Formation professionnelle

La formation professionnelle est le processus d'apprentissage qui permet à un salarié ou un demandeur d'emploi d'acquérir le savoir, le savoir-faire et le savoir-être (capacité et aptitude) nécessaires à l'exercice d'un métier ou d'une activité professionnelle. La formation professionnelle s’apparente beaucoup à la formation continue mais elle est bien distincte de cette dernière. En effet, elle vise à gagner de nouvelles compétences nécessaires à une évolution ou à un changement de métier. Les formations professionnelles peuvent s’inscrire dans le plan de formation de votre entreprise ou émaner de votre demande personnelle. Selon les cas la formation peut être purement théorique ou comporter aussi une partie pratique (de base ou sous forme de période d’apprentissage). La formation théorique peut être délivrée de différentes manières telles que des cours magistraux ou à l'aide d'outils pédagogiques. Que ce soit dans un but d'évolution ou de changement de métier, la formation profes

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MGT-413: Entrepreneurship & new venture strategy

A foundational course on the science and practice of launching new ventures. The purpose is to study and experience the first stages of the entrepreneurial process: from the identification of promising opportunities to the development of a viable and innovative business concept.

MGT-404: Management of intellectual property

This class provides an introduction to the management of intellectual property (IP), primarily patents. It relies on formal lectures, case studies, speakers, and a project.

BIO-455: Introduction to law and ethics

Le but du cours est de familiariser l'étudiant-e aux notions de base du droit et de l'éthique applicables à la recherche en STV et à son transfert en applications, et de lui fournir les éléments essentiels pour identifier les enjeux juridiques et éthiques dans sa future pratique professionnelle.

Accessible Maker-based Approaches to Educational Robotics in Online Learning

Amaury Robert Dame, Christian Giang, Anthony Guinchard, Aditya Mehrotra, Francesco Mondada

Educational Robotics holds the potential to promote the development of important 21st century skills, such as creativity and problem-solving skills in addition to digital literacy. However, the emergence of the Covid-19 pandemic has posed particular obstacles that had to be overcome in order to allow Educational Robotics activities to be conducted in distance learning. In the first place, the obligation to work from home limited the access to required equipment for many students. Secondly, many teachers had to face the novel challenge of creating pedagogically meaningful activities in online learning formats. Aiming to address these challenges, this work explored maker-based approaches as a way to implement Educational Robotics activities in online learning. The devised tools and activities were evaluated in two case studies performed with (i) high school students participating in a mobile robotics summer school and (ii) in-service teachers attending a professional development course on Educational Robotics. The teachers’ and students’ perception of the proposed activities was analyzed using online surveys and video interviews. The findings showed that the combination of the devised tools and activities allowed teachers and students to explore the basics of mobile robotics while helping them develop a maker mindset. The use of ubiquitous construction materials and affordable electronic components promotes accessibility of the approach. The proposed tools and activities may therefore provide an exemplary framework for more general applications of Educational Robotics in online learning that go beyond the context of emergency remote teaching.

2021

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A principled approach to the design of collaborative MOOC curricula

Stian Haklev

MOOCs have the potential to benefit from the large number of very diverse learners that participate in courses, but this requires a principled approach to MOOC curriculum development. Courses need to take into consideration the diversity of learner experience and intentions, and incorporate scripts that both benefit from the large numbers of learners (crowd-sourcing), as well as enabling small-scale intense collaboration. The real challenge is tying together a set of learning activities and the development of a community knowledge base, with the specific curriculum learning goals of the course. This paper offers a pragmatic approach to developing courses, based on the experience of a MOOC for teacher professional development.

2017

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Predicting involuntary hospitalization in psychiatry: A machine learning investigation

Mehdi Gholam

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-learning (ML) models to identify socio-demographic, clinical and procedural characteristics that predict the use of compulsory admission on a large sample of psychiatric patients. Methods We retrospectively analyzed the routinely collected data of all psychiatric admissions that occurred between 2013 and 2017 in the canton of Vaud, Switzerland (N = 25,584). The main predictors of involuntary hospitalization were identified using two ML algorithms: Classification and Regression Tree (CART) and Random Forests (RFs). Their predictive power was compared with that obtained through traditional logistic regression. Sensitivity analyses were also performed and missing data were imputed through multiple imputation using chain equations. Results The three models achieved similar predictive balanced accuracy, ranging between 68 and 72%. CART showed the lowest predictive power (68%) but the most parsimonious model, allowing to estimate the probability of being involuntarily admitted with only three checks: aggressive behaviors, who referred the patient to hospital and primary diagnosis. The results of CART and RFs on the imputed data were almost identical to those obtained on the original data, confirming the robustness of our models. Conclusions Identifying predictors of coercion is essential to efficiently target the development of professional training, preventive strategies and alternative interventions. ML methodologies could offer new effective tools to achieve this goal, providing accurate but simple models that could be used in clinical practice.

CAMBRIDGE UNIV PRESS2021

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Postgraduate education

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Grade universitaire

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Master of Business Administration

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