Abstract:

The article will present the development of the tool "Deleted for Blind Review" (DFBR), used at the DFBR University in the automation engineering Technical & Scientific area.

In the DFBR University, the pressure to deliver well trained engineers is steadily high in the last two decades, thus producing a well-known situation of massification in Higher Education Institutions, namely in engineering degrees. In the school year of 2013/14, the course where the tool was used has about 300 students, despite quite low retention rates. The full article will also include a brief characterization of the engineering program, course and expected outcomes in full alignment with the ideas promoted by the EurAce referential for the accreditation of engineering programs and also in strong consonance with the ideas defended by the Bologna process. The course includes lab work and a part of those uses Problem Based Learning methodology.

In the last decade, the professors of the mentioned course have tried to limit the usage of real world industrial equipments because of budget concerns, always without hindering the learning process. Adequate simulation tools were sought on the market but not found, mainly because the needs of a full blown engineer are frequently not the same as those of an early engineering student. At that point, the decision was made to develop an in-house tool, adequate for students. Industrial-grade equipment was not totally set aside, only reserved for latter stages and the actual usage strategy allowed the number of equipments to be halved.

The article will go on briefly describing the “DFBR” tool and new strategies available for lab classes and PBL.
As control groups would be unethical, students’ quiz data from the two last editions of the course are used to evaluate learning (self-assessed). Grading strategy and coordination with the university’s LMS is also addressed. Final grades of the course and satisfaction are also discussed.

The students’ assessment is that the “DFBR” tool is very useful for the learning process and easier to use than the available industrial counterpart.

Continuous improvement efforts have tried to push students to adequate PBL work only possible with the tool, with some results hinting deep learning in the technical area at stake.

Some final thoughts, lessons learned and good practices are also to be summarized in the full article.

Future work will also be presented.

Keywords:

engineering, automation, learning.