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Validating instructional design and predicting student performance in histology education: Using machine learning via
Allyson Fries1, Marie Pirotte1, Laurent Vanhee2
1Department of Biomedical and Preclinical Sciences, Faculty of Medicine, University of Liege, Liège, Belgium.
Anatomical Sciences Education
|October 7, 2023
Summary
This study uses machine learning and learning analytics to analyze virtual microscopy user behavior in histology education. It empirically demonstrates how digital tools predict student success and validate instructional designs.
Area of Science:
- Digital learning environments
- Histology education
- Machine learning applications in education
Background:
- Virtual microscopy (VM) enhances histology learning but lacks empirical evidence on pedagogical impact.
- Digitized slides in VM offer new avenues for studying student behavior during histology learning.
- Learning analytics derived from VM user data can predict academic success.
Purpose of the Study:
- To analyze student perceptions and user behavior data using machine learning algorithms.
- To develop predictive learning analytics for identifying behaviors conducive to academic success in histology.
- To validate instructional designs and align educational components within a virtual microscopy environment.
Main Methods:
- Collected perception, performance, and user behavior data from 552 students using the Cytomine® virtual microscope platform.
- Employed an ensemble of machine learning algorithms, including the extra-tree regression method and predictive statistics.
- Utilized predictive algorithms to identify key histological slides, descriptive tags, and student behaviors linked to academic achievement.
Main Results:
- Identified 10 specific student behaviors associated with academic success in histology.
- Validated the instructional design by aligning educational purpose, learning outcomes, and evaluation methods.
- Developed a predictive model for student examination scores with a low error margin (<0.5/20).
Conclusions:
- Empirically demonstrated the value of virtual microscopy and digital learning environments in histology education.
- Highlighted the utility of machine learning and learning analytics for understanding and improving student learning.
- Provided a data-driven approach to validating and refining histology instructional strategies.
