe et al. ( 2022 ) stress the need for these tools to align with contemporary learning environments. However, the importance of a well-defined pedagogical framework to guide the effective integration of these technologies is highlighted (Wickramasinghe et al., 2022 ; Alexander et al., 2024 ). Consequently, the discussion has shifted from whether to digitize anatomical education to how to integrate it into the anatomy curriculum (Azer& Azer, 2016 ; Clunie et al., 2018 ; Xiao & Evans, 2022 ). Computer-based 3DM are digital representations of objects created using computer software, allowing for visualization, manipulation, and analysis in three dimensions by implementing them in digital learning environments. These models are widely used in various fields, including education and health care (Adnan & Xiao, 2023 ). Although computer-based 3DM are displayed on a 2D screen and viewed as monoscopic projections, they can still provide subtle depth cues. As a result, they are preferred over 2DI for understanding size differences and spatial relationships (Yohannan et al., 2024 ). Computer-based 3DM are more accessible than virtual, augmented, and mixed reality, as they require no expensive software or specialized glasses and are easy to use in various settings. Multiple studies claim that the use of computer-based 3DM is more effective to learn human anatomy than the use of 2DI (Chauhan et al., 2023 ; Casallas & Quijano, 2018 ; Eroğlu et al., 2023 ; Haque et al., 2021 ; Sun et al., 2022 ; Triepels et al., 2020 ; Yammine & Violate, 2015; Yohannan et al., 2024 ). Yohannan et al. ( 2024 ) found that 3DM (in both stereoscopy and monoscopy) were more effective for teaching radiological anatomy compared to the control group using 2DI. Yammine and Violato ( 2015 ) and Triepels et al. ( 2020 ) reported that students exhibited a preference for 3D tools over traditional instructional methods. Moreover, their findings indicate that the integration of 3DM in anatomy education contributes to enhanced learning outcomes. Eroğlu et al. ( 2023 ) stated that this effect was more pronounced for complex anatomical structures, such as the male genitalia, than for simpler structures, such as the liver. However, Berney et al. ( 2015 ) found no differences in performance after learning the functional anatomy of the scapula from either 3DM or 2DI. The authors concluded that the building of effective mental representations of the scapula and its movements was possible for the learners, regardless of the representation format. These results are supported by other studies such as Khot et al ( 2013 ), Preece et al. ( 2013 ) and Vandenbossche et al. ( 2023 ). The latter study suggested that the learning material, the osteology of the pelvis, may have lacked sufficient complexity to demonstrate potential advantages, a point similarly noted by Eroğlu et al. ( 2023 ). A major limitation of these studies is their failure to determine the optimal integration of computer-based 2DI and 3DM in anatomy curricula, particularly concerning the cognitive levels required by specific learning objectives. The effectiveness of anatomy education is commonly evaluated using student satisfaction surveys and general knowledge assessments (Clunie et al., 2018 ). However, these assessments frequently fail to align with the intended cognitive levels of the learning objectives, thereby inadequately evaluating the depth of understanding and skill development fostered by the learning task. Constructive alignment emphasizes that learning tasks must align with learning objectives and its cognitive levels, prioritizing the educational framework over the isolated effectiveness of any learning method (Biggs, 2003 ). 2DI and 3DM each have their specific characteristics and should be utilized based on the knowledge they develop and the learning objectives that need to be achieved (Azer & Azer, 2016 ). Bloom’s Taxonomy (Bloom, 1956 ; Anderson & Krathwohl, 2001 ) is employed in anatomy education to align assessments with learning objectives (Choudhury & Freemont, 2017 ) and teaching methodologies (Thompson & Lake, 2023 ). The Blooming Anatomy Tool (BAT) was developed to specify Bloom’s Taxonomy for the design and evaluation of assessments in the anatomical sciences (Thompson & O’Loughlin, 2015). In the BAT, knowledge (level 1) and comprehension (level 2) were categorized as lower order levels, and application (level 3) and analysis (level 4) as higher order levels of cognition. The first level represents the foundational stage of learning. At this level, learners focus on identifying, recalling, or memorizing facts, definitions, and basic information, such as recognizing structures on a model. Level 2 represents the ability to comprehend concepts, spatial organization, or pathways by describing, explaining or summarizing the learned material. To reach the third level, students should be able to apply the new information to predict or infer the interaction between body systems. For example, describing brain irrigation corresponds to Bloom's Level 2, while predicting the impact of blood flow disruption reaches Level 3. The fourth level in the BAT contains the competence to analyze a situation regarding the taught subjects, such as interpreting brain images after a stroke. A comprehensive overview is presented in Table 1 . Table 1 Cognitive levels in anatomy education based on Bloom ( 1956 ), Anderson & Krathwohl ( 2001 ) and Thompson & O’Loughlin (2015) Lower order levels Higher order levels Cognitive level 1. Knowledge 2. Comprehension 3. Application 4. Analysis Cognitive verb Remember Understand Apply Analyze Skills Identify, repeat, recall, memorize, list, recognize Describe, distinguish, explain, summarize Infer, predict Judge, critique, interpret Types of anatomical information Definitions, facts, straightforward recall Concepts, spatial organization, pathways, blood supply, innervation Interaction between body systems, functional aspects beyond memorization Applying information to a new situation, interpretation of anatomical images, clinical judgement Technology-enhanced learning facilitates students' capacity to achieve higher levels of cognition by fostering active learning (Elzainy & Sadik, 2022 ). Learning environments that incorporate manipulable 3DM are particularly effective in promoting active learning due to their interactive nature, which fosters engagement and enhances spatial comprehension by providing immediate spatial insights (Cabrera-Duffaut et al., 2024 ). As the student rotates the 3DM in 360 degrees, the student is handling the object itself while learning. The embodied cognition theory suggests that physical interaction with objects can foster meaningful learning (Fiorella, 2021 ). Research indicates that early manipulation of 3DM activates the motor cortex during subsequent mental rotation tasks involving the same objects (Kosslyn et al., 2001 ). This activation does not occur when objects are not manipulated beforehand. These findings suggest that 3DM stimulate increased brain activity, thereby supporting deeper cognitive engagement and learning. Moreover, 3DM can provide a clear and explicit external representation of anatomical structures for learners with limited spatial skills (Hays, 1996 ; Jang et al., 2017 ). These findings suggest that using computer-based 3DM for learning anatomy facilitate achieving higher cognitive levels, such as application (level 3). However, empirical evidence underpinning this claim is currently lacking. Therefore, this study will explore the effectiveness of computer-based 2DI and computer-based 3DM in anatomy education in attaining lower and higher cognitive levels as defined by Bloom's taxonomy. The research aims to identify the cognitive levels where 2DI are most effectively employed, as well as those where 3DM provide superior benefits. Materials and methods To examine the effectiveness of 2DI and 3DM in attaining specific learning outcomes, an experiment was set-up. This section details the methodology, including participant information, research context, study design, and instruments. The operationalization of key concepts and the analysis approach are also discussed. A walk-through is available online, which explains the preparation, cleaning, analysis, and reporting of the data. The R code, along with all the study materials and raw datasets, is available via the Open Science Framework. Participants and context Fifty-eight first-year students of the bachelor program ‘Revalidation Sciences and Physiotherapy’ were recruited at the University of Antwerp, Belgium. All students participated on a voluntary basis. The volunteers consisted of 13 males and 16 females in the 2D group, and 15 males and 14 females in the 3D group, with a mean age of approximately 20 ± 3.09 years old (all means will be reported with standard deviation). At this stage of their studies, students had completed a course in anatomical terminology and basic anatomy of the musculoskeletal system containing osteology, arthrology and myology. The osteology of the skull was not seen in depth and described superficial structures only. This allowed us to assume that the students had notions of the temporal bone without insight or orientation skills, which gave us the opportunity to use this bone in our study. Based on the study of Vandenbossche et al. ( 2023 ) and Eroğlu et al. ( 2023 ), the temporal bone was considered as complex enough to show differences in 2D versus 3D learning. Students were randomly assigned to the 2D and 3D group. Most of the students ( n = 44) participated in a computer class at the campus, with each student utilizing an individual workstation. Fourteen students participated in the Antwerp Social Lab, where their eye movements, galvanic skin responses, and screen activity were recorded for related research that is not included in this study. Study design The study had an experimental design that consisted of three phases: (1) a pre-test to capture sex, age, spatial ability and prior anatomical knowledge, (2) a learning phase containing a pretraining and an independent learning phase during which students learned about the temporal bone with either computer-based 2DI or 3DM, and (3) a post-test to measure students’ post anatomical knowledge of the temporal bone (see Fig. 1 ). Phase 2 encompasses the intervention, as detailed below. In the learning phase, students first watched a knowledge clip focusing on the temporal bone. This video, presented in the native language, provided an overview of the orientation and structure of the temporal bone as a form of pre-training (see Mayer & Fiorella, 2021 ). Also, attention was drawn to the passages of nerves and blood vessels through and along the temporal bone and to some major muscle attachments. For this, drawings were used from Standard Anatomy, a free anatomical platform ( https://www.anatomystandard.com/ ). Students were not allowed to pause the video. The full knowledge clip can be consulted at the Open Science Framework. Next, students were presented a short video instruction on the learning goals they should accomplish and practical instructions about the use of the anatomical models in the digital learning environment. After these instructions, students used the environment to explore the complexity of the temporal bone at their own pace for maximal 25 minutes. The digital learning environment was made using the Genially© application ( www.genially.com ) and surface scanned bones uploaded in Sketchfab© ( www.sketchfab.com/marjanmaldoy ). Informed consent was obtained for these bones from the body donor program of the University of Antwerp. The digital learning environment contained either 2DI (see environment in native language here) or 3DM (see environment in native language here) of the temporal bone, depending on the condition. Students were able to rotate the 3DM in 360 degrees. Annotations of the different parts of the temporal bone could be selected by the student. The 2DI consisted of annotated screenshots taken from the 3DM. Students were allowed to freely navigate between the different planes. Although the structures shown on the bone were described orientatively, the orientation of the 2DI and 3DM was not separately clarified. This way, both learning environments were aligned to ensure comparable research environments as suggested by Cook ( 2005 ). Students were not allowed to make notes during the learning phase. Instruments and variables Spatial ability Spatial ability was measured paper-based using the 24-item Mental Rotation Test (MRT) described by Peters et al. ( 1995 ). The MRT captures spatial cognition and is used widely in anatomy education studies (e.g. Vandenbossche et al., 2023 ; Berney et al., 2015 ; Jang et al., 2017 ). Each item presents a configuration of blocks (see Fig. 2 ) and asks students to indicate which alternatives represent a rotated version of that same configuration (two correct options). Two example items were given, and three items were used to practice. Upon comprehensive understanding of the task, the test commenced with 12 out of the 24 items to be completed within three minutes. After a break of two minutes, the remaining 12 items were questioned in three minutes. Each item was scored as correct if two and only two correct variations were marked. As the MRT has already been validated in previous research (Peters et al., 1995 ), only the internal consistency of the MRT was checked. To be sufficiently reliable, Cronbach’s alpha should be at least 0.7 (Tavakol & Dennick, 2011 ). The internal consistency of the MRT was satisfactory ( 𝛼 = 0.83) and was not improved by omitting an item. Consequently, scores across all MRT items were summed per student to create the variable ‘Spatial ability’ (12.88 ± 4.61). This variable was standardized before analysis. Prior anatomical knowledge The prior knowledge test consisted of eight questions: three aimed at capturing students’ prior knowledge about the temporal bone and five assessed students' prior understanding of general anatomy. The general knowledge test included terminology (e.g., identifying the Latin name for a pointed bony protuberance). It was assumed that students possessed a solid foundational knowledge of general anatomy, deemed essential for comprehending the learning material. Since the items evaluated distinct aspects of prior anatomical knowledge, internal consistency was not calculated. A dummy variable, ‘General Prior Knowledge’, was created to classify students based on their performance: those scoring at least 4 items correct were coded as 1 ( n = 21, 36.2%) and those below this threshold coded as 0 (< 4 correct; n = 37, 63.8%). Prior knowledge of the temporal bone anatomy was assessed using three items (e.g., identifying the parts of the temporal bone). Students were expected to have minimal knowledge of this bone, with scores of at most one correct response out of three anticipated. A dummy variable, ‘Prior Knowledge about the Temporal Bone’, was created based on these three items. Students who failed all items were coded as 0 ( n = 50, 86.2%), while those answering at least one item correctly were coded as 1 ( n = 8, 13.8%). Post knowledge about the temporal bone at the level of remembering, understanding and applying Students’ anatomical knowledge of the temporal bone after intervention was measured using a paper-based test. In total, twenty-nine questions tapped into what students remembered about the temporal bone (level 1 of Bloom), their understanding of the temporal bone (level 2 of Bloom), and to what extent they were able to apply their knowledge about the temporal bone (level 3 of Bloom). Classification of the questions was checked by three experts in anatomy education using the Blooming Anatomy Tool (Thompson & O’Loughlin, 2015). To assess the inter-rater reliability, Fleiss kappa was calculated using the R-package irr (Gamer et al., 2019 ; version 0.84.1). Inter-rater reliability across all levels was excellent ( K = 0.78). Raters strongly agreed on the classification of items into level 1 ( K = 0.95) and level 2 ( K = 0.74) of Bloom’s taxonomy. However, there was only moderate agreement regarding classification of items into level 3 ( K = 0.44). One rater classified items 5a, 5b, 5c and 7 as belonging to level 2 (understanding), while the two other raters classified these items as level 3 (application). Following a discussion, the four items were classified as belonging to level 3. All items, the analysis on inter-rater reliability, and the final classification of the items are found in the walk-through. Students’ knowledge about the temporal bone at the level of remembering, understanding and applying was measured using respectively 15, 10 and 4 items. The quality of the items was examined separately per level of Bloom. First, items answered correctly by (almost) all students were deleted as these items were not able to discriminate between students. Then, classical test theory was applied whereby items showing item-total correlations lower than 0.20 were excluded from further analysis (De Champlain, 2010 ). This analysis was repeated until all items showed an acceptable correlation with the total score. Finally, the internal consistency of the remaining items was checked. The remaining items were summed per level of Bloom to create the variables ‘Remembering’ (6 items, 𝛼 = 0.69), ‘Understanding’ (5 items, 𝛼 = 0.65) and ‘Applying’ (3 items, 𝛼 = 0.68). All analyses and intermediate results can be consulted in the walk-through. As the variables are not normally distributed (see Fig. 3 ), the variables ‘Remembering’, ‘Understanding’, and ‘Applying’ were recoded into ordered categorical variables. The variable ‘Remembering’ is composed of four ordered categories. These categories represent scores between 0 and 2 (category D), a score of 3 (category C), a score of 4 (category B) and scores of 5 or 6 (category A). The variable ‘Understanding’ contains five ordered categories, reflecting scores of 0 (category E), 1 (category D), 2 (category C), 3 (category B) and 4 or 5 (category A). The variable ‘Applying’ was not recoded, but was treated as an ordered ordinal variable whereby each score represents another category. Data analysis To examine the impact of condition on students’ remembering, understanding and applying of knowledge about the temporal bone, cumulative link models were fitted in R using the ordinal package (Christensen, 2023 ; version 2023.12–4.1). This type of model accounts for the ordinal nature of the dependent variables by estimating intercepts (thresholds) that express the probability of belonging to a specific category or lower. Hence, the number of thresholds to be estimated is one less than the number of categories of the dependent variable. For instance, the variable ‘Remembering’ consisted of four categories. Consequently, three thresholds were estimated that express respectively the probability of belonging to category D (threshold D|C), category C or lower (threshold C|B), and category B or lower (threshold B|A). These thresholds can be used to construct the probability of belonging to each category of the variable ‘Remembering’. Two cumulative link models were fitted per dependent variable. The two models differed in assuming that the impact of condition was the same across all thresholds (proportional odds models) or differed across thresholds (partial proportional odds model). In the latter case, the impact of condition on the probability of belonging to each category of the dependent variable varies across categories. Both models comprised five predictors: ‘Condition’, ‘Location’, ‘Spatial ability’ and the two variables that capture prior knowledge (‘General Prior Knowledge’ and ‘Prior Knowledge about the Temporal Bone’). Spatial ability and prior knowledge were controlled for as literature suggests that these impact anatomy learning (e.g., Azer & Azer, 2016 ; Berney et al., 2015 ) and should be taken into account when estimating the impact of condition. As the procedures for data collection differed between students who participated individually in the lab or those who joined a group session at the campus, this was accounted for by adding the dummy variable ‘Location’ as a predictor. Both models were compared, and the research question was answered based on all the estimates of the best model. In order to estimate the average effect of condition on the dependent variables, it was necessary to take into account the non-linearity of the estimates. This was achieved by calculating the average probability of belonging to each category of the dependent variable for both conditions (Agresti & Tarantola, 2018 ; Long et al., 2021). As students in both conditions differed in terms of their ‘Spatial ability’, ‘General Prior Knowledge’, and ‘Prior Knowledge about the Temporal Bone’ (for further details, please refer to the walk-through), the average probabilities were predicted based on a balanced data grid. This grid contained all possible combinations of condition and the three dummy predictors, with ‘Spatial ability’ fixated at three specific values (the mean, -1 SD , and + 1 SD ) using the marginal effects package in R (Arel-Bundock et al., 2024 ; version 0.24.0). Subsequently, the estimated probabilities were aggregated per condition (and category) and 95% confidence intervals were estimated. The walk-through presents a detailed account of all the steps taken to estimate the average effect of condition. To draw inferences about the population, two pieces of information were considered: the effect size, expressed as an odds ratio per category of the dependent variable, and the 95% confidence interval of these odds ratios. Odds ratios of 1.5 (or smaller than 0.67), 2.5 (or smaller than 0.41) and 4 (or smaller than 0.26) were interpreted as indicating a small, medium and large effect size respectively (Maher et al., 2013 ). Results The results section describes the average effect of condition. The results are expressed in probabilities and odds ratios to facilitate interpretation for the reader. The appendix presents all model estimates, including those regarding the impact of spatial ability, prior knowledge and the location in which students participated in the study. Figure 4 summarizes the average impact of condition on remembering, understanding and applying knowledge about the temporal bone. Impact of condition on remembering knowledge about the temporal bone On average, students in the 3D condition are more likely to be classified in a higher category of ‘Remembering’ than students in the 2D condition (see Fig. 4 ). For example, the probability of belonging to category A is, on average, predicted to be 31.9% for students in the 2D condition and 70% for students in the 3D condition. The odds of belonging to category A of ‘Remembering’ are, on average, 5.07 times higher for a student in the 3D condition than for a student in the 2D condition. The magnitude of the odds ratio indicates a large effect of condition on remembering of knowledge about the temporal bone. As the 95% confidence interval of the odds ratio for category A excludes 1 (see Table 2 ), it is reasonable to assume that the impact of condition on ‘Remembering’ will also be observed in the general population. A similar conclusion may be drawn based on the odds ratios and 95% confidence intervals of the other categories (see Table 2 ). Therefore, it is concluded that learning with 3DM results in a higher probability of remembering knowledge about the temporal bone than learning using 2DI. Table 2 Average probability of belonging to each category of ‘Remembering’ for students in the 2D and 3D condition and odds ratio per category. 95% confidence intervals are presented between brackets. Category Average probability per condition [95% CI] Odds ratio [95% CI] 2D 3D Remembering (level 1) A 0.32 [0.12–0.52] 0.70 [0.54–0.86] 5.07 [5.83–8.99] B 0.39 [0.25–0.54] 0.23 [0.10–0.36] 0.46 [0.36–0.47] C 0.17 [0.05–0.28] 0.04 [0.00-0.09] 0.23 [0.04–0.24] D 0.12 [0.03–0.21] 0.02 [0.00-0.05] 0.16 [0.00-0.18] Understanding (level 2) A 0.38 [0.18–0.58] 0.31 [0.09–0.52] 0.72 [0.46–0.79] B 0.23 [0.11–0.35] 0.22 [0.11–0.33] 0.97 [0.94–1.04] C 0.22 [0.10–0.33] 0.24 [0.11–0.36] 1.13 [1.10–1.17] D 0.09 [0.02–0.16] 0.11 [0.03–0.20] 1.28 [1.29–1.30] E 0.09 [0.01–0.16] 0.12 [0.03–0.22] 1.45 [1.46–1.77] Applying (level 3) A 0.24 [0.06–0.42] 0.42 [0.19–0.65] 2.25 [2.54–3.40] B 0.21 [0.05–0.37] 0.40 [0.20–0.59] 2.47 [2.43–4.95] C 0.38 [0.20–0.55] 0.05 [0.00-0.11] 0.08 [0.00-0.10] D 0.17 [0.05–0.30] 0.14 [0.03–0.25] 0.78 [0.69–0.77] Impact of condition on understanding knowledge about the temporal bone Students in the 3D condition are, on average, less likely to be classified in a higher category of ‘Understanding’ than students in the 2D condition (see Fig. 4 ). For example, the average predicted probability of belonging to category A is 38% for students who learn in 2D and 31% for students who learn with 3DM. The odds of a student in the 3D condition to be classified in category A are, on average, 0.72 times higher than for a student in the 2D condition. Thus, students in the 2D condition are more likely to belong to category A than their peers who studied in 3D. Although the 95% confidence interval of the odds ratio excludes 1 (see Table 2 ), the magnitude of the odds ratio of 0.72 signifies a lack of effect. A similar outcome is observed for the other categories of ‘Understanding’ (see Table 2 ). It is concluded that studying with 2DI or 3DM leads to a similar understanding of knowledge about the temporal bone. Impact of condition on applying knowledge about the temporal bone Students in the 3D condition are, on average, more likely to belong to a higher category of ‘Applying’ than students in the 2D condition (see Fig. 4 ). The average predicted probability of belonging to category A of ‘Applying’ is respectively 24% and 42% for students in the 2D and 3D condition. The odds of being classified in category A are, on average, 2.25 times higher for students who learned with 3DM. The magnitude of the odds ratio points to a small effect of condition on applying knowledge about the temporal bone. As the 95% confidence interval around the odds ratio also excludes 1 (95% CI: 2.54–3.40), it is reasonable to assume that the impact of condition on ‘Applying’ will also be observed in the general population. The same result is found regarding categories B and C of ‘Applying’ for which respectively a medium effect of condition is found (see Table 2 ). Only with regard to the lowest category of ‘Applying’ (category D), the magnitude of the odds ratio points to a lack of effect (see Table 2 ). Hence, there are indications that studying with 3DM leads to better application of knowledge about the temporal bone, but there is uncertainty regarding this finding. Discussion Previous research has examined the overall effectiveness of 2DI and 3DM in facilitating general knowledge acquisition in the context of anatomy education. However, it often overlooked the alignment with specific cognitive outcomes as defined by Bloom's taxonomy (Bloom, 1956 ; Anderson & Krathwohl, 2001 ; Thompson & O’Loughlin, 2015). This may contribute to the inconsistent findings regarding the relative effectiveness of 2DI and 3DM. To address this research gap, the present study employed an experimental design involving 58 first-year bachelor students ‘Revalidation Sciences and Physiotherapy’. Following a learning phase using either computer-based 2DI or 3DM, students’ knowledge of the temporal bone was assessed. The aim of this study is to determine the cognitive levels for which 2DI are most effective and those for which 3DM offer superior benefits. The findings of this study indicate that 3DM are particularly effective for first-year students in enhancing cognitive outcomes at the level of "Remembering" (Level 1) of Bloom's taxonomy. 3DM provide immediate insight into the overall form, orientation, and key characteristics of anatomical structures, thereby facilitating efficient recall. This aligns with findings by Yammine & Violate (2015), Zibis et al. ( 2021 ) and Park et al. ( 2019 ), who highlighted that 3DM improve the identification of structures. Similarly, Anderson et al. ( 2019 ) observed greater object recognition, as measured by electroencephalography, in students learning from 3DM compared to those utilizing 2DI. Zilverschoon et al. ( 2022 ) found that junior medical students scored significantly higher and were significantly faster when using a 3D study tool compared to students using a 2D atlas in an open book examination. These results were explained by the possible influence of the increased mental steps it takes to convert a 2DI into a 3D mental representation. Moreover, Yohannan et al. ( 2024 ) assert that the effectiveness of visualizations for basic recall is significantly influenced by the presence and quality of depth cues. This suggests that depth perception contributes to improved recall, the first level of Bloom. Nevertheless, the mechanisms by which students learn from 3DM remain poorly understood (Azer & Azer, 2016 ), and the underlying learning processes have yet to be thoroughly examined. The results for the other two cognitive levels are less conclusive. For “Understanding” (level 2), 2DI are as effective as 3DM. At the level of "Applying" (level 3), 3DM show greater effectiveness, although these results are fraught with uncertainty. Although it was expected that 3DM would enhance the attainment of higher cognitive levels, the results of this study do not provide clear empirical support for this claim. Park et al. ( 2019 ) suggest that 3DM may be less effective in promoting deeper anatomical understanding, as demonstrated in open- and closed-book examinations using 2D or 3D atlases. Based on neurological data on stereopsis, Anderson et al. ( 2019 ) conclude that a combination of 2DI and 3DM may improve learning, retention, and transfer based on neurological data. However, empirical evidence on which combination works for whom is lacking. The mixed effectiveness of 3DM may be due to their complexity, which can cause cognitive overload, especially for students with limited spatial skills (Labranche et al., 2022 ). Contrarily, Berney et al. ( 2015 ) posits that students with lower spatial ability may benefit more from these 3D visualizations, whereas those with higher spatial ability may experience a disadvantage when using 3DM. Given this ambiguity, variations in both spatial and cognitive abilities may account for the inconsistent results observed in the average student population. The role of these factors in learning was not the focus of this study, although the study did control for spatial ability. Future research should investigate how spatial ability affects 2D and 3D learning. Studies assessing the effectiveness of 3DM predominantly assessed knowledge at the first level of Bloom (e.g. Vandenbossche et al., 2023 ; Zibis et al., 2021 ) or provide insufficient detail about the specific content assessed in post-knowledge tests (e.g. Haque et al., 2021 ; Eroğlu et al., 2023 ; Casallas & Quijano, 2018 ; Park et al., 2019 ). Crowther et al. ( 2024 ) noted that anatomy education often overlooks higher cognitive levels, resulting in assessments that inadequately address advanced domains. This might be explained by the challenging task of developing effective higher-order questions, which often require alternatives to traditional multiple-choice formats. This highlights the need for greater transparency in assessment design and sharing of the knowledge tests that are used for research purposes. Eroglu et al. (2023) emphasize that 3DM are particularly effective for novice learners; however, the specific cognitive levels examined in their study remain ambiguous. Most research on the effectiveness of 3DM focuses on first-year students, as their limited prior knowledge provides a controlled basis for assessment. In this study as well, first-year students were selected due to the same rationale. However, caution is warranted when generalizing these findings to broader populations. While 3DM appears to be less effective at higher cognitive levels in first-year students, its impact may differ for more experienced learners, as their prior knowledge could facilitate deeper cognitive processing. Limitations This study has some limitations. Firstly, the study population was relatively small and the number of items per Bloom level was limited. This may constrain the generalizability of the results. Secondly, to enable group comparisons, participants were required to follow a standardized learning approach that may not reflect their typical study methods. They were given 25 minutes to complete the course without taking notes, and both groups were restricted to screen-based materials, potentially disadvantaging those who prefer paper-based resources such as atlases or textbooks. Thirdly, this post-test was administered in a paper-based (2D) format, which might have favored the 2D group. Conclusion The results of this study indicate that 3DM are effective for facilitating recognition and recall at the level of "Remembering" (Level 1) in the Taxonomy of Bloom, while their advantages at higher cognitive levels are less clear. Possible reasons for this are searched in multiple factors such as cognitive load and spatial abilities. However, these findings may be different in experts, so vigilance is required, and further research is needed. Future research should prioritize transparent qualitative assessments, include questions across all cognitive levels, and examine the learning processes underlying these methods in both novice and experienced learners. Declarations Ethics approval and consent to participate All methods were carried out in accordance with national and European guidelines and regulations. The study was approved by the Ethics Advisory Committee for Social and Human Sciences (EASHW) of the University of Antwerp (SHW_2022_136_1) . Written informed consent was obtained from all subjects. Funding declaration Not applicable. Clinical trial number Not applicable. Consent for publication All participants gave written informed consent for publication. Availability of data and materials A walk-through is available online , which explains the preparation, cleaning, analysis, and reporting of the data. The R code, along with all the study materials and raw datasets, is available via the Open Science Framework. Competing interests The authors declare that they have no competing interests. Authors' contributions M.M. conceived and designed the study under the supervision of T.V.D. and L.U., with intellectual input from V.V. M.M., L.U. and I.G. developed the study materials and learning resources. T.V.D. performed the statistical analyses, interpreted the data, and drafted the results section. M.M. drafted the main manuscript text. L.V.N. and V.V. critically revised the manuscript for important intellectual content. 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BMC Med Educ. 2024;24(1):45. https://doi.org/10.1186/s12909-023-04987-7 . Preece D, Williams SB, Lam R, Weller R. Let's get physical: advantages of a physical model over 3D computer models and textbooks in learning imaging anatomy. Anat Sci Educ. 2013;6(4):216–24. https://doi.org/10.1002/ase.1345 . Sun W, Chen H, Zhong Y, Zhang W, Chu F, Li L, Chen Y, Wang X, Wang Q, Wang Y, Wei Y, Liu L, Xu Y. Three-Dimensional Tooth Models with Pulp Cavity Enhance Dental Anatomy Education. Anat Sci Educ. 2022;15(3):566–75. https://doi.org/10.1002/ase.2085 . Tavakol M, Dennick R. Making sense of Cronbach’s alpha. Int J Med Educ. 2011;2:53–5. 10.5116/ijme.4dfb.8dfd . Thompson AR, Lake LPO. Relationship between learning approach, Bloom’s taxonomy, and student performance in an undergraduate Human Anatomy course. Adv Health Sci Educ. 2023;28(4):1115–30. 10.1007/s10459-023-10208-z . Thompson AR, O'Loughlin VD. The Blooming Anatomy Tool (BAT): A discipline-specific rubric for utilizing Bloom's taxonomy in the design and evaluation of assessments in the anatomical sciences. Anat Sci Educ. 2015;8(6):493–501. https://doi.org/10.1002/ase.1507 . Triepels CPR, Smeets CFA, Notten KJB, Kruitwagen RFPM, Futterer JJ, Vergeldt TFM, Van Kuijk SMJ. (2020). Does three-dimensional anatomy improve student understanding? Clinical Anatomy (New York, N.Y.), 33 (1), 25–33. https://doi.org/10.1002/ca.23405 Vandenbossche V, Valcke M, Willaert W, Audenaert E. From bones to bytes: Do manipulable 3D models have added value in osteology education compared to static images? Med Educ. 2023;57(4):359–68. https://doi.org/10.1111/medu.14993 . Wickramasinghe N, Thompson BR, Xiao J. The Opportunities and Challenges of Digital Anatomy for Medical Sciences: Narrative Review. JMIR Med Educ. 2022;8(2):e34687. https://doi.org/10.2196/34687 . Xiao J, Evans DJR. Anatomy education beyond the Covid-19 pandemic: A changing pedagogy. Anat Sci Educ. 2022;15(6):1138–44. https://doi.org/10.1002/ase.2222 . Yammine K, Violato C. A meta-analysis of the educational effectiveness of three-dimensional visualization technologies in teaching anatomy. Anat Sci Educ. 2015;8(6):525–38. https://doi.org/10.1002/ase.1510 . Yohannan DG, Oommen AM, Kumar AS, Devanand S, UT MR, Sajan N, Thomas NE, Anzer N, Raju NK, Thomas B, Rajan JE, Govindapillai UK, Harish P, Kapilamoorthy TR, Kesavada CK, Sivaswarmy J. Visualization matters – stereoscopic visualization of 3D graphic neuroanatomic models through AnaVu enhances basic recall and radiologic anatomy learning when compared with monoscopy. BMC Med Educ. 2024;24(1):932. https://doi.org/10.1186/s12909-024-05910-4 . Zibis A, Mitrousias V, Varitimidis S, Raoulis V, Fyllos A, Arvanitis D. Musculoskeletal anatomy: evaluation and comparison of common teaching and learning modalities. Scientific Reports. 2021;11(1):1517. https://doi.org/10.1038/s41598-020-80860-7 . Zilverschoon M, Custers EJ, Cate T, Kruitwagen O, C. L. J. J., Bleys RLAW. Support for using a three-dimensional anatomy application over anatomical atlases in a randomized comparison. Anat Sci Educ. 2022;15(1):178–86. https://doi.org/10.1002/ase.2110 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 19 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 12 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8584889","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578734308,"identity":"d89e7eb2-bff8-4f04-b09f-bac9350c9c3e","order_by":0,"name":"Marjan 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design\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8584889/v1/d1fef3f444ef59536d015572.png"},{"id":101398031,"identity":"a56905e7-1063-45ea-9c49-55b1309e1578","added_by":"auto","created_at":"2026-01-29 09:39:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42399,"visible":true,"origin":"","legend":"\u003cp\u003eExample of test item of Mental Rotation Test (Peters et al., 1995)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8584889/v1/1d36965d23b8313f284b62aa.png"},{"id":101362618,"identity":"0b28cb12-eae9-43eb-861d-12e06b4f1ad4","added_by":"auto","created_at":"2026-01-29 00:30:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56637,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the original and (recoded) ordered categorical variables ‘Remembering’, ‘Understanding’ and ‘Applying’\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8584889/v1/b3eea8eda967dbe2a0957ca7.png"},{"id":101362617,"identity":"54a3bf84-918b-4a46-8c0e-e3b9a07c1171","added_by":"auto","created_at":"2026-01-29 00:30:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61674,"visible":true,"origin":"","legend":"\u003cp\u003eAverage predicted probability to belong to each category of ‘Remembering’, ‘Understanding’, and ‘Applying’ for the 2D and the 3D condition.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8584889/v1/4244a1e6260565f1e66fcb1e.png"},{"id":101399043,"identity":"6949988a-9091-4d08-9d52-c0627c9d4909","added_by":"auto","created_at":"2026-01-29 09:51:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1064674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8584889/v1/58f7f01b-2ec9-407f-84b3-a213c43384e5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the effectiveness of 2D images and 3D models to achieve cognitive levels of Bloom in first-year anatomy students","fulltext":[{"header":"Background","content":"\u003cp\u003eEducators and students worldwide increasingly use two-dimensional (2D) and three-dimensional (3D) digital tools to visualize anatomy (Pettersson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies have shown that traditional human dissections, considered the gold standard in anatomy education, can be effectively enhanced with technology such as computer-based 2D images (2DI) and computer-based 3D models (3DM) (Casallas \u0026amp; Quijano, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Codd \u0026amp; Choudhury, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zibis et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Wickramasinghe et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) stress the need for these tools to align with contemporary learning environments. However, the importance of a well-defined pedagogical framework to guide the effective integration of these technologies is highlighted (Wickramasinghe et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alexander et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, the discussion has shifted from whether to digitize anatomical education to how to integrate it into the anatomy curriculum (Azer\u0026amp; Azer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Clunie et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xiao \u0026amp; Evans, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComputer-based 3DM are digital representations of objects created using computer software, allowing for visualization, manipulation, and analysis in three dimensions by implementing them in digital learning environments. These models are widely used in various fields, including education and health care (Adnan \u0026amp; Xiao, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although computer-based 3DM are displayed on a 2D screen and viewed as monoscopic projections, they can still provide subtle depth cues. As a result, they are preferred over 2DI for understanding size differences and spatial relationships (Yohannan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Computer-based 3DM are more accessible than virtual, augmented, and mixed reality, as they require no expensive software or specialized glasses and are easy to use in various settings.\u003c/p\u003e \u003cp\u003eMultiple studies claim that the use of computer-based 3DM is more effective to learn human anatomy than the use of 2DI (Chauhan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Casallas \u0026amp; Quijano, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Eroğlu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Haque et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Triepels et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yammine \u0026amp; Violate, 2015; Yohannan et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Yohannan et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that 3DM (in both stereoscopy and monoscopy) were more effective for teaching radiological anatomy compared to the control group using 2DI. Yammine and Violato (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Triepels et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that students exhibited a preference for 3D tools over traditional instructional methods. Moreover, their findings indicate that the integration of 3DM in anatomy education contributes to enhanced learning outcomes. Eroğlu et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) stated that this effect was more pronounced for complex anatomical structures, such as the male genitalia, than for simpler structures, such as the liver. However, Berney et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found no differences in performance after learning the functional anatomy of the scapula from either 3DM or 2DI. The authors concluded that the building of effective mental representations of the scapula and its movements was possible for the learners, regardless of the representation format. These results are supported by other studies such as Khot et al (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Preece et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Vandenbossche et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The latter study suggested that the learning material, the osteology of the pelvis, may have lacked sufficient complexity to demonstrate potential advantages, a point similarly noted by Eroğlu et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA major limitation of these studies is their failure to determine the optimal integration of computer-based 2DI and 3DM in anatomy curricula, particularly concerning the cognitive levels required by specific learning objectives. The effectiveness of anatomy education is commonly evaluated using student satisfaction surveys and general knowledge assessments (Clunie et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, these assessments frequently fail to align with the intended cognitive levels of the learning objectives, thereby inadequately evaluating the depth of understanding and skill development fostered by the learning task. Constructive alignment emphasizes that learning tasks must align with learning objectives and its cognitive levels, prioritizing the educational framework over the isolated effectiveness of any learning method (Biggs, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). 2DI and 3DM each have their specific characteristics and should be utilized based on the knowledge they develop and the learning objectives that need to be achieved (Azer \u0026amp; Azer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBloom\u0026rsquo;s Taxonomy (Bloom, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Anderson \u0026amp; Krathwohl, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) is employed in anatomy education to align assessments with learning objectives (Choudhury \u0026amp; Freemont, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and teaching methodologies (Thompson \u0026amp; Lake, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Blooming Anatomy Tool (BAT) was developed to specify Bloom\u0026rsquo;s Taxonomy for the design and evaluation of assessments in the anatomical sciences (Thompson \u0026amp; O\u0026rsquo;Loughlin, 2015). In the BAT, knowledge (level 1) and comprehension (level 2) were categorized as lower order levels, and application (level 3) and analysis (level 4) as higher order levels of cognition. The first level represents the foundational stage of learning. At this level, learners focus on identifying, recalling, or memorizing facts, definitions, and basic information, such as recognizing structures on a model. Level 2 represents the ability to comprehend concepts, spatial organization, or pathways by describing, explaining or summarizing the learned material. To reach the third level, students should be able to apply the new information to predict or infer the interaction between body systems. For example, describing brain irrigation corresponds to Bloom's Level 2, while predicting the impact of blood flow disruption reaches Level 3. The fourth level in the BAT contains the competence to analyze a situation regarding the taught subjects, such as interpreting brain images after a stroke. A comprehensive overview is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCognitive levels in anatomy education based on Bloom (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1956\u003c/span\u003e), Anderson \u0026amp; Krathwohl (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and Thompson \u0026amp; O\u0026rsquo;Loughlin (2015)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLower order levels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eHigher order levels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1. Knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2. Comprehension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3. Application\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4. Analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive verb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderstand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eApply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnalyze\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkills\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentify, repeat, recall, memorize, list, recognize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescribe, distinguish, explain, summarize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInfer, predict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJudge, critique, interpret\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTypes of anatomical information\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinitions, facts, straightforward recall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConcepts, spatial organization, pathways, blood supply, innervation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteraction between body systems, functional aspects beyond memorization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApplying information to a new situation, interpretation of anatomical images, clinical judgement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTechnology-enhanced learning facilitates students' capacity to achieve higher levels of cognition by fostering active learning (Elzainy \u0026amp; Sadik, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Learning environments that incorporate manipulable 3DM are particularly effective in promoting active learning due to their interactive nature, which fosters engagement and enhances spatial comprehension by providing immediate spatial insights (Cabrera-Duffaut et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As the student rotates the 3DM in 360 degrees, the student is handling the object itself while learning. The embodied cognition theory suggests that physical interaction with objects can foster meaningful learning (Fiorella, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research indicates that early manipulation of 3DM activates the motor cortex during subsequent mental rotation tasks involving the same objects (Kosslyn et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This activation does not occur when objects are not manipulated beforehand. These findings suggest that 3DM stimulate increased brain activity, thereby supporting deeper cognitive engagement and learning. Moreover, 3DM can provide a clear and explicit external representation of anatomical structures for learners with limited spatial skills (Hays, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Jang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings suggest that using computer-based 3DM for learning anatomy facilitate achieving higher cognitive levels, such as application (level 3). However, empirical evidence underpinning this claim is currently lacking. Therefore, this study will explore the effectiveness of computer-based 2DI and computer-based 3DM in anatomy education in attaining lower and higher cognitive levels as defined by Bloom's taxonomy. The research aims to identify the cognitive levels where 2DI are most effectively employed, as well as those where 3DM provide superior benefits.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eTo examine the effectiveness of 2DI and 3DM in attaining specific learning outcomes, an experiment was set-up. This section details the methodology, including participant information, research context, study design, and instruments. The operationalization of key concepts and the analysis approach are also discussed. A walk-through is available online, which explains the preparation, cleaning, analysis, and reporting of the data. The R code, along with all the study materials and raw datasets, is available via the Open Science Framework.\u003c/p\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eParticipants and context\u003c/h2\u003e\n \u003cp\u003eFifty-eight first-year students of the bachelor program \u0026lsquo;Revalidation Sciences and Physiotherapy\u0026rsquo; were recruited at the University of Antwerp, Belgium. All students participated on a voluntary basis. The volunteers consisted of 13 males and 16 females in the 2D group, and 15 males and 14 females in the 3D group, with a mean age of approximately 20\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09 years old (all means will be reported with standard deviation). At this stage of their studies, students had completed a course in anatomical terminology and basic anatomy of the musculoskeletal system containing osteology, arthrology and myology. The osteology of the skull was not seen in depth and described superficial structures only. This allowed us to assume that the students had notions of the temporal bone without insight or orientation skills, which gave us the opportunity to use this bone in our study. Based on the study of Vandenbossche et al. (\u003cspan\u003e2023\u003c/span\u003e) and Eroğlu et al. (\u003cspan\u003e2023\u003c/span\u003e), the temporal bone was considered as complex enough to show differences in 2D versus 3D learning. Students were randomly assigned to the 2D and 3D group. Most of the students (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;44) participated in a computer class at the campus, with each student utilizing an individual workstation. Fourteen students participated in the Antwerp Social Lab, where their eye movements, galvanic skin responses, and screen activity were recorded for related research that is not included in this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eThe study had an experimental design that consisted of three phases: (1) a pre-test to capture sex, age, spatial ability and prior anatomical knowledge, (2) a learning phase containing a pretraining and an independent learning phase during which students learned about the temporal bone with either computer-based 2DI or 3DM, and (3) a post-test to measure students\u0026rsquo; post anatomical knowledge of the temporal bone (see Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Phase 2 encompasses the intervention, as detailed below.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn the learning phase, students first watched a knowledge clip focusing on the temporal bone. This video, presented in the native language, provided an overview of the orientation and structure of the temporal bone as a form of pre-training (see Mayer \u0026amp; Fiorella, \u003cspan\u003e2021\u003c/span\u003e). Also, attention was drawn to the passages of nerves and blood vessels through and along the temporal bone and to some major muscle attachments. For this, drawings were used from Standard Anatomy, a free anatomical platform (\u003cspan\u003e\u003cspan\u003ehttps://www.anatomystandard.com/\u003c/span\u003e\u003c/span\u003e). Students were not allowed to pause the video. The full knowledge clip can be consulted at the Open Science Framework. Next, students were presented a short video instruction on the learning goals they should accomplish and practical instructions about the use of the anatomical models in the digital learning environment. After these instructions, students used the environment to explore the complexity of the temporal bone at their own pace for maximal 25 minutes.\u003c/p\u003e\n\u003cp\u003eThe digital learning environment was made using the Genially\u0026copy; application (\u003cspan\u003e\u003cspan\u003ewww.genially.com\u003c/span\u003e\u003c/span\u003e) and surface scanned bones uploaded in Sketchfab\u0026copy; (\u003cspan\u003e\u003cspan\u003ewww.sketchfab.com/marjanmaldoy\u003c/span\u003e\u003c/span\u003e). Informed consent was obtained for these bones from the body donor program of the University of Antwerp. The digital learning environment contained either 2DI (see environment in native language here) or 3DM (see environment in native language here) of the temporal bone, depending on the condition. Students were able to rotate the 3DM in 360 degrees. Annotations of the different parts of the temporal bone could be selected by the student. The 2DI consisted of annotated screenshots taken from the 3DM. Students were allowed to freely navigate between the different planes. Although the structures shown on the bone were described orientatively, the orientation of the 2DI and 3DM was not separately clarified. This way, both learning environments were aligned to ensure comparable research environments as suggested by Cook (\u003cspan\u003e2005\u003c/span\u003e). Students were not allowed to make notes during the learning phase.\u003c/p\u003e\n\u003ch3\u003eInstruments and variables\u003c/h3\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eSpatial ability\u003c/h2\u003e\n \u003cp\u003eSpatial ability was measured paper-based using the 24-item Mental Rotation Test (MRT) described by Peters et al. (\u003cspan\u003e1995\u003c/span\u003e). The MRT captures spatial cognition and is used widely in anatomy education studies (e.g. Vandenbossche et al., \u003cspan\u003e2023\u003c/span\u003e; Berney et al., \u003cspan\u003e2015\u003c/span\u003e; Jang et al., \u003cspan\u003e2017\u003c/span\u003e). Each item presents a configuration of blocks (see Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e) and asks students to indicate which alternatives represent a rotated version of that same configuration (two correct options). Two example items were given, and three items were used to practice. Upon comprehensive understanding of the task, the test commenced with 12 out of the 24 items to be completed within three minutes. After a break of two minutes, the remaining 12 items were questioned in three minutes. Each item was scored as correct if two and only two correct variations were marked.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAs the MRT has already been validated in previous research (Peters et al., \u003cspan\u003e1995\u003c/span\u003e), only the internal consistency of the MRT was checked. To be sufficiently reliable, Cronbach\u0026rsquo;s alpha should be at least 0.7 (Tavakol \u0026amp; Dennick, \u003cspan\u003e2011\u003c/span\u003e). The internal consistency of the MRT was satisfactory (\u003cem\u003e𝛼\u003c/em\u003e = 0.83) and was not improved by omitting an item. Consequently, scores across all MRT items were summed per student to create the variable \u0026lsquo;Spatial ability\u0026rsquo; (12.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61). This variable was standardized before analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003ePrior anatomical knowledge\u003c/h3\u003e\n\u003cp\u003eThe prior knowledge test consisted of eight questions: three aimed at capturing students\u0026rsquo; prior knowledge about the temporal bone and five assessed students\u0026apos; prior understanding of general anatomy. The general knowledge test included terminology (e.g., identifying the Latin name for a pointed bony protuberance). It was assumed that students possessed a solid foundational knowledge of general anatomy, deemed essential for comprehending the learning material. Since the items evaluated distinct aspects of prior anatomical knowledge, internal consistency was not calculated. A dummy variable, \u0026lsquo;General Prior Knowledge\u0026rsquo;, was created to classify students based on their performance: those scoring at least 4 items correct were coded as 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21, 36.2%) and those below this threshold coded as 0 (\u0026lt;\u0026thinsp;4 correct; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37, 63.8%).\u003c/p\u003e\n\u003cp\u003ePrior knowledge of the temporal bone anatomy was assessed using three items (e.g., identifying the parts of the temporal bone). Students were expected to have minimal knowledge of this bone, with scores of at most one correct response out of three anticipated. A dummy variable, \u0026lsquo;Prior Knowledge about the Temporal Bone\u0026rsquo;, was created based on these three items. Students who failed all items were coded as 0 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50, 86.2%), while those answering at least one item correctly were coded as 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8, 13.8%).\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003ePost knowledge about the temporal bone at the level of remembering, understanding and applying\u003c/h2\u003e\n \u003cp\u003eStudents\u0026rsquo; anatomical knowledge of the temporal bone after intervention was measured using a paper-based test. In total, twenty-nine questions tapped into what students remembered about the temporal bone (level 1 of Bloom), their understanding of the temporal bone (level 2 of Bloom), and to what extent they were able to apply their knowledge about the temporal bone (level 3 of Bloom). Classification of the questions was checked by three experts in anatomy education using the Blooming Anatomy Tool (Thompson \u0026amp; O\u0026rsquo;Loughlin, 2015). To assess the inter-rater reliability, Fleiss kappa was calculated using the R-package irr (Gamer et al., \u003cspan\u003e2019\u003c/span\u003e; version 0.84.1). Inter-rater reliability across all levels was excellent (\u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78). Raters strongly agreed on the classification of items into level 1 (\u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95) and level 2 (\u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74) of Bloom\u0026rsquo;s taxonomy. However, there was only moderate agreement regarding classification of items into level 3 (\u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.44). One rater classified items 5a, 5b, 5c and 7 as belonging to level 2 (understanding), while the two other raters classified these items as level 3 (application). Following a discussion, the four items were classified as belonging to level 3. All items, the analysis on inter-rater reliability, and the final classification of the items are found in the walk-through.\u003c/p\u003e\n \u003cp\u003eStudents\u0026rsquo; knowledge about the temporal bone at the level of remembering, understanding and applying was measured using respectively 15, 10 and 4 items. The quality of the items was examined separately per level of Bloom. First, items answered correctly by (almost) all students were deleted as these items were not able to discriminate between students. Then, classical test theory was applied whereby items showing item-total correlations lower than 0.20 were excluded from further analysis (De Champlain, \u003cspan\u003e2010\u003c/span\u003e). This analysis was repeated until all items showed an acceptable correlation with the total score. Finally, the internal consistency of the remaining items was checked. The remaining items were summed per level of Bloom to create the variables \u0026lsquo;Remembering\u0026rsquo; (6 items, 𝛼 = 0.69), \u0026lsquo;Understanding\u0026rsquo; (5 items, 𝛼 = 0.65) and \u0026lsquo;Applying\u0026rsquo; (3 items, 𝛼 = 0.68). All analyses and intermediate results can be consulted in the walk-through.\u003c/p\u003e\n \u003cp\u003eAs the variables are not normally distributed (see Fig.\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e), the variables \u0026lsquo;Remembering\u0026rsquo;, \u0026lsquo;Understanding\u0026rsquo;, and \u0026lsquo;Applying\u0026rsquo; were recoded into ordered categorical variables. The variable \u0026lsquo;Remembering\u0026rsquo; is composed of four ordered categories. These categories represent scores between 0 and 2 (category D), a score of 3 (category C), a score of 4 (category B) and scores of 5 or 6 (category A). The variable \u0026lsquo;Understanding\u0026rsquo; contains five ordered categories, reflecting scores of 0 (category E), 1 (category D), 2 (category C), 3 (category B) and 4 or 5 (category A). The variable \u0026lsquo;Applying\u0026rsquo; was not recoded, but was treated as an ordered ordinal variable whereby each score represents another category.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003eData analysis\u003c/h2\u003e\n \u003cp\u003eTo examine the impact of condition on students\u0026rsquo; remembering, understanding and applying of knowledge about the temporal bone, cumulative link models were fitted in R using the ordinal package (Christensen, \u003cspan\u003e2023\u003c/span\u003e; version 2023.12\u0026ndash;4.1). This type of model accounts for the ordinal nature of the dependent variables by estimating intercepts (thresholds) that express the probability of belonging to a specific category or lower. Hence, the number of thresholds to be estimated is one less than the number of categories of the dependent variable. For instance, the variable \u0026lsquo;Remembering\u0026rsquo; consisted of four categories. Consequently, three thresholds were estimated that express respectively the probability of belonging to category D (threshold D|C), category C or lower (threshold C|B), and category B or lower (threshold B|A). These thresholds can be used to construct the probability of belonging to each category of the variable \u0026lsquo;Remembering\u0026rsquo;.\u003c/p\u003e\n \u003cp\u003eTwo cumulative link models were fitted per dependent variable. The two models differed in assuming that the impact of condition was the same across all thresholds (proportional odds models) or differed across thresholds (partial proportional odds model). In the latter case, the impact of condition on the probability of belonging to each category of the dependent variable varies across categories. Both models comprised five predictors: \u0026lsquo;Condition\u0026rsquo;, \u0026lsquo;Location\u0026rsquo;, \u0026lsquo;Spatial ability\u0026rsquo; and the two variables that capture prior knowledge (\u0026lsquo;General Prior Knowledge\u0026rsquo; and \u0026lsquo;Prior Knowledge about the Temporal Bone\u0026rsquo;). Spatial ability and prior knowledge were controlled for as literature suggests that these impact anatomy learning (e.g., Azer \u0026amp; Azer, \u003cspan\u003e2016\u003c/span\u003e; Berney et al., \u003cspan\u003e2015\u003c/span\u003e) and should be taken into account when estimating the impact of condition. As the procedures for data collection differed between students who participated individually in the lab or those who joined a group session at the campus, this was accounted for by adding the dummy variable \u0026lsquo;Location\u0026rsquo; as a predictor. Both models were compared, and the research question was answered based on all the estimates of the best model.\u003c/p\u003e\n \u003cp\u003eIn order to estimate the average effect of condition on the dependent variables, it was necessary to take into account the non-linearity of the estimates. This was achieved by calculating the average probability of belonging to each category of the dependent variable for both conditions (Agresti \u0026amp; Tarantola, \u003cspan\u003e2018\u003c/span\u003e; Long et al., 2021). As students in both conditions differed in terms of their \u0026lsquo;Spatial ability\u0026rsquo;, \u0026lsquo;General Prior Knowledge\u0026rsquo;, and \u0026lsquo;Prior Knowledge about the Temporal Bone\u0026rsquo; (for further details, please refer to the walk-through), the average probabilities were predicted based on a balanced data grid. This grid contained all possible combinations of condition and the three dummy predictors, with \u0026lsquo;Spatial ability\u0026rsquo; fixated at three specific values (the mean, -1\u003cem\u003eSD\u003c/em\u003e, and +\u0026thinsp;1\u003cem\u003eSD\u003c/em\u003e) using the marginal effects package in R (Arel-Bundock et al., \u003cspan\u003e2024\u003c/span\u003e; version 0.24.0). Subsequently, the estimated probabilities were aggregated per condition (and category) and 95% confidence intervals were estimated. The walk-through presents a detailed account of all the steps taken to estimate the average effect of condition. To draw inferences about the population, two pieces of information were considered: the effect size, expressed as an odds ratio per category of the dependent variable, and the 95% confidence interval of these odds ratios. Odds ratios of 1.5 (or smaller than 0.67), 2.5 (or smaller than 0.41) and 4 (or smaller than 0.26) were interpreted as indicating a small, medium and large effect size respectively (Maher et al., \u003cspan\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe results section describes the average effect of condition. The results are expressed in probabilities and odds ratios to facilitate interpretation for the reader. The appendix presents all model estimates, including those regarding the impact of spatial ability, prior knowledge and the location in which students participated in the study. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the average impact of condition on remembering, understanding and applying knowledge about the temporal bone.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImpact of condition on remembering knowledge about the temporal bone\u003c/h2\u003e \u003cp\u003eOn average, students in the 3D condition are more likely to be classified in a higher category of \u0026lsquo;Remembering\u0026rsquo; than students in the 2D condition (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, the probability of belonging to category A is, on average, predicted to be 31.9% for students in the 2D condition and 70% for students in the 3D condition. The odds of belonging to category A of \u0026lsquo;Remembering\u0026rsquo; are, on average, 5.07 times higher for a student in the 3D condition than for a student in the 2D condition. The magnitude of the odds ratio indicates a large effect of condition on remembering of knowledge about the temporal bone. As the 95% confidence interval of the odds ratio for category A excludes 1 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), it is reasonable to assume that the impact of condition on \u0026lsquo;Remembering\u0026rsquo; will also be observed in the general population. A similar conclusion may be drawn based on the odds ratios and 95% confidence intervals of the other categories (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, it is concluded that learning with 3DM results in a higher probability of remembering knowledge about the temporal bone than learning using 2DI.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage probability of belonging to each category of \u0026lsquo;Remembering\u0026rsquo; for students in the 2D and 3D condition and odds ratio per category. 95% confidence intervals are presented between brackets.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAverage probability per condition [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOdds ratio [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3D\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemembering (level 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 [0.12\u0026ndash;0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 [0.54\u0026ndash;0.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.07 [5.83\u0026ndash;8.99]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39 [0.25\u0026ndash;0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23 [0.10\u0026ndash;0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46 [0.36\u0026ndash;0.47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17 [0.05\u0026ndash;0.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04 [0.00-0.09]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23 [0.04\u0026ndash;0.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12 [0.03\u0026ndash;0.21]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02 [0.00-0.05]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16 [0.00-0.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderstanding (level 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 [0.18\u0026ndash;0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31 [0.09\u0026ndash;0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72 [0.46\u0026ndash;0.79]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23 [0.11\u0026ndash;0.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22 [0.11\u0026ndash;0.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97 [0.94\u0026ndash;1.04]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22 [0.10\u0026ndash;0.33]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24 [0.11\u0026ndash;0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.13 [1.10\u0026ndash;1.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09 [0.02\u0026ndash;0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11 [0.03\u0026ndash;0.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28 [1.29\u0026ndash;1.30]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09 [0.01\u0026ndash;0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12 [0.03\u0026ndash;0.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.45 [1.46\u0026ndash;1.77]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApplying (level 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24 [0.06\u0026ndash;0.42]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42 [0.19\u0026ndash;0.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.25 [2.54\u0026ndash;3.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21 [0.05\u0026ndash;0.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40 [0.20\u0026ndash;0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.47 [2.43\u0026ndash;4.95]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38 [0.20\u0026ndash;0.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05 [0.00-0.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.08 [0.00-0.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17 [0.05\u0026ndash;0.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14 [0.03\u0026ndash;0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78 [0.69\u0026ndash;0.77]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImpact of condition on understanding knowledge about the temporal bone\u003c/h2\u003e \u003cp\u003eStudents in the 3D condition are, on average, less likely to be classified in a higher category of \u0026lsquo;Understanding\u0026rsquo; than students in the 2D condition (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For example, the average predicted probability of belonging to category A is 38% for students who learn in 2D and 31% for students who learn with 3DM. The odds of a student in the 3D condition to be classified in category A are, on average, 0.72 times higher than for a student in the 2D condition. Thus, students in the 2D condition are more likely to belong to category A than their peers who studied in 3D. Although the 95% confidence interval of the odds ratio excludes 1 (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), the magnitude of the odds ratio of 0.72 signifies a lack of effect. A similar outcome is observed for the other categories of \u0026lsquo;Understanding\u0026rsquo; (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It is concluded that studying with 2DI or 3DM leads to a similar understanding of knowledge about the temporal bone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImpact of condition on applying knowledge about the temporal bone\u003c/h2\u003e \u003cp\u003eStudents in the 3D condition are, on average, more likely to belong to a higher category of \u0026lsquo;Applying\u0026rsquo; than students in the 2D condition (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The average predicted probability of belonging to category A of \u0026lsquo;Applying\u0026rsquo; is respectively 24% and 42% for students in the 2D and 3D condition. The odds of being classified in category A are, on average, 2.25 times higher for students who learned with 3DM. The magnitude of the odds ratio points to a small effect of condition on applying knowledge about the temporal bone. As the 95% confidence interval around the odds ratio also excludes 1 (95% CI: 2.54\u0026ndash;3.40), it is reasonable to assume that the impact of condition on \u0026lsquo;Applying\u0026rsquo; will also be observed in the general population. The same result is found regarding categories B and C of \u0026lsquo;Applying\u0026rsquo; for which respectively a medium effect of condition is found (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Only with regard to the lowest category of \u0026lsquo;Applying\u0026rsquo; (category D), the magnitude of the odds ratio points to a lack of effect (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hence, there are indications that studying with 3DM leads to better application of knowledge about the temporal bone, but there is uncertainty regarding this finding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrevious research has examined the overall effectiveness of 2DI and 3DM in facilitating general knowledge acquisition in the context of anatomy education. However, it often overlooked the alignment with specific cognitive outcomes as defined by Bloom's taxonomy (Bloom, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Anderson \u0026amp; Krathwohl, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Thompson \u0026amp; O\u0026rsquo;Loughlin, 2015). This may contribute to the inconsistent findings regarding the relative effectiveness of 2DI and 3DM. To address this research gap, the present study employed an experimental design involving 58 first-year bachelor students \u0026lsquo;Revalidation Sciences and Physiotherapy\u0026rsquo;. Following a learning phase using either computer-based 2DI or 3DM, students\u0026rsquo; knowledge of the temporal bone was assessed. The aim of this study is to determine the cognitive levels for which 2DI are most effective and those for which 3DM offer superior benefits.\u003c/p\u003e \u003cp\u003eThe findings of this study indicate that 3DM are particularly effective for first-year students in enhancing cognitive outcomes at the level of \"Remembering\" (Level 1) of Bloom's taxonomy. 3DM provide immediate insight into the overall form, orientation, and key characteristics of anatomical structures, thereby facilitating efficient recall. This aligns with findings by Yammine \u0026amp; Violate (2015), Zibis et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Park et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who highlighted that 3DM improve the identification of structures. Similarly, Anderson et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) observed greater object recognition, as measured by electroencephalography, in students learning from 3DM compared to those utilizing 2DI. Zilverschoon et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that junior medical students scored significantly higher and were significantly faster when using a 3D study tool compared to students using a 2D atlas in an open book examination. These results were explained by the possible influence of the increased mental steps it takes to convert a 2DI into a 3D mental representation. Moreover, Yohannan et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) assert that the effectiveness of visualizations for basic recall is significantly influenced by the presence and quality of depth cues. This suggests that depth perception contributes to improved recall, the first level of Bloom. Nevertheless, the mechanisms by which students learn from 3DM remain poorly understood (Azer \u0026amp; Azer, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and the underlying learning processes have yet to be thoroughly examined.\u003c/p\u003e \u003cp\u003eThe results for the other two cognitive levels are less conclusive. For \u0026ldquo;Understanding\u0026rdquo; (level 2), 2DI are as effective as 3DM. At the level of \"Applying\" (level 3), 3DM show greater effectiveness, although these results are fraught with uncertainty. Although it was expected that 3DM would enhance the attainment of higher cognitive levels, the results of this study do not provide clear empirical support for this claim. Park et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) suggest that 3DM may be less effective in promoting deeper anatomical understanding, as demonstrated in open- and closed-book examinations using 2D or 3D atlases. Based on neurological data on stereopsis, Anderson et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) conclude that a combination of 2DI and 3DM may improve learning, retention, and transfer based on neurological data. However, empirical evidence on which combination works for whom is lacking.\u003c/p\u003e \u003cp\u003eThe mixed effectiveness of 3DM may be due to their complexity, which can cause cognitive overload, especially for students with limited spatial skills (Labranche et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Contrarily, Berney et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) posits that students with lower spatial ability may benefit more from these 3D visualizations, whereas those with higher spatial ability may experience a disadvantage when using 3DM. Given this ambiguity, variations in both spatial and cognitive abilities may account for the inconsistent results observed in the average student population. The role of these factors in learning was not the focus of this study, although the study did control for spatial ability. Future research should investigate how spatial ability affects 2D and 3D learning.\u003c/p\u003e \u003cp\u003eStudies assessing the effectiveness of 3DM predominantly assessed knowledge at the first level of Bloom (e.g. Vandenbossche et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zibis et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or provide insufficient detail about the specific content assessed in post-knowledge tests (e.g. Haque et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Eroğlu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Casallas \u0026amp; Quijano, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Park et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Crowther et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) noted that anatomy education often overlooks higher cognitive levels, resulting in assessments that inadequately address advanced domains. This might be explained by the challenging task of developing effective higher-order questions, which often require alternatives to traditional multiple-choice formats. This highlights the need for greater transparency in assessment design and sharing of the knowledge tests that are used for research purposes.\u003c/p\u003e \u003cp\u003eEroglu et al. (2023) emphasize that 3DM are particularly effective for novice learners; however, the specific cognitive levels examined in their study remain ambiguous. Most research on the effectiveness of 3DM focuses on first-year students, as their limited prior knowledge provides a controlled basis for assessment. In this study as well, first-year students were selected due to the same rationale. However, caution is warranted when generalizing these findings to broader populations. While 3DM appears to be less effective at higher cognitive levels in first-year students, its impact may differ for more experienced learners, as their prior knowledge could facilitate deeper cognitive processing.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. Firstly, the study population was relatively small and the number of items per Bloom level was limited. This may constrain the generalizability of the results. Secondly, to enable group comparisons, participants were required to follow a standardized learning approach that may not reflect their typical study methods. They were given 25 minutes to complete the course without taking notes, and both groups were restricted to screen-based materials, potentially disadvantaging those who prefer paper-based resources such as atlases or textbooks. Thirdly, this post-test was administered in a paper-based (2D) format, which might have favored the 2D group.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study indicate that 3DM are effective for facilitating recognition and recall at the level of \"Remembering\" (Level 1) in the Taxonomy of Bloom, while their advantages at higher cognitive levels are less clear. Possible reasons for this are searched in multiple factors such as cognitive load and spatial abilities. However, these findings may be different in experts, so vigilance is required, and further research is needed. Future research should prioritize transparent qualitative assessments, include questions across all cognitive levels, and examine the learning processes underlying these methods in both novice and experienced learners.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods were carried out in accordance with national and European guidelines and regulations.\u0026nbsp;\u003cspan\u003eThe study was approved by the Ethics Advisory Committee for Social and Human Sciences (EASHW) of the University of Antwerp (SHW_2022_136_1)\u003c/span\u003e. Written informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding declaration\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave written informed consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003eA walk-through is available\u0026nbsp;\u003c/span\u003e\u003cspan\u003eonline\u003c/span\u003e\u003cspan\u003e, which explains the preparation, cleaning, analysis, and reporting of the data. The R code, along with all the study materials and raw datasets, is available via the Open Science Framework.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.M. conceived and designed the study under the supervision of T.V.D. and L.U., with intellectual input from V.V. M.M., L.U. and I.G. developed the study materials and learning resources. T.V.D. performed the statistical analyses, interpreted the data, and drafted the results section. M.M. drafted the main manuscript text. L.V.N. and V.V. critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdnan S, Xiao J. A scoping review on the trends of digital anatomy education. Clin Anat. 2023;36(3):471\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ca.23995\u003c/span\u003e\u003cspan address=\"10.1002/ca.23995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgresti A, Tarantola C. Simple ways to interpret effects in modeling ordinal categorical data. 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Support for using a three-dimensional anatomy application over anatomical atlases in a randomized comparison. Anat Sci Educ. 2022;15(1):178\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ase.2110\u003c/span\u003e\u003cspan address=\"10.1002/ase.2110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"anatomy education, Taxonomy of Bloom, 3D models, 2D images, cognitive levels","lastPublishedDoi":"10.21203/rs.3.rs-8584889/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8584889/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThere is a critical need for a defined pedagogical framework for using computer-based 2D images (2DI) and 3D models (3DM) in anatomy education. While 3DM are considered to be more or evenly effective as 2DI, the specific knowledge types they support remain unclear. The interactive nature of 3DM promotes engagement and spatial understanding, potentially aiding higher cognitive levels. This study aims to identify which cognitive levels of Bloom\u0026rsquo;s taxonomy benefit most from 2DI and where 3DM provide greater advantages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e58 first-year physiotherapy students participated in a study with three phases: (1) a pre-test to capture sex, age, spatial ability, and prior knowledge, (2) a learning phase with pretraining and independent study using either 2DI or 3DM of the temporal bone, and (3) a post-test to measure knowledge gained at level 1 (Remembering), level 2 (Understanding) and level 3 (Applying).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eStudents in the 3D condition are 5.07 times more likely to excel at \"Remembering\" (Level 1). Results for other cognitive levels are less clear: 2DI are as effective as 3DM for \"Understanding\" (Level 2), while 3DM may lead to better outcomes for \"Applying\" (Level 3), though results are uncertain due to mixed effects per score.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe findings suggest that 3DM enhance recognition and recall at Level 1 (the \"Remembering\" level) of Bloom\u0026rsquo;s Taxonomy, with mixed effectiveness at higher cognitive levels. This may be due to factors such as cognitive load and spatial ability. 3DM can be used effectively in anatomy education to enhance recognition and recall.\u003c/p\u003e","manuscriptTitle":"Exploring the effectiveness of 2D images and 3D models to achieve cognitive levels of Bloom in first-year anatomy students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:30:31","doi":"10.21203/rs.3.rs-8584889/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"29553894477465728748381394355370654606","date":"2026-04-08T13:34:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-05T14:21:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297845420108022036744738495888004981195","date":"2026-03-31T13:42:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T11:55:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T06:46:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T12:55:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T12:54:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Education","date":"2026-01-12T19:16:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"meed","sideBox":"Learn more about [BMC Medical Education](http://bmcmededuc.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/meed/default.aspx","title":"BMC Medical Education","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a1adaa0-e648-497b-aba7-e799a6a694d7","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T00:30:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:30:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8584889","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8584889","identity":"rs-8584889","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}