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Open AccessArticle

Monitoring Students at the University: Design and Application of a Moodle Plugin

1
Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Research Group DATAHES, Pº Comendadores s/n, 09001 Burgos, Spain
2
Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group ADMIRABLE, Escuela Politécnica Superior, Avd. de Cantabria s/n, 09006 Burgos, Spain
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(10), 3469; https://doi.org/10.3390/app10103469
Received: 25 April 2020 / Revised: 11 May 2020 / Accepted: 12 May 2020 / Published: 18 May 2020
(This article belongs to the Special Issue Smart Learning)
Early detection of at-risk students is essential, especially in the university environment. Moreover, personalized learning has been shown to increase motivation and lower student dropout rates. At present, the average dropout rates among students following courses leading to the award of Spanish university degrees are around 18% and 42.8% for presential teaching and online courses, respectively. The objectives of this study are: (1) to design and to implement a Modular Object-Oriented Dynamic Learning Environment (Moodle) plugin, “eOrientation”, for the early detection of at-risk students; (2) to test the effectiveness of the “eOrientation” plugin on university students. We worked with 279 third-year students following health sciences degrees. A process for extracting information records was also implemented. In addition, a learning analytics module was developed, through which both supervised and unsupervised Machine Learning techniques can be applied. All these measures facilitated the personalized monitoring of the students and the easier detection of students at academic risk. The use of this tool could be of great importance to teachers and university governing teams, as it can assist the early detection of students at academic risk. Future studies will be aimed at testing the plugin using the Moodle environment on degree courses at other universities. View Full-Text
Keywords: student guidance; personalized learning; Machine Learning; Moodle; plugin student guidance; personalized learning; Machine Learning; Moodle; plugin
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MDPI and ACS Style

Sáiz-Manzanares, M.C.; Marticorena-Sánchez, R.; García-Osorio, C.I. Monitoring Students at the University: Design and Application of a Moodle Plugin. Appl. Sci. 2020, 10, 3469. https://doi.org/10.3390/app10103469

AMA Style

Sáiz-Manzanares MC, Marticorena-Sánchez R, García-Osorio CI. Monitoring Students at the University: Design and Application of a Moodle Plugin. Applied Sciences. 2020; 10(10):3469. https://doi.org/10.3390/app10103469

Chicago/Turabian Style

Sáiz-Manzanares, María C.; Marticorena-Sánchez, Raúl; García-Osorio, César I. 2020. "Monitoring Students at the University: Design and Application of a Moodle Plugin" Appl. Sci. 10, no. 10: 3469. https://doi.org/10.3390/app10103469

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