Professional development

Professional development, also known as professional education, is learning that leads to or emphasizes education in a specific professional career field or builds practical job applicable skills emphasizing praxis in addition to the transferable skills and theoretical academic knowledge found in traditional liberal arts and pure sciences education. It is used to earn or maintain professional credentials such as professional certifications or academic degrees through formal coursework at institutions known as professional schools, or attending conferences and informal learning opportunities to strengthen or gain new skills. Professional education has been described as intensive and collaborative, ideally incorporating an evaluative stage. There is a variety of approaches to professional development or professional education, including consultation, coaching, communities of practice, lesson study, case study, capstone project, mentoring, reflective supervision and tech

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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

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

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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