Abstract:

Following the implementation of new university curricula, where previously annual courses have become semester-based, it has been detected that Master's students in Agricultural Engineering needs to develop their experimental design and data science skills. Those, with mathematical and statistically advanced tools, are fundamental for their professional future and employment, and basically for the economic growth of companies and the progress of society. Learning hard skills not specific to the subject, in addition to their skills, is a complex challenge because it cannot involve additional work in teaching students. It is necessary to find new learning techniques so that all the proposed skills can be taught and learned by the students. So, the aim is to apply Aronson´s Puzzle Technique (APT) to achieve experiment design and data science skills, by planning an entire experiment from the design phase to the making of a results report, including analysis and interpretation. The introduction of a new teaching methodology of Aronson's method is proposed in the course "Productivity and Management of Agricultural Systems”, in which the teaching lab consisted in testing the effects of different water management and nitrogen application experiments in a maize crop using a classical methodology. To develop APT in the classroom, students will be divided into four groups, and each member will be considered a subject specialist. In this way, groups of specialists in four areas will be formed, who will be provided with didactic materials and reference literature on their respective areas. The specialization areas will be experimental design, data matrix collection and management, computer programming and statistical analysis, and discussion of results and report writing. Once the specialization process is completed, groups of four students will be formed, each with a specialist in each area. After forming these hybrid groups, the course's case study will be presented, initiating the collaborative experimental process. The results will be evaluated as a group, by correcting the final results report, and individually, through objective tests, before and after, about hard skills (specific, experimental design, and data science) and teaching surveys. The main expected conclusion is that students develop experimental design skills not previously addressed and work on other skills introduced in their training, such as effective communication, teamwork, and data science skills. These group-earning proposals can be used in other master subjects and other master sciences where any experimental approach may be necessary.

Keywords:

Agricultural, skills, biological engineering, data science.