Discovering learning processes using Inductive MinerA case study with Learning Management Systems (LMSs)

  1. Alejandro Bogarín 1
  2. Rebeca Cerezo 2
  3. Cristóbal Romero 1
  1. 1 Universidad de Córdoba
    info

    Universidad de Córdoba

    Córdoba, España

    ROR https://ror.org/05yc77b46

  2. 2 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

Revista:
Psicothema

ISSN: 0214-9915

Any de publicació: 2018

Volum: 30

Número: 3

Pàgines: 322-329

Tipus: Article

DIALNET GOOGLE SCHOLAR lock_openAccés obert editor

Altres publicacions en: Psicothema

Resum

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

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Informació de finançament

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).

Finançadors

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