Abstract:

According to the Bologna Framework, each full-time year course should be composed of 60 ECTS-credits. The ECTS credits provide information about the time dedicated to face to face activities and also the time required for self-learning, studying, etc. Each ECTS credit corresponds to about 25 hours of work. These 25 hours can be divided into, at least, two different blocks: about 10 hours correspond to face to face activities and the rest to personal work, self-learning, studying, etc. Taking into account the ECTS load in each course, the work load per course is estimated in about 1500 hours. Taking into account that the course is divided into two semesters, and that each semester has an average of 15 weeks, the students are expected to dedicate about 50 hours of work per week.
The equity in the distribution of the work load is crucial, because the existence of peaks and valleys of workloads limits the learning outcomes of the students. This can be explained because during the valleys the students tends to relax their working rhythm, reducing their potential capacity, and because during the peaks the students are not able to afford all the work load, leading to a situation in which the students discard several tasks dedicating time and effort only to a few tasks considered crucial.
In this context it is clear that it is really important to programme all the activities of each subject in the frame of a course in order to avoid overloads and insufficient workloads, which could lead the students to undesirable learning outcomes.
The aim of this work was to analyse the weekly work load distribution in the second course of the Chemical Engineering Degree at the University of Castilla-La Mancha, identifying the work load peaks and valleys. As a result of this analysis it was observed that during the first weeks of each semester the work load was under the optimum, about a 80% of the optimum workload. After that appeared a peak of the work load due to the existence of laboratory activities during the afternoon coupled to the teaching activities carried out in the morning leading to a work load of about 150% of the optimum work load. The semester was finished also with a very high work load due to the existence of mid-term exams of several subjects which leaded again to a work load higher than the optimum, being a typical value about 120% of the optimum work load. In order to avoid the inconveniences of this unbalanced distribution, the work load was analysed and distributed, as good as possible, by using programming tools. With these tools the variability in the work load distribution was difficult to eliminate, because of the existence of teaching activities in the morning coinciding with practical ones in the afternoon, but at least it was possible to alternate weeks with high and low workloads. We decided to alternate the workloads in order to allow the students to recover the rhythm of the course. Under our point of view, this new distribution could result in better learning outcomes of the students.

Keywords:

Short-term scheduling, workload.