ABSTRACT
Building Information Modelling is being adopted worldwide and universities are thus expected to provide the market with new professionals with BIM knowledge and skills. However, introduction of this theme into the curriculum can be challenging to teaching staff. Having successful implementation examples can help carrying on this task. This paper presents the structure, syllabus, adopted tools and activities of an introductory BIM course offered to first-year engineering students. Implemented with only 2 credits, it covers BIM fundamental concepts and develops collaboration skills and abilities with BIM software tools. It was effectively deployed on big classes and successfully offered both in face-to-face and remote modes, adopting a practice focus. An innovative organization for student group projects was adopted, enabling student participation on several projects, performing a different role in each one. Perceived benefits to students' development are reported. The covid-19 pandemics impact is discussed. Future improvements in the course are suggested. Overall results achieved were considered very good. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Building Information Modelling is being adopted worldwide and universities are thus expected to provide the market with new professionals with BIM knowledge and skills. However, introduction of this theme into the curriculum can be challenging to teaching staff. Having successful implementation examples can help carrying on this task. This paper presents the structure, syllabus, adopted tools and activities of an introductory BIM course offered to first-year engineering students. Implemented with only 2 credits, it covers BIM fundamental concepts and develops collaboration skills and abilities with BIM software tools. It was effectively deployed on big classes and successfully offered both in face-to-face and remote modes, adopting a practice focus. An innovative organization for student group projects was adopted, enabling student participation on several projects, performing a different role in each one. Perceived benefits to students’ development are reported. The covid-19 pandemics impact is discussed. Future improvements in the course are suggested. Overall results achieved were considered very good. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Collaboration is one required skill for the future workforce that requires constant practice and evaluation. However, students often lack formative feedback and support for their collaboration skills during their formal learning. Current technologies for emergent learning due to COVID-19 could make visible digital traces of collaboration to support timely feedback. This work aims to automatically detect the group work environment using speech data captured during group activities. Grounded in literature and students’ perspectives, this work defines and implements three indicators for detecting the work environment namely noise, silence and speech time. Three experts rated two hundred thirty-two video instances lasting 30-secs each to get a group work environment score. We report the results of two machine learning models for detecting the group work environment and briefly reflect on these results. © 2022, Springer Nature Switzerland AG.