Studying Histology in 3D: Development and Evaluation of an Interactive Virtual Histology Learning Tool using a 3D Model of the Renal Corpuscle
Bibliographic record
Abstract
Histology education relies on two‐dimensional (2D) histological sections where students are required to tacitly translate their 2D knowledge into a three‐dimensional (3D) understanding of complex structural‐functional relationships at the microscopic level. While in the field of Gross Anatomy, virtual 3D models address this cognitive hurdle, it is not known whether virtual 3D tools are effective in Histology education. The objective of our study was to develop an interactive virtual 3D Histology learning tool, and determine whether interaction with the tool results in improved learning outcomes in third‐year medical sciences students at Western University, as well as evaluate the relationship between spatial ability and students' histology knowledge. We used a virtual 3D model of the renal corpuscle, previously generated (Amira 5.2) from serial semi‐thin histological sections, to develop a virtual interactive tool that allows superimposing the 2D sections within the context of the 3D model. The model, and raw data sections in three orthogonal planes, were incorporated into Unity software to generate the learning tool. The human‐computer interface was designed to allow for various levels of interactivity. Among other capabilities these include: free rotation, zooming, choices of visualization of highlighted parts, scrolling through serial sections with or without the integrated model, textual and auditory explanation of highlighted components. To evaluate the efficacy of the tool, and spatial ability, students (n=156) participated in a two‐session study. During the first session participants completed a pre‐test, and a spatial ability test. These scores were used to generate three balanced groups: a 3D interactive group (access to the 3D tool), a 2D interactive group (access to virtual 2D slides), and a control group (access to static images). During session two, students were given access to one of the three available learning tools, followed by a post‐test, and a questionnaire to determine performance, student attitudes and perceptions towards the different learning modalities. Preliminary results demonstrate a significant positive correlation between histology knowledge and spatial ability (r=0.32, P<0.01). Completion of session two will determine any difference between pre‐ and post‐test scores in the 3D interactive group as compared to the control groups, and reveal any correlation to spatial ability. Virtual 3D learning tools for Histology may be effective at improving test performance, and fostering student engagement. These tools could therefore alter the way in which we teach and learn the subject, opening the door for future computer‐based technology to enhance Histology education. Furthermore, spatial ability may be a determinant factor of students' success in Histology that should be considered in curriculum design. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".