Analysis of the perception that students have of a subject. Application to Integrated Information Systems of the Industrial Engineering Degree
Keywords:Student perception, Task Value, Teaching, Integrated Information System
The main purpose of this work is to establish and analyze the perception that students of the Degree in Industrial Engineering (GIOI) have about the subject Integrated Information Systems (IIS) in relation to the Task Value, and how this perception varies by taking a subject of the subject-matter analyzed. To achieve the objective, two surveys have been carried out on the students of the subject “Integrated Information Systems in Industrial Organizations” of GIOI. The first survey was passed to the students at the beginning of the course, prior to taking the classes of the subject. The survey was repeated at the end of the course, once the subject was completed, to see the changes in the assessment of the students. GIOI Students have a relatively low initial assessment of IIS subject, especially when compared to other subjects of the degree. However, after taking a subject in this area, its relative assessment increases, and its perception of Utility and Cost improves significantly. The course gets the students to perceive the IIS as something key for their future work. This did not happen before the course. Consequently, it can be concluded that taking the subject changes positively the perception of the students about IIS. Once this result is obtained and validated that it is positive, the research should deepen in which activities, contents or methodologies of the course help to achieve this result, with the objective of enhancing them with respect to those that are not so effective. This work allows teachers to find out the students' perception of a particular matter-subject, and its variation after taking a subject in that matter-subject. With this information, it will be possible to design and carry out the most appropriate teaching-learning actions to the given situation, to develop, as far as possible, a deep learning of the subject.
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