Abstract:

In present universities, numerous different learning methodologies co-exist, from traditional to more innovative, active, cooperative and high-tech ones. Most professors and researchers have difficulties in knowing, comparing and assessing all these available methodologies. On the other hand, there are professors who know these methodologies but encounter vicissitudes in implementing the best activities and strategies for their own courses. This is the case, for example, of young unexperienced professors who start their teaching career. Also the case of busy professionals that bring their real world experience to some university courses. As well as researchers in charge of a university course but mainly dedicated to their projects, which are in fields of expertise far away from university learning. In this context, all professors would be delightful to easily discover the best methodology to incorporate in our specific university course, in order to improve our students’ learning process taking into account the different conditions involved. These conditions are, above all, thinking levels, number of students, available time, classroom size, hour of the day and professor’s skills.

This project aims to develop a new tool full of resources in order to help university professors to choose the best learning activities for each particular discipline, course and case. To do so, this tool takes into account thinking levels, applicability and time constraints, environmental impact, learning process, innovation and satisfaction. This project has started with the case of architectural technology courses in the Barcelona Architecture School (ETSAB), Polytechnic University of Catalonia (UPC). The tool definition relied on seminars of experts, who defined a new model incorporating a multi criteria decision making model with value functions a mathematic algorithm. This new tool has been applied for the first time to the ETSAB third year, in a construction subject course. In this course, the professors have been able to choose the most sustainable activities and strategies for each scenario considering the alternative satisfaction indexes. This first experience has been highly positive because it has assisted these professors to incorporate the best active learning methodologies in their teaching activities and to promote lecturers’ management of their classes’ contents and time. In the future, improved versions of this tool of resources will have greater interactivity, will incorporate a greater database of learning methodologies and will be applicable to more disciplines, courses and cases in order to keep improving students’ learning processes.

Keywords:

Lectures, active learning, Bloom Taxonomy, MCDM, MIVES, Knapsack.