150 - Validating Instructional Design and Predicting Student Performance in Histology Education: Using Machine Learning via Virtual Microscopy

Poster Board Number: 150
There are separate poster presentation times for odd and even posters.
Odd poster #s – first hour
Even poster #s – second hour

Co-authors:

Pierre Bonnet - Université de liège; Christophe Debruyne - Université de liège; Raphael Marée - Université de liège; Pascale Quatresooz - Université de liège; valerie defaweux - Université de liège

Abstract Body : 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.

This approach is a real opportunity to assist students in their learning process and to assess their development of higher-level thinking skills, like the diagnostic approach. This latter will be studied with second-year medical students with improved predictive algorithms processing perception and performance data as well as user data collected on Cytomine®.

In the futur, it is necessary to take advantage of the analysis of user data investigating specific complex skills in histology courses, to develop an automated feedback system that could stimulate the metacognitions of students in large medical education cohorts.