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

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.

Computer Network Dynamic Balance Flow Distribution Based on Closed-Loop Particle Swarm Feedback Model

Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.

Evaluation of the effectiveness of handwashing training given to paramedic students remotely

Background and objectives: The COVID-19 pandemic has affected face to face medical education and training activities around the world. The aim of this study was to provide remote practical handwashing training to health sciences students and to measure the effectiveness of the training provided and to create a feedback model. Methods: Students of the Paramedic department were included in the study. Two virtual classrooms were created via Zoom Video Communication system. An 11-step handwashing algorithm was developed. Two hours of remote handwashing training was given. Participants were asked to apply the handwashing application they learned at their own location and to record videos. Application videos were evaluated and scored. Results: A total of 135 Term-1 and Term 2 students of the Paramedic department participated in the study. The duration of the evaluated videos was on average 57.67 ± 12.69 (34-95) seconds. Fifty-five (40.7%) of the participants successfully completed all the steps and their average success score was 10.3 ± 0.67 (8-11). The most failure (33.3%) in the process steps was the 9th step in which the wrists are rubbed with soap. Conclusion: Suitable teaching and feedback methods are required for medical and health science students who receive education and practical training remotely from home. Ibrahim Med. Coll. J. 2021; 15(2): 18-25