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Recommending Learning Objects with Arguments and Explanations
- Source :
- Applied Sciences, Volume 10, Issue 10, Applied Sciences, Vol 10, Iss 3341, p 3341 (2020), RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia, instname
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- [EN] The massive presence of online learning resources leads many students to have more information than they can consume efficiently. Therefore, students do not always find adaptive learning material for their needs and preferences. In this paper, we present a Conversational Educational Recommender System (C-ERS), which helps students in the process of finding the more appropriated learning resources considering their learning objectives and profile. The recommendation process is based on an argumentation-based approach that selects the learning objects that allow a greater number of arguments to be generated to justify their suitability. Our system includes a simple and intuitive communication interface with the user that provides an explanation to any recommendation. This allows the user to interact with the system and accept or reject the recommendations, providing reasons for such behavior. In this way, the user is able to inspect the system's operation and understand the recommendations, while the system is able to elicit the actual preferences of the user. The system has been tested online with a real group of undergraduate students in the Universidad Nacional de Colombia, showing promising results.<br />This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government, and by the Generalitat Valenciana (PROMETEO/2018/002) project.
- Subjects :
- Computer science
Process (engineering)
02 engineering and technology
Recommender system
lcsh:Technology
Argumentation theory
Educational recommender systems
lcsh:Chemistry
Human–computer interaction
argumentation
020204 information systems
Argumentation
0202 electrical engineering, electronic engineering, information engineering
educational recommender systems
General Materials Science
Instrumentation
lcsh:QH301-705.5
Fluid Flow and Transfer Processes
Explanations
lcsh:T
Process Chemistry and Technology
Online learning
General Engineering
lcsh:QC1-999
Computer Science Applications
lcsh:Biology (General)
lcsh:QD1-999
lcsh:TA1-2040
explanations
020201 artificial intelligence & image processing
Adaptive learning
lcsh:Engineering (General). Civil engineering (General)
LENGUAJES Y SISTEMAS INFORMATICOS
Communication interface
lcsh:Physics
Subjects
Details
- Language :
- English
- ISSN :
- 20763417
- Database :
- OpenAIRE
- Journal :
- Applied Sciences
- Accession number :
- edsair.doi.dedup.....a4af0b3ca9faf2fbe255c917d650a254
- Full Text :
- https://doi.org/10.3390/app10103341