Intelligent textbooks are often engineered with an explicit representation of their concepts and prerequisite relations (PR). PR identification is hence crucial for intelligent textbooks but still presents some challenges, also when performed by human experts. This may cause PR-annotated datasets to be inconsistent and compromise the accuracy of automatic creation of enhanced learning materials. This paper investigates possible reasons for PR disagreement and the nature of PR itself, with the aim of contributing to the development of shared strategies for PR annotation, analysis and modelling in textbooks.

Digging into prerequisite annotation

Alzetta C.;Galluccio I.;Koceva F.;Passalacqua S.;Torre I.
2020-01-01

Abstract

Intelligent textbooks are often engineered with an explicit representation of their concepts and prerequisite relations (PR). PR identification is hence crucial for intelligent textbooks but still presents some challenges, also when performed by human experts. This may cause PR-annotated datasets to be inconsistent and compromise the accuracy of automatic creation of enhanced learning materials. This paper investigates possible reasons for PR disagreement and the nature of PR itself, with the aim of contributing to the development of shared strategies for PR annotation, analysis and modelling in textbooks.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1037253
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