The Importance of Guidance: Teaching Histology with an Interactive Virtual 3D Tool of the Renal Corpuscle
Bibliographic record
Abstract
Background Histology education classically relies on two‐dimensional (2D) sections where students are challenged to tacitly translate their 2D observations into a three‐dimensional (3D) understanding of complex structure‐functional relationships at the microscopic level. To overcome this cognitive hurdle, we previously generated a virtual 3D model of the renal corpuscle (RC) from serial histological sections and developed an interactive virtual 3D Histology learning tool (VHLT). The tool enables users to explore, listen, and read descriptions about a serially sectioned and digitally segmented RC. In addition, 2D serial ultramicrotome sections are superimposed over the 3D model and viewable in 3 orthogonal planes. This highly interactive interface allows users to freely rotate, zoom, and modify it by highlighting histological features, scroll through serial sections with or without the integrated model. Previously, the VHLT demonstrated improved student test results with low prior histology knowledge and those with low spatial ability. The current study explores how metacognitive guidance and learning preference affects student success. Specifically, we hypothesized students using the VHLT with guided instruction would perform better on post‐tests than unguided students using the VHLT and better than a control group using a traditional laboratory approach (2D Virtual Slide Box). All groups followed the same learning objectives. In addition, perceived student learning preference was considered in relation to the type of learning tool used. Participants (n62, 42F, 20M) in a large 3rd‐year introductory Histology course were categorized into learning preferences using the VARK model (visual, aural, read/write, kinesthetic, or multi‐modal) and completed a pre‐knowledge test of the histology of the RC. Using a VARK‐balanced allocation, learners were assigned to 2 experimental groups: guided instruction (GI), free exploration with the VHLT (UGI), and a control group (C). Average pre‐test histology scores across the 3 groups were 8.7±3.9, 7.4±2.8 and 7.8±2.7 for GI, UGI, and C groups respectively, with scores ranging from 3/30 to 15/30. During use of the assigned tools, investigator field notes and time on task were collected. The learning exercises took place in a laboratory environment where 3–10 students worked at computers. One week later, learners completed a post‐knowledge test that evaluated learning outcomes by comparing the change in test scores between pre‐ and post‐test. A questionnaire sampled learner preference with respect to their level of guidance and learning tool. Preliminary post‐knowledge test scores showed significant increases compared to pre‐test scores (14.8±3.7 for GI, and 13±5.4 for UGI, with scores ranging from 6/30 to 24/30). We predict that our data will reinforce the importance of metacognitive teaching approaches when using novel pedagogic tools. We also predict that guidance removes any potential differences that may appear in unguided uses where student learning preference may become a factor. This study will highlight how current and novel learning tools in histology education can be pedagogically potentiated through metacognitive adjustments in the organization of the undergraduate curriculum. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".