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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.

This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation, praise and other. Four multi-label classification methods of the data transformation approach - binary relevance, classification chains, power labelset and rakel-d - are compared with the base algorithms SVM, Random Forest, Logistic Regression and Naive Bayes. The methodology was applied to a case study in which 11013 feedbacks written in Spanish language from 121 online courses of the Law degree from a public university in Mexico were collected from the Blackboard learning manager system. The results show that the random forests algorithms and vector support machines will have the best performance when using the binary relevance transformation and classifier chains methods.

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