Autonomous Cycles of Data Analysis based on Process Mining for the Study of the Curricular Behavior of Students

  • Sonia Duarte Universidad Pedagógica Experimental Libertador
  • Jose Aguilar Universidad de Los Andes
Keywords: Process Mining, Data Analytics, curricular behavior, Learning Analytics

Abstract

In this work, the curricular behavior of the students of a master's degree program is evaluated through Process Mining. Specifically, what is related to the determination of the internal and external factors that affect the pursuit of their studies is analyzed. To understand student behavior, the MIDANO methodology is used, which has been used for the development of data analytics applications. In particular, it is specified the Autonomous Cycles of data analysis tasks that allow studying the dropout of the master's degree program during schooling or during the development of graduate thesis, in order to determine the causes or problems that arise during the pursuit of the studies. Very encouraging results were obtained on the causes of the dropout of the master's degree program, which discover the autonomous cycles.

DOI

Downloads

Download data is not yet available.

References

A. Bogarín, C. Romero, R. Cerezo and M. Sánchez-Santillán. “Clustering for Improving Educational Process Mining”. Proc. 4th Intl Conf. Learning Analytics and Knowledge, pp. 11–15. 2014

R. Wang and O. Zaïane. “Discovering Process in Curriculum Data to Provide Recommendation.” EDM. Pp. 580-581, 2015

M. Pechenizkiy, N. Trcka, E. Vasilyeva, W. Van der Aalst and P. De Bra. “Process Mining Online Assessment Data”. International Working Group on Educational Data Mining. 2009

A. Cairns, B. Gueni, M. Fhima, S. David and N. Khelifa. “Process mining in the education domain”. International Journal on Advances in Intelligent Systems, vol. 8, pp. 219-232. 2015.

G. Sedrakyan, M. Snoeck and J. De Weerdt. “Process mining analysis of conceptual modeling behavior of novices–empirical study using JMermaid modeling and experimental logging environment”. Computers in Human Behavior, vol. 41, pp. 486-503. 2014

F. Lozano and R. Lozano, “Developing the curriculum for a new Bachelor's degree in Engineering for Sustainable Development”, Journal of Cleaner Production, vol. 64, pp. 136-146, 2014.

Y. Moreno, C. Aguilar and F. Hidrobo. “Análisis de los Problemas de Rendimiento en un EVA a través de la Extracción de Conocimiento”. Revista Ingeniería al Día, vol. 4, pp. 3-24. 2018

J. Aguilar, O. Buendía, A. Pinto and J. Gutiérrez. Social learning analytics for determining learning styles in a smart classroom, Interactive Learning Environments. 2019.

K. Mattingly, M. Rice and Z. Berge, “Learning analytics as a tool for closing the assessment loop in higher education”, Knowledge Management & E-Learning: An International Journal, vol..4, pp. 236-247, 2012.

S. Iglesias-Pradas, C. Ruiz-de-Azcárate, and Á. Agudo-Peregrina, “Assessing the suitability of student interactions from Moodle data logs as predictors of cross-curricular competencies”, Computers in Human Behavior, vol. 47, pp. 81-89, 2015.

F. Pacheco, C. Rangel, J. Aguilar, M. Cerrada and J. Altamiranda, “Methodological framework for data processing based on the Data Science paradigm”, XL Latin American Computing Conference (CLEI). 2014

J. Aguilar, O. Buendia, K. Moreno and D. Mosquera, “Autonomous cycle of data analysis tasks for learning processes, In International Conference on Technologies and Innovation, Communications in Computer and Information Science Series”, vol. 658, pp. 187-202. 2016

J. Aguilar, M. Sánchez, M, J. Cordero, P. Valdiviezo-Díaz, L. Barba-Guamán and L. Chamba-Eras. “Learning analytics tasks as services in smart classrooms”. Universal Access in the Information Society, vol. 17, pp. 693–709, 2018

J. Aguilar, J. Cordero and O. Buendía. “Specification of the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom”, Journal of Educational Computing Research, vol. 56, pp. 866-891. 2018

M. Sánchez., J. Aguilar, J. Cordero, P. Valdiviezo-Díaz, L. Barba-Guamán, and L. Chamba-Eras “Cloud Computing in Smart Educational Environments: Application in Learning Analytics as Service”. In: Rocha Á., Correia A., Adeli H., Reis L., Mendonça Teixeira M. (eds) New Advances in Information Systems and Technologies. Advances in Intelligent Systems and Computing, vol. 444,. pp. 993-1002 2016.

Reglamento de Estudios de Postgrado. Universidad Pedagógica Experimental Libertador. Gaceta 1-2018. Resolución Nº 2018.488.056. 2018

Published
2021-01-01
How to Cite
[1]
S. Duarte and J. Aguilar, “Autonomous Cycles of Data Analysis based on Process Mining for the Study of the Curricular Behavior of Students”, LAJC, vol. 8, no. 1, pp. 54-69, Jan. 2021.
Issue
Vol 8 No 1 (2021)
Section
Research Articles for the Regular Issue

Licencia Creative Commons

 Copyright Notice

Authors who publish this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-Non-Commercial-Share-Alike 4.0 International 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.

Disclaimer

LAJC in no event shall be liable for any direct, indirect, incidental, punitive, or consequential copyright infringement claims related to articles that have been submitted for evaluation, or published in any issue of this journal. Find out more in our Disclaimer Notice.