infonet-economy

Marketing instructors have traditionally sought to use experiential and active learning methods in their teaching. The flipped classroom is a learner-centered innovative pedagogical approach that moves the delivery of class material outside the classroom to focus on collaborative activities during class sessions. This qualitative exploratory research aims at understanding how students perceive their experience and the outcome of flipped classroom marketing courses in two Swiss universities. The analysis shows mixed results depending on the student population involved, as well as on the format of the preparatory material provided.

City-Zen is an interactive temporal knowledge-browsing platform that aims to valorise cultural heritages. While many organizations propose relevant data sets, they are hardly accessed, analysed and reused because of the formats inconsistency and the inappropriate information browsing and visualization. The goal of the project is to valorise the existing cultural heritage through a citizen centric design platform. The use case of this project involves a user willing to discover the history of a region and to embark in a cultural journey in the past. This project addresses a novel approach of data analysis of both assessment methods of data quality and spatio-temporal information. The proposed paper will describe the approach, its model and its implementation tests in the context of the State of Wallis.

Algorithms, data, services seem to create a net of semantic stability for users to consume information but come with concrete disadvantages regarding the way we understand, discuss and teach them. Social bookmarking applications are no exception to this, as they follow a similarly opaque way to organize and publish data. This article will examine the possibility to shift from predetermined results to open and descriptive protocols and applications that revisits fundamental web user activities such as search, classification, group formation and valorization of participation. This approach combines both a data handling protocol (CoWaBoo) and an application (collective observatories) that serve as wider concept and practice of application use and development. Our initial results from 2015 and 2016 university group course works, contributes to shifting our attention from how things end up, to how things become, an important competence in the field of ICT training and education.

The Health On the Net’s Foundation (HON) Code of Conduct, HONcode, is the oldest and the most used ethical and trustworthy code for medical and health related information available on the Internet. Until recently, websites voluntarily applying for the HONcode seal were evaluated manually by an expert medical team according to 8 principles, referred to as criteria, and associated published guidelines. In the scope of the European project Kconnect, HON is developing an automated system to identify the 8 HONcode criteria within health webpages. When the research on the development of such a system evolved from simple algorithmic testing to a real full-content setting, it revealed a number of issues. The preceding study consisted in taking a set of 27 health-related websites and having them assessed for their compliance to each of the 8 HONcode criterion, first manually by senior HONcode experts, and then through supervised machine learning by the automated system. The results showed disc repancies mainly for two criteria: “submerged content” under the Complementarity criterion and “extremely low recall” under the Date Attribution criterion. In this article, the authors investigate different approaches to solve the problems related to each of these criteria, namely a customized Named Entity Recognition Model instead of a machine learning component for Date Attribution, and a sliding window instead of the whole document as a unit of detection for Complementarity. The results obtained show that the newly adapted automated system greatly improves accuracy: 74% vs. 41% for the Date Attribution criterion and 74% vs. 22% for the Complementarity criterion.

Credit Commons is an infrastructure, aiming to link existing and new communities in an open, grassroots, intercommunity value exchange environment. This paper aims to demonstrate that an infrastructure for intra and inter community exchange cannot be considered, merely as a technological project. It needs to be founded on a sociotechnical approach and deployed within an inclusive approach ensuring the early engagement of users and communities, as well as, other organisations such as academic institutions. Credit Commons comes to reinforce existing currency interchange hub solutions (Huber et Martignoni, 2013) having Switzerland as their initial center of activities. In this sense, the Credit Commons infrastructure would operate on three areas developed in this paper: Firstly, explaining the concept of mutual credit in a credit commons infrastructure. inclusive, crosscommunity participation, based on local communities accounting systems and mutual credit principles. Business entities engaging in bartering, online payments and transactions services, timebanks, LETS each with their respective time management systems are possible Credit Commons nodes. Secondly, mapping and discussing on the Credit Commons community. This part is looking closer to what kind of pilots and communities would be interested to use this infrastructure. The Credit Commons propose an inclusive design methodology, integrating a horizontal gender approach on Credit Common’s impact, social metrics and evaluation Thirdly, early deploying the open protocol layer as the backbone of the Credit Commons, where the aforementioned entities develop their activities. CoWaBoo is a protocol aiming to provide a sociotechnical section in between an early technical implementation of a intercommunity transactions protocol, confronted to group formation, knowledge curation and redocumentation of results in distributed, blockchain, architectures. This paper will end with a short discussion on the promise of a more transparent complexity that protocols based on blockchain and smart contracts bring with them. We are entering a new era of possibilities, and responsibilities, where sociotechnical tools will be conditioning both to our understanding and acting in it.

The marketing education community is well suited to be a leader in online education and the internet has proven a powerful tool in teaching a variety of marketing courses. Massive Open Online Courses (MOOCs) are one of the fastest growing approaches to teaching and learning in the new digital paradigm. They are categorized either as cMOOCs following a connectivist approach, or as xMOOCs based on behaviorist principles. In xMOOC environments, forums, if they function as a community, may however play a real connectivist role. This paper builds on the criteria of Herring (2004) in order to determine the existence and dynamics of a community in a Marketing MOOC. In a connectivist approach, forum participants request help, answer questions, discuss concepts and techniques and suggest application and additional learning material, thus co-creating knowledge. In today’s online teaching methods, users are included in multi-way processes which are not well accounted for in the literature often describing one-way processes. These dynamics may be key in ensuring a strong and well-functioning learning community. This research questions the existence of a clear distinction between cMOOCs and xMOOCs. It also observes that equal gender roles in marketing classes don’t seem to be replicated in MOOC environments, i.e. female participants appear to adopt lower profiles in MOOC forums.

BiTeM/SIB Text Mining (http://bitem.hesge.ch/) is a University re-search group carrying over activities in semantic and text analytics applied to health and life sciences. This paper reports on the participation of our team at the CLEF eHealth 2016 evaluation lab. The processing applied to each evaluation corpus (QUAREO and CépiDC) was originally very similar. Our method is based on an Au-tomatic Text Categorization (ATC) system. First, the system is set with a specific input ontology (French UMLS), and ATC assigns a rank list of related concepts to each document received in input. Then, a second module relocates all of the positive matches in the text, and normalizes the extracted entities. For the CépiDC corpus, the system was loaded with the Swiss ICD-10 GM thesaurus. However a late minute data transformation issue forced us to implement an ad hoc solution based on simple pat-tern matching to comply with the constraints of the CépiDC challenge. We obtained an average precision of 62% on the QUAREO entity extraction (over MEDLINE/EMEA texts, and exact/inexact), 48% on normalizing this entities, and 59% on the CépiDC subtask. Enhancing the recall by expanding the coverage of the terminologies could be an interesting approach to improve this system at moderate labour costs.