Informal learning has been a global hot topic for the past several years. The growth of the internet and the pervasiveness of computers in everyday life means that a huge part of this informal learning is done through a computer. In the European Union, since the official recognition of informal learning in 1999 with the Bologna Treaty, a number of guidelines and proposals have been published providing techniques and recommendations for translating informal learning outcomes to formal competences. Most of these guidelines depend on an evaluator (internal or external) to oversee and certify the process. In our work, we propose the usage of a more social and dynamic framework for gathering, validating and promoting a learner´s digital informal learning. This framework is based primarily on peer interaction and peer assessment instead of employing experts and provides mechanisms for personalized recommendations in order to introduce further informal learning opportunities to the learners. We propose an approach where a learner´s evaluation happens organically while other learners adopt the same activities and evaluate them positively or negatively.
Free Libre Open Source Software (FLOSS) has become a strategic asset in software development, and open source communities behind FLOSS are a key player in the field.The analysis of open source community dynamics is a key capability in risk management practices focused on the integration of FLOSS in all types of organizations. We are conducting research in developing methodologies for managing risks of FLOSS adoption and deployment in various application domains. This paper is about the ability to systematically capture, filter, analyze, reason about, and build theories upon, the behavior of an open source community in combination with the structured elicitation of expert opinions on potential organizational business risk. The novel methodology presented here blends together qualitative and quantitative information as part of a wider analytics platform. The approach combines big data analytics with automatic scripting of scenarios that permits experts to assess risk indicators and business risks in focused tactical and strategic workshops. These workshops generate data that is used to construct Bayesian networks that map data from community risk drivers into statistical distributions that are feeding the platform risk management dashboard. A special feature of this model is that the dynamics of an open source community are tracked using social network metrics that capture the structure of unstructured chat data. The method is illustrated with a running example based on experience gained in implementing our approach in an academic smart environment setting including Moodbile, a Mobile Learning for Moodle (www.moodbile.org). This example is the first in a series of planned experiences in the domain of smart environments with the ultimate goal of deriving a complete risk model in that field.
The placements and internships are one of the main paths to get professional background and some skills for students, especially in areas like informatics and computer sciences. The European-funded VALS project tries to promote the virtual placements and establish a new initiative in virtual placements called Semester of Code. This initiative binds higher education institutions, students, companies, foundations and Open Source projects in order to create virtual placements and solve needs that they have in relation with those placements. This paper introduces some projects about virtual placements that other institutions and companies perform, also the paper describes the needs, opinions and considerations about the virtual placements for each stakeholder involved in the placements, to finally explain the design decisions and actions behind the Semester of Code, and how they are intended to get better virtual placements and successful results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.