Learning Micromanipulation, Part 1: An Approach Based on Multidimensional Ability Inventories and Text Mining
AbstractIn the last decades, an effort has been made to improve the efficiency of high-level and academic education players. Nowadays, students’ preferences and habits are continuously evolving and so the educational institutions deal with important challenges, such as not losing attractiveness or preventing early abandonment during the programs. In many countries, some important universities are public, and so they receive national grants that are based on a variety of factors, on which the teaching efficiency has a great impact. This contribution presents a method to improve students commitment during traditional lessons and laboratory tests. The idea consists in planning some activities according to the students’ learning preferences, which were studied by means of two different approaches. The first one was based on Gardner’s multiple intelligence inventory, which is useful to highlight some peculiar characteristics of the students on the specific educational field. In the second method, direct interviews, voice recognition, and text mining were used to extract some interesting characteristics of the group of students who participated in the projects. The methods were applied in May 2018 to the students attending the course of Micro-Nano Sensors and Actuators for the postgraduate academic program dedicated to Industrial Nanotechnologies Engineering of the University of Rome La Sapienza. The present paper represents the first part of the investigation and it is dedicated essentially to the adopted methods. The second part of the work is presented in the companion paper dedicated to the presentation of the practical project that the students completed before the exam. View Full-Text
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Biancucci, G.; Bonciani, G.; Fioravanti, S.; Binni, A.; Lucchese, F.; Matrisciano, A. Learning Micromanipulation, Part 1: An Approach Based on Multidimensional Ability Inventories and Text Mining. Actuators 2018, 7, 55.
Biancucci G, Bonciani G, Fioravanti S, Binni A, Lucchese F, Matrisciano A. Learning Micromanipulation, Part 1: An Approach Based on Multidimensional Ability Inventories and Text Mining. Actuators. 2018; 7(3):55.Chicago/Turabian Style
Biancucci, Gaetano; Bonciani, Giovanni; Fioravanti, Simona; Binni, Antonello; Lucchese, Franco; Matrisciano, Apollonia. 2018. "Learning Micromanipulation, Part 1: An Approach Based on Multidimensional Ability Inventories and Text Mining." Actuators 7, no. 3: 55.
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