Discovering learning processes using Inductive MinerA case study with Learning Management Systems (LMSs)
- Alejandro Bogarín 1
- Rebeca Cerezo 2
- Cristóbal Romero 1
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1
Universidad de Córdoba
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2
Universidad de Oviedo
info
ISSN: 0214-9915
Año de publicación: 2018
Volumen: 30
Número: 3
Páginas: 322-329
Tipo: Artículo
Otras publicaciones en: Psicothema
Resumen
Background: Process mining with educational data has made use of various algorithms for model discovery, principally Alpha Miner, Heuristic Miner, and Evolutionary Tree Miner. In this study we propose the implementation of a new algorithm for educational data called Inductive Miner. Method: We used data from the interactions of 101 university students in a course given over one semester on the Moodle 2.0 platform. Data was extracted from the platform’s event logs; following preprocessing, the mining was carried out on 21,629 events to discover what models the various algorithms produced and to compare their fi tness, precision, simplicity and generalization. Results: The Inductive Miner algorithm produced the best results in the tests on this dataset, especially for fi tness, which is the most important criterion in terms of model discovery. In addition, when we weighted the various metrics according to their importance, Inductive Miner continued to produce the best results. Conclusions: Inductive Miner is a new algorithm which, in addition to producing better results than other algorithms using our dataset, also provides valid models which can be interpreted in educational terms
Información de financiación
Authors gratefully acknowledge the financial subsidy provided by Spanish Ministry of Science and Technology TIN2017-83445-P and EDU2014-57571-P. We have also received funds from the European Union and the Principality of Asturias, through its Science, Technology and Innovation Plan (grant GRUPIN14-053).Financiadores
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National Plan for Science, Technology and Innovation
Saudi Arabia
- GRUPIN14-053
- European Commission European Union
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- TIN2017-83445-P
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