Abstract:

The learning outcomes that our students must achieve at university level, indicated in the case of professions regulated by the competences that they must be able to develop in their professional practice, do not leave much room for motivational pedagogical experimentations, although they are duly contrasted. However, to get involved in the type of activities to be carried out, the methodology to be followed and type of evaluation or tutoring to be applied, simultaneously with the achievement of the specific learning results of the degree, can have a great impact on the effectiveness with which our work develops.

Identifying the extent to which the generation of a learning activity influences the understanding of a concept or, in general, the development of a competency, is one of the concerns that teachers have in mind when developing the work plan of a subject.

Taking the correct decisions in this sense can have a significant influence on the learning of the group of students, sometimes directly and sometimes because of the motivation that can be created by presenting scenarios that strengthen their work capacity.

Knowing if the tasks programmed will have effectiveness cannot be elucidated previously, although the accumulated experience provides us with various clues that can be exploited to achieve greater efficiency.

The extraordinary development of the Learning Management Systems (LMS) has partly converted these clues into statistical data, which conveniently analyzed can provide us very valuable information to assess the extent to which the information presented is being used by the students, and therefore to intervene immediately if necessary.

Learning Analytics (LA) defined as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs”, can be of great help if we know how to properly analyze the intervention marks that students perform when they access the different elements and tasks programmed in the subject.

The contrast of this intervention with the results obtained from the evaluations of these tasks offers us a map of possibilities with capacity to draw conclusions from the most effective programmed activities from the point of view of the success achieved.

The main objective of this paper is to present an analysis of the use of different formative elements to increase the interest of students in the learning of fundamental concepts related to “Computer Structure and Technology” that will be necessary to understand more complex computational structures.

Contextual cards, summaries, exercises and practices, mentoring sessions, video-tutorials, self-evaluation tests, evaluative tests, are examples of activities that we have developed in the subject and checked continuously to analyze student intervention in each.

Following the principles of LA, we have extracted the data about the number of times that the students have accessed the available resources at the LMS. Grouped by their scores the distribution of the data for each group is represented by means of the well-known box-and-whisker plots that allow us to easily show the 25th and the 75th percentiles (i.e., first and third quartile).

Keywords:

Mentoring, Learning Analytics, Learning Management Systems, students’ motivation, students’ success rate.