Authors:
Matej Guid
;
Matevž Pavlič
and
Martin Možina
Affiliation:
Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, Ljubljana and Slovenia
Keyword(s):
Intelligent Tutoring Systems, Argument-based Machine Learning (ABML), ABML Knowledge Refinement Loop, Learning by Arguing, Feedback Generation, Financial Statements.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
Domain Applications and Case Studies
;
e-Learning
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Intelligent Learning and Teaching Systems
;
Intelligent Tutoring Systems
Abstract:
Argument-based machine learning provides the ability to develop interactive learning environments that are able to automatically select relevant examples and counter-examples to be explained by the students. However, in order to build successful argument-based intelligent tutoring systems, it is essential to provide useful feedback on students’ arguments and explanations. To this end, we propose three types of feedback for this purpose: (1) a set of relevant counter-examples, (2) a numerical evaluation of the quality of the argument, and (3) the generation of hints on how to refine the arguments. We have tested our approach in an application that allows students to learn by arguing with the aim of improving their understanding of financial statements.