EVALUATION OF THE SELF EFFICACY OF LEARNERS DURING INTENSIVE STATISTICAL TRAINING SESSIONS

With the growing access to spatial data, through free satellite imagery, cheap drone cameras and GPS on all sort of devices, many applications in agriculture and environmental sciences can benefit from those new sources of data.

The importance of this new field justifies the creation of lifelong learning courses. The OpenSpat [1] training course is a European master level course for adult who already have statistical skills and wish to be trained in the spatial data analysis. This course is the result of an Erasmus+ collaboration project between three partners (University of Liege, University of Lisboa and Montpellier SupAgro), and is based on free and open tools like QGIS and R.

In order to assess this new course we have evaluated, during a testing session, some parameters (self-efficacy, the task value, the learners’ interest/enjoyment, the acquired competence, the professor’s attitude and the level of commitment to peer learning activities) which are related to the motivation of the learners.