Data Mining for the Analysis of Student Assessment Results in Engineering by Applying Active Didactic Strategy

To make improvements in the teaching-learning process in educational institutions such as universities, it is necessary to analyse the results obtained and recorded from applying Active Didactic Strategies and, based on this, to propose improvements that will help to achieve the Student Outcomes established for the subject in question; the problem to be solved is thus defined, and the results to be obtained from the analysis are relevant for the improvement of student performance. The objective is to analyse the results of the student assessment, the basis for the calculation of which is based on the recording of the qualification achieved through the performance indicators defined for each criterion, of the competencies involved and aligned with the Student Outcomes of the problems proposed to the student, applying various data mining techniques. Data mining is used to treat large amounts and types of data to obtain hidden information and reveal states, patterns and trends; as well as in Education to study the behaviour of students in terms of their performance. The methodology used for the development of the work is based on the Cross-Industry Standard Process for Data Mining methodological model, which is widely used in data mining projects. The results obtained reveal that the Student's t-test and Snedecor's F-test are highly significant, as well as the determination of the lowest performance indicators in order to plan future improvement actions towards better student performance and achieve a high level of learning. Concluding that if the same teaching and learning process is applied the result will be very similar, therefore, the students have finished learning very well.

@article{Baluarte-Araya2024,
  title     = {Data Mining for the Analysis of Student Assessment Results in Engineering by Applying Active Didactic Strategy},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {César Baluarte-Araya and Oscar Ramirez-Valdez and Ernesto Suarez-Lopez and Percy Huertas-Niquén},
  doi       = {10.14569/IJACSA.2024.0150921},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150921}
}