scholarly journals Development and implementation of an online platform for curriculum mapping in medical education

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jaroslav Majerník ◽  
Andrea Kacmarikova ◽  
Martin Komenda ◽  
Andrzej A. Kononowicz ◽  
Anna Kocurek ◽  
...  
Keyword(s):  
Medical Education ◽  
Language Processing ◽  
Building Blocks ◽  
Quality Improvements ◽  
Online Platform ◽  
Academic Environments ◽  
Efficient Management ◽  
Constant Quality ◽  
Electronic Support ◽  
Methodological Guidelines

Abstract Objectives Nowadays universities face ever-increasing demands on quality of education, which is crucial from perspective of future graduates. In face of the need of constant quality improvements of medical curricula, it is important to seek strategies for their efficient management. The general trend is to develop electronic support tools to streamline the curricular design, analysis and harmonization. Methods Based on the requirements we have identified by the needs analysis among curriculum designers, teachers and managers at five universities involved in the Building Curriculum Infrastructure in Medical Education (BCIME) project, and evidence published in literature on curriculum development, we have developed methodological guidelines on curriculum innovations and a software-based tools that help manage, map and analyse curricula in the medical and healthcare study fields. Results In this paper, we share our experiences with building and implementation of EDUportfolio, an online platform developed within our consortium and intended to facilitate harmonisation and optimisation of medical outcome-based curricula. Its functionalities and outputs were verified by pilot mapping of Anatomy curricula as taught at partner universities in five European countries. Conclusions The visualisation and the analysis of described curriculum data using natural language processing techniques revealed both the hidden relations between curriculum building blocks and a set of overlaps and gaps in curricula. In addition, we demonstrate both the usability of the platform in the context of the involved academic environments and the capability to map and compare curricula across different institutions and different countries.

scholarly journals The Sustainability Argument for Open Science

2021 ◽  
Author(s):  
Gisela Govaart ◽  
Simon M. Hofmann ◽  
Evelyn Medawar
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Scientific Progress ◽  
Building Blocks ◽  
Open Science ◽  
Academic Environments ◽  
Science Practices ◽  
Research Activities ◽  
Systemic Level ◽  
Climate Crisis

Ever-increasing anthropogenic greenhouse gas emissions narrow the timeframe for humanity to mitigate the climate crisis. Scientific research activities are resource demanding and, consequently, contribute to climate change; at the same time, scientists have a central role in advancing knowledge, also on climate-related topics. In this opinion piece, we discuss (1) how open science – adopted on an individual as well as on a systemic level – can contribute to making research more environmentally friendly, and (2) how open science practices can make research activities more efficient and thereby foster scientific progress and solutions to the climate crises. While many building blocks are already at hand, systemic changes are necessary in order to create academic environments that support open science practices and encourage scientists from all fields to become more carbon-conscious, ultimately contributing to a sustainable future.

The future of manufacturing industry: a strategic roadmap toward Industry 4.0

2018 ◽  
Vol 29 (6) ◽  
pp. 910-936 ◽  
Author(s):  
Morteza Ghobakhloo
Keyword(s):  
Language Processing ◽  
Industry 4.0 ◽  
Manufacturing Industry ◽  
Core Competencies ◽  
Building Blocks ◽  
Design Principles ◽  
Ongoing Research ◽  
Holistic View ◽  
Content Type ◽  
Technology Trends

Purpose The purpose of this paper is to conduct a state-of-the-art review of the ongoing research on the Industry 4.0 phenomenon, highlight its key design principles and technology trends, identify its architectural design and offer a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition. Design/methodology/approach The study performs a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0. The study further benefits from a comprehensive content analysis of the 178 documents identified, both manually and via IBM Watson’s natural language processing for advanced text analysis. Findings Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Industry 4.0 is no longer a hype and manufacturers need to get on board sooner rather than later. Research limitations/implications The strategic roadmap presented in this study can serve academicians and practitioners as a stepping stone for development of a detailed strategic roadmap for successful transition from traditional manufacturing into the Industry 4.0. However, there is no one-size-fits-all strategy that suits all businesses or industries, meaning that the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets. Practical implications The first step for transitioning into the Industry 4.0 is the development of a comprehensive strategic roadmap that carefully identifies and plans every single step a manufacturing company needs to take, as well as the timeline, and the costs and benefits associated with each step. The strategic roadmap presented in this study can offer as a holistic view of common steps that manufacturers need to undertake in their transition toward the Industry 4.0. Originality/value The study is among the first to identify, cluster and describe design principles and technology trends that are building blocks of the Industry 4.0. The strategic roadmap for Industry 4.0 transition presented in this study is expected to assist contemporary manufacturers to understand what implementing the Industry 4.0 really requires of them and what challenges they might face during the transition process.

scholarly journals Remote rapid cycle deliberate practice simulation training during the COVID-19 pandemic

2020 ◽  
pp. bmjstel-2020-000671 ◽  
Author(s):  
Reinis Balmaks ◽  
Luize Auzina ◽  
Isabel Theresia Gross
Keyword(s):  
Medical Education ◽  
Simulation Training ◽  
Deliberate Practice ◽  
Online Platform ◽  
Social Distancing ◽  
New Challenges ◽  
Simulation Practice

The COVID-19 pandemic is posing new challenges for medical education and simulation practice given local social distancing requirements.This report describes the use of an online platform for rapid cycle deliberate practice simulation training that can be used and tailored to local COVID-19 pandemic restrictions as it allows for participants, facilitators and simulation equipment to be apart.

scholarly journals Engaging Unmotivated Smokers to Move Toward Quitting: Design of Motivational Interviewing–Based Chatbot Through Iterative Interactions (Preprint)

2020 ◽  
Author(s):  
Fahad Almusharraf ◽  
Jonathan Rose ◽  
Peter Selby
Keyword(s):  
Motivational Interviewing ◽  
Language Processing ◽  
Interdisciplinary Collaboration ◽  
Low Cost ◽  
Building Blocks ◽  
Training Data ◽  
Free Form ◽  
Computer Engineering ◽  
Cigarette Smokers ◽  
The Web

BACKGROUND At any given time, most smokers in a population are ambivalent with no motivation to quit. Motivational interviewing (MI) is an evidence-based technique that aims to elicit change in ambivalent smokers. MI practitioners are scarce and expensive, and smokers are difficult to reach. Smokers are potentially reachable through the web, and if an automated chatbot could emulate an MI conversation, it could form the basis of a low-cost and scalable intervention motivating smokers to quit. OBJECTIVE The primary goal of this study is to design, train, and test an automated MI-based chatbot capable of eliciting reflection in a conversation with cigarette smokers. This study describes the process of collecting training data to improve the chatbot’s ability to generate MI-oriented responses, particularly reflections and summary statements. The secondary goal of this study is to observe the effects on participants through voluntary feedback given after completing a conversation with the chatbot. METHODS An interdisciplinary collaboration between an MI expert and experts in computer engineering and natural language processing (NLP) co-designed the conversation and algorithms underlying the chatbot. A sample of 121 adult cigarette smokers in 11 successive groups were recruited from a web-based platform for a single-arm prospective iterative design study. The chatbot was designed to stimulate reflections on the pros and cons of smoking using MI’s running head start technique. Participants were also asked to confirm the chatbot’s classification of their free-form responses to measure the classification accuracy of the underlying NLP models. Each group provided responses that were used to train the chatbot for the next group. RESULTS A total of 6568 responses from 121 participants in 11 successive groups over 14 weeks were received. From these responses, we were able to isolate 21 unique reasons for and against smoking and the relative frequency of each. The gradual collection of responses as inputs and smoking reasons as labels over the 11 iterations improved the F1 score of the classification within the chatbot from 0.63 in the first group to 0.82 in the final group. The mean time spent by each participant interacting with the chatbot was 21.3 (SD 14.0) min (minimum 6.4 and maximum 89.2). We also found that 34.7% (42/121) of participants enjoyed the interaction with the chatbot, and 8.3% (10/121) of participants noted explicit smoking cessation benefits from the conversation in voluntary feedback that did not solicit this explicitly. CONCLUSIONS Recruiting ambivalent smokers through the web is a viable method to train a chatbot to increase accuracy in reflection and summary statements, the building blocks of MI. A new set of 21 <i>smoking reasons</i> (both for and against) has been identified. Initial feedback from smokers on the experience shows promise toward using it in an intervention.

scholarly journals Engaging Unmotivated Smokers to Move Toward Quitting: Design of Motivational Interviewing–Based Chatbot Through Iterative Interactions

10.2196/20251 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e20251
Author(s):  
Fahad Almusharraf ◽  
Jonathan Rose ◽  
Peter Selby
Keyword(s):  
Motivational Interviewing ◽  
Language Processing ◽  
Interdisciplinary Collaboration ◽  
Low Cost ◽  
Building Blocks ◽  
Training Data ◽  
Free Form ◽  
Computer Engineering ◽  
Cigarette Smokers ◽  
The Web

Background At any given time, most smokers in a population are ambivalent with no motivation to quit. Motivational interviewing (MI) is an evidence-based technique that aims to elicit change in ambivalent smokers. MI practitioners are scarce and expensive, and smokers are difficult to reach. Smokers are potentially reachable through the web, and if an automated chatbot could emulate an MI conversation, it could form the basis of a low-cost and scalable intervention motivating smokers to quit. Objective The primary goal of this study is to design, train, and test an automated MI-based chatbot capable of eliciting reflection in a conversation with cigarette smokers. This study describes the process of collecting training data to improve the chatbot’s ability to generate MI-oriented responses, particularly reflections and summary statements. The secondary goal of this study is to observe the effects on participants through voluntary feedback given after completing a conversation with the chatbot. Methods An interdisciplinary collaboration between an MI expert and experts in computer engineering and natural language processing (NLP) co-designed the conversation and algorithms underlying the chatbot. A sample of 121 adult cigarette smokers in 11 successive groups were recruited from a web-based platform for a single-arm prospective iterative design study. The chatbot was designed to stimulate reflections on the pros and cons of smoking using MI’s running head start technique. Participants were also asked to confirm the chatbot’s classification of their free-form responses to measure the classification accuracy of the underlying NLP models. Each group provided responses that were used to train the chatbot for the next group. Results A total of 6568 responses from 121 participants in 11 successive groups over 14 weeks were received. From these responses, we were able to isolate 21 unique reasons for and against smoking and the relative frequency of each. The gradual collection of responses as inputs and smoking reasons as labels over the 11 iterations improved the F1 score of the classification within the chatbot from 0.63 in the first group to 0.82 in the final group. The mean time spent by each participant interacting with the chatbot was 21.3 (SD 14.0) min (minimum 6.4 and maximum 89.2). We also found that 34.7% (42/121) of participants enjoyed the interaction with the chatbot, and 8.3% (10/121) of participants noted explicit smoking cessation benefits from the conversation in voluntary feedback that did not solicit this explicitly. Conclusions Recruiting ambivalent smokers through the web is a viable method to train a chatbot to increase accuracy in reflection and summary statements, the building blocks of MI. A new set of 21 smoking reasons (both for and against) has been identified. Initial feedback from smokers on the experience shows promise toward using it in an intervention.

scholarly journals Conversational Agents: Goals, Technologies, Vision and Challenges

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8448
Author(s):  
Merav Allouch ◽  
Amos Azaria ◽  
Rina Azoulay
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Building Blocks ◽  
Future Research ◽  
Conversational Agents ◽  
Daily Routines ◽  
Security Issues ◽  
Current State ◽  
Technological Developments ◽  
Emotional Aspects

In recent years, conversational agents (CAs) have become ubiquitous and are a presence in our daily routines. It seems that the technology has finally ripened to advance the use of CAs in various domains, including commercial, healthcare, educational, political, industrial, and personal domains. In this study, the main areas in which CAs are successful are described along with the main technologies that enable the creation of CAs. Capable of conducting ongoing communication with humans, CAs are encountered in natural-language processing, deep learning, and technologies that integrate emotional aspects. The technologies used for the evaluation of CAs and publicly available datasets are outlined. In addition, several areas for future research are identified to address moral and security issues, given the current state of CA-related technological developments. The uniqueness of our review is that an overview of the concepts and building blocks of CAs is provided, and CAs are categorized according to their abilities and main application domains. In addition, the primary tools and datasets that may be useful for the development and evaluation of CAs of different categories are described. Finally, some thoughts and directions for future research are provided, and domains that may benefit from conversational agents are introduced.

scholarly journals A corpus-driven analysis of structural types of lexical bundles in court judgments in English and their translation into Lithuanian

Kalbotyra ◽  
2018 ◽  
Vol 70 (70) ◽  
pp. 7
Author(s):  
Donata Berūkštienė
Keyword(s):  
Corpus Linguistics ◽  
English Language ◽  
Legal Discourse ◽  
Building Blocks ◽  
The European Union ◽  
English Court ◽  
Lexical Bundles ◽  
Structural Types ◽  
Legal Texts ◽  
Methodological Guidelines

Formulaicity is one of the characteristic features of legal discourse, which manifests itself not only at the level of wording, “but also in the content, structure and layout” of legal texts (Ruusila & Londroos 2016, 123). Formulaic language, which includes phrasal and prepositional verbs, idioms, collocations, lexico-grammatical associations, lexical bundles, etc., are building blocks of legal discourse shaping legal text meanings. However, up to now, far too little attention has been paid to the nature of frequently occurring “sequences of three or more words that show a statistical tendency to co-occur” (Biber & Conrad 1999, 183), i.e. lexical bundles, in different genres of legal texts. Most studies in the field of lexical bundles in legal texts have only been based on one language (e.g. Jablonkai 2009; Goźdź-Roszkowski 2011; Breeze 2013), whereas translation-oriented contrastive studies on lexical bundles are lacking. In respect of the aforementioned gaps, the aim of this pilot study is to analyse structural types of lexical bundles in court judgments of the Court of Justice of the European Union in English and to examine the way these structures are rendered into Lithuanian. To gain insights into the frequency and structure of lexical bundles, the present study uses the methodological guidelines of corpus linguistics. The classification of lexical bundles into structural types is based on the framework suggested by Biber et al. (1999, 2004). For the purpose of this study, a parallel corpus of court judgments was compiled comprising approximately 1 million words of original court judgments in the English language and about 8 hundred thousand words of court judgments translated into Lithuanian. Lexical bundles in this research were identified using the corpus analysis toolkit AntConc 3.4.4 (Anthony 2015). A concordance program AntPConc 1.2.0 (Anthony 2017) was employed to find Lithuanian equivalents of the most frequent lexical bundles identified in the English court judgments. The evidence from this study suggests that different structural types of lexical bundles have more or less regular equivalents in Lithuanian; however, in most cases, these equivalents tend to be shorter.

Alternative Ways for Knowledge Collection, Indexing and Robust Language Retrieval

1998 ◽  
Vol 37 (04/05) ◽  
pp. 315-326 ◽  
Author(s):  
C. Lovis ◽  
A.-M. Rassinoux ◽  
J.-R. Scherrer ◽  
R. H. Baud
Keyword(s):  
Natural Language Processing ◽  
Knowledge Representation ◽  
Natural Language ◽  
Language Processing ◽  
Building Blocks ◽  
Linguistic Knowledge ◽  
Recent Experience ◽  
Medical Texts ◽  
Natural Components

AbstractDefinitions are provided of the key entities in knowledge representation for Natural Language Processing (NLP). Starting from the words, which are the natural components of any sentence, both the role of expressions and the decomposition of words into their parts are emphasized. This leads to the notion of concepts, which are either primitive or composite depending on the model where they are created. The problem of finding the most adequate degree of granularity for a concept is studied. From this reflection on basic Natural Language Processing components, four categories of linguistic knowledge are recognized, that are considered to be the building blocks of a Medical Linguistic Knowledge Base (MLKB). Following on the tracks of a recent experience in building a natural language-based patient encoding browser, a robust method for conceptual indexing and query of medical texts is presented with particular attention to the scheme of knowledge representation.

Managing mining project documentation using human language technology

2018 ◽  
Vol 36 (6) ◽  
pp. 993-1009
Author(s):  
Aleksandra Tomašević ◽  
Ranka Stanković ◽  
Miloš Utvić ◽  
Ivan Obradović ◽  
Božo Kolonja
Keyword(s):  
Language Processing ◽  
Keyword Search ◽  
Semantic Networks ◽  
Human Language ◽  
Language Resources ◽  
Content Type ◽  
Efficient Management ◽  
Language Technology ◽  
Mining Projects ◽  
Human Language Technology

Purpose This paper aims to develop a system, which would enable efficient management and exploitation of documentation in electronic form, related to mining projects, with information retrieval and information extraction (IE) features, using various language resources and natural language processing. Design/methodology/approach The system is designed to integrate textual, lexical, semantic and terminological resources, enabling advanced document search and extraction of information. These resources are integrated with a set of Web services and applications, for different user profiles and use-cases. Findings The use of the system is illustrated by examples demonstrating keyword search supported by Web query expansion services, search based on regular expressions, corpus search based on local grammars, followed by extraction of information based on this search and finally, search with lexical masks using domain and semantic markers. Originality/value The presented system is the first software solution for implementation of human language technology in management of documentation from the mining engineering domain, but it is also applicable to other engineering and non-engineering domains. The system is independent of the type of alphabet (Cyrillic and Latin), which makes it applicable to other languages of the Balkan region related to Serbian, and its support for morphological dictionaries can be applied in most morphologically complex languages, such as Slavic languages. Significant search improvements and the efficiency of IE are based on semantic networks and terminology dictionaries, with the support of local grammars.

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