An Ontology-Based Study Recommendation System for Dental Anatomy (Abstract)

Jon Fernquest, Mae Fah Luang University, IT Department, 5/11/2021

[Full Index]

Dissertation Proposal Abstract: Recommendation systems constitute a substantial area of research in applied artificial intelligence and machine learning, deployed in various use cases from product recommendation at Amazon to networking recommendation at LinkedIn (Aggarwal, 2016).

Lindsey (2014) proposes study recommendation systems as an extension of this idea, but limits itself to the problem of optimal spacing of review sessions for memorization (spaced repetition), aiming for a system for general application.

The question often arises of what to review next in fact-intensive learning domains such as language learning or the study of anatomy in dental and medical schools. By directing students to what to review next at a particular point during the progress of a course of study such systems aim to enhance student performance.

In addition to spaced repetition, the ‘ontology’ of the knowledge domain, the structure of the facts to be memorized, may also benefit the study of intensive fact-memorization subjects. Anatomical ontology, in particular, is a well-researched subject (Burger et al. 2007; Dahdul 2012; Preim et al 2018).

Recommendation systems that focus on the structure of knowledge are known as ontology or knowledge-based recommendation systems (Aggarwal 2016:167-197).

An ontology-based study recommendation system would have as its primary emphasis employing the ontology implicit in the subject matter and course syllabus as a vehicle for pinpointing patterns of difficulty that students encounter in mastering facts, with a secondary emphasis on discerning the ideal interval between review of facts (spaced repetition).

In addition to a working system for learning dental anatomy aimed at maximizing student performance, the present project also aims at a critical re-assessment of how dental anatomy is presented in textbooks and how the subject matter might be reviewed to enhance comprehension and retention in memory with possible recommendations for pedagogical best practice.

REFERENCES

Aggarwal, Charu C. Recommender Systems: The Textbook. Springer. 2016.

Burger, Albert, Duncan Davidson, and Richard Baldock, eds. Anatomy ontologies for bioinformatics: principles and practice. Springer. 2007.

Dahdul, Wasila M., James P. Balhoff, David C. Blackburn, Alexander D. Diehl, Melissa A. Haendel, Brian K. Hall, Hilmar Lapp et al. "A unified anatomy ontology of the vertebrate skeletal system." PloS one 7, no. 12 2012: e51070.

Lindsey, Robert. "Probabilistic models of student learning and forgetting." PhD dissertation, University of Colorado at Boulder, 2014.

Preim, Bernhard, and Patrick Saalfeld. "A survey of virtual human anatomy education systems." Computers & Graphics 71 2018: 132-153.