Hyperparameter tuning for multi-label classification of feedbacks in online courses

Issue title: Recent Advances in Language & Knowledge Engineering

Guest editors: David Pinto, Beatriz Beltrán and Vivek Singh

Article type: Research Article

Authors: Ruiz Alonso, Dorian^{; *} | Zepeda Cortés, Claudia | Castillo Zacatelco, Hilda | Carballido Carranza, José Luis

Affiliations: Facultad de Ciencias de la Computación, Benemérita Universidad Autónoma de Puebla

Correspondence: [*] Corresponding author. Dorian Ruiz Alonso. E-mail: [email protected].

Abstract: In this work, we propose the extension of a methodology for the multi-label classification of feedback according to the Hattie and Timperley feedback model, incorporating a hyperparameter tuning stage. It is analyzed whether the incorporation of the hyperparameter tuning stage prior to the execution of the algorithms support vector machines, random forest and multi-label k-nearest neighbors, improves the performance metrics of multi-label classifiers that automatically locate the feedback generated by a teacher to the activities sent by students in online courses on the Blackboard platform at the task, process, regulation, praise and other levels proposed in the feedback model by Hattie and Timperley. The grid search strategy is used to refine the hyperparameters of each algorithm. The results show that the adjustment of the hyperparameters improves the performance metrics for the data set used.

Keywords: Text mining, multi-label classification, hyperparameter tuning, online courses

DOI: 10.3233/JIFS-219238

Journal: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 5, pp. 4493-4501, 2022