Abstract:

Agriculture is subject to continuous change. The development of new techniques requires agricultural experimentation. So, the use of adequate statistical procedures for the design of experiments and for the subsequent analysis of the data is crucial. Therefore, the subject Statistics is included in the syllabus corresponding to the Degree and Master Degree of Agricultural Engineering at Spanish Universities.

The application of statistical computing tools is widely extended for obtaining solutions to real problems in all engineering branches. Therefore, a thorough training in such tools is provided in almost all engineering degree programs. Usually, only a basic general training in statistics is provided in the first courses focusing on the resolution of purely statistical problems.

Up to recent years, the teaching activities focused on the theoretical concepts and their application through problem resolution. In parallel, practical sessions were carried out for training the students in the application of statistical software. However, this methodology presented some problems. The students might not link properly the theoretical concepts and their application with the software for facing real problems.

This paper describes updated teaching activities for the subject Statistics in the Degree Program of Agricultural Engineering at the Universitat de les Illes Balears in order to contribute to offset the difficulties of the students for acquiring the scheduled competencies. The new approach strengthens the role of the practical sessions as a way to acquire key competencies through self-learning, because they are better suited for the actual skills of the students. The adapted evaluation system aims to assess the main skills of the students, i.e. it intends to use the mark as an indicator of the validity of the student for facing related tasks in his/her future professional career.

Accordingly, the main changes proposed consist in three issues: a) enhancing practical sessions in the computer lab using statistical software, b) weekly continuous evaluation based on the presentation and discussion of encountered results, and c) problem resolution using the proposed guidelines as remote activity.

Keywords:

Statistics, statistical software, agricultural engineering, self-learning.