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Automated essay scoring and the future of educational assessment in medical education
Author(s) -
Gierl Mark J,
Latifi Syed,
Lai Hollis,
Boulais AndréPhilippe,
Champlain André
Publication year - 2014
Publication title -
medical education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 138
eISSN - 1365-2923
pISSN - 0308-0110
DOI - 10.1111/medu.12517
Subject(s) - summative assessment , formative assessment , computer science , process (computing) , context (archaeology) , scoring system , writing assessment , educational measurement , artificial intelligence , natural language processing , machine learning , mathematics education , curriculum , psychology , medicine , pedagogy , surgery , operating system , paleontology , biology
Context Constructed‐response tasks, which range from short‐answer tests to essay questions, are included in assessments of medical knowledge because they allow educators to measure students’ ability to think, reason, solve complex problems, communicate and collaborate through their use of writing. However, constructed‐response tasks are also costly to administer and challenging to score because they rely on human raters. One alternative to the manual scoring process is to integrate computer technology with writing assessment. The process of scoring written responses using computer programs is known as ‘automated essay scoring’ ( AES ). Methods An AES system uses a computer program that builds a scoring model by extracting linguistic features from a constructed‐response prompt that has been pre‐scored by human raters and then, using machine learning algorithms, maps the linguistic features to the human scores so that the computer can be used to classify (i.e. score or grade) the responses of a new group of students. The accuracy of the score classification can be evaluated using different measures of agreement. Results Automated essay scoring provides a method for scoring constructed‐response tests that complements the current use of selected‐response testing in medical education. The method can serve medical educators by providing the summative scores required for high‐stakes testing. It can also serve medical students by providing them with detailed feedback as part of a formative assessment process. Conclusions Automated essay scoring systems yield scores that consistently agree with those of human raters at a level as high, if not higher, as the level of agreement among human raters themselves. The system offers medical educators many benefits for scoring constructed‐response tasks, such as improving the consistency of scoring, reducing the time required for scoring and reporting, minimising the costs of scoring, and providing students with immediate feedback on constructed‐response tasks.