The challenges of histology education

Ang, Eng-Tat; Liu, Pai; Jitesh, R; Chandrika, Muthukrishnan; Satish, Ramapatna L.; Lian, Wen Quan Derrick; Huppertz, Berthold; Khamuani, Munesh; Minarcik, John R

doi:10.1002/ca.23989

Cited by 6 publications

(1 citation statement)

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“…Thus, we demonstrated that the implementation and use of a virtual microscope are relevant for achieving specific educational objectives, and validating instructional designs based on the Biggs model (Biggs, 1996; Biggs & Tang, 2014). In a context wherein most education programs are decreasing the number of hours dedicated to teaching (Gribbin et al, 2022), teaching strategies must be useful, effective, and focused on learning outcomes (Kaliannan & Chandran, 2012; Eng‐Tat et al, 2023). The enlightened use of digital tools can help build an efficient teaching environment.…”

Section: Discussionmentioning

confidence: 99%

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy

Fries,

Pirotte,

Vanhee

et al. 2023

Anatomical Sciences Ed

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As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. Virtual microscopy provides fresh opportunities for investigating user behavior during the histology learning process, through digitized histological slides. This study establishes how students' perceptions and user behavior data can be processed and analyzed using machine learning algorithms. These also provide predictive data called learning analytics that enable predicting students' performance and behavior favorable for academic success. This information can be interpreted and used for validating instructional designs. Data on the perceptions, performances, and user behavior of 552 students enrolled in a histology course were collected from the virtual microscope, Cytomine®. These data were analyzed using an ensemble of machine learning algorithms, the extra‐tree regression method, and predictive statistics. The predictive algorithms identified the most pertinent histological slides and descriptive tags, alongside 10 types of student behavior conducive to academic success. We used these data to validate our instructional design, and align the educational purpose, learning outcomes, and evaluation methods of digitized histological slides on Cytomine®. This model also predicts students' examination scores, with an error margin of <0.5 out of 20 points. The results empirically demonstrate the value of a digital learning environment for both students and teachers of histology.

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Section: Discussionmentioning

confidence: 99%

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy

Fries,

Pirotte,

Vanhee

et al. 2023

Anatomical Sciences Ed

View full text Add to dashboard Cite

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A slide into obscurity? The current state of histology education in Australian and Aotearoa New Zealand medical curricula in 2022–2023

Meyer,

Chapman

2024

Anatomical Sciences Ed

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Australia and Aotearoa New Zealand (AANZ) medical schools have been impacted by curricular changes and the introduction of virtual microscopy (VM). No survey has explicitly described the outcome of these events on histology education in AANZ. This study provides a cross‐sectional overview of histology education in accredited medical schools across AANZ in 2022–2023. Responses were received from 83% (19/23) of Australian medical schools, and 50% (1/2) of medical schools in Aotearoa New Zealand. VM, either exclusively (42%) or combined with traditional microscopy (37%), emerged as the preferred mode for delivering histology education. Common instructional methods included face‐to‐face lectures (26%) and synchronous online live lectures (26%). A significant proportion (84%) of educators supplemented resources with virtual microscopy websites. Integration of histology education was prevalent (79%), primarily with pathology (32%) or gross anatomy (26%). On average, medical students in the region spent a maximum of 21 ± 17 h in face‐to‐face histology laboratories throughout their degree. Histology education was predominantly taught by academics with a PhD degree. This study also examined the similarities and differences in histology education between AANZ and the global landscape. Through this examination, the present study positions AANZ within the broader context of histology education worldwide discusses key factors impacting histology education, and advocates for action to mitigate a looming shortage of pathologists in AANZ. In light of these findings, AANZ medical schools should integrate histology and pathology, establish a core curriculum, and promote flexible teaching modalities.

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