Next Article in Journal
Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
Next Article in Special Issue
Implementing AutoML in Educational Data Mining for Prediction Tasks
Previous Article in Journal
Extraction of Creation-Time for Recovered Files on Windows FAT32 File System
Previous Article in Special Issue
Short CFD Simulation Activities in the Context of Fluid-Mechanical Learning in a Multidisciplinary Student Body
Article

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

1
Centro de Ciências, Tecnologias e Saúde, Universidade Federal de Santa Catarina, Araranguá 88906072, Brazil
2
Escuela de Ingeniería Civil Informática, Universidad de Valparaíso, Valparaíso 2362735, Chile
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5523; https://doi.org/10.3390/app9245523
Received: 2 November 2019 / Revised: 9 December 2019 / Accepted: 11 December 2019 / Published: 15 December 2019
Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models. View Full-Text
Keywords: at-risk students; machine learning; learning management system; blended learning; introduction to programming at-risk students; machine learning; learning management system; blended learning; introduction to programming
Show Figures

Figure 1

MDPI and ACS Style

Buschetto Macarini, L.A.; Cechinel, C.; Batista Machado, M.F.; Faria Culmant Ramos, V.; Munoz, R. Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems. Appl. Sci. 2019, 9, 5523. https://doi.org/10.3390/app9245523

AMA Style

Buschetto Macarini LA, Cechinel C, Batista Machado MF, Faria Culmant Ramos V, Munoz R. Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems. Applied Sciences. 2019; 9(24):5523. https://doi.org/10.3390/app9245523

Chicago/Turabian Style

Buschetto Macarini, Luiz A., Cristian Cechinel, Matheus F. Batista Machado, Vinicius Faria Culmant Ramos, and Roberto Munoz. 2019. "Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems" Applied Sciences 9, no. 24: 5523. https://doi.org/10.3390/app9245523

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop