Developing the Creative Abilities and Competencies of Future Digital Professionals

Vasilieva, Elena

doi:10.3103/s0005105518050060

Cited by 13 publications

(12 citation statements)

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“…In recent decades, there has been growing evidence of the importance of EI competency in primary school (Karavasilis et al, 2010;O'Rourke et al, 2010;Kopecki and Katavic, 2011;O'Connor, 2012;Pangiras et al, 2012;Baumeister et al, 2014;Barmpouti et al, 2015;Ferres et al, 2018). The new competencies needed for academic achievement in primary school are no longer exclusively academic (Aslam, 2018;Howard and Cogswell, 2018;Vahabzadeh et al, 2018;Vasilieva, 2018). Other types of skills such as EI and everything that EI implies (e.g., selfesteem, self-realization, emotional self-consciousness, positive relationships, empathy, and social responsibility) are also factors in academic achievement.…”

Section: Discussionmentioning

confidence: 99%

Can Academic Achievement in Primary School Students Be Improved Through Teacher Training on Emotional Intelligence as a Key Academic Competency?

Pozo-Rico

Sandoval

2020

Front. Psychol.

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Background: The primary aim of the current study was to develop a multimethodological teacher training program based on emotional intelligence (EI) as a key competency in order to improve student academic achievement based on two methods: face-to-face instruction and game-based e-learning instruction. Methods: Seventy-four primary education teachers and their 2069 students were randomly assigned to three groups. The first group of teachers (n = 23) were trained to use a face-to-face method. The objective of the training was that the teachers would be able to implement EI into their teaching to improve academic achievement in their students (n = 645) using face-to-face instruction. For the second group (n = 28), the teachers were trained to use an e-learning gamification method. Similar to the first group, the objective of the training was that the teachers would be able to implement EI into their teaching to improve academic achievement in their students (n = 758) using e-learning gamification instruction. The third group of teachers (n = 23) served as the controls and did not receive any special training, nor did they implement EI into their teaching (n = 666). Results: Implementation of EI into classroom teaching effectively improved academic achievement in primary school students using both methods. However, there was a greater increase in academic achievement and higher teacher satisfaction in the game-based e-learning group. No significant differences in student achievement were observed in the control group. Conclusion: Emotional intelligence as a key academic competency.

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Section: Discussionmentioning

confidence: 99%

Can Academic Achievement in Primary School Students Be Improved Through Teacher Training on Emotional Intelligence as a Key Academic Competency?

Pozo-Rico

Sandoval

2020

Front. Psychol.

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“…As práticas de gestão de pessoas estão viabilizando a ação criativa e intencional? Objetivo: analisar as práticas de gestão de pessoas (GP) associadas às ações criativa e intencional Contexto da pesquisa: Três instituições financeiras brasileiras com ações na B3 que possuem os maiores índices de felicidade no trabalho Processo de Coleta dos Dados • Etapa 1: Obter panorama dos estudos sobre gestão de pessoas atualmente e ação criativa Realização de levantamento bibliográfico pela base da Scopus e Portal Periódicos CAPES • Etapa 2: Acessar os documentos Realização de download dos relatórios administrativos e notas explicativas das instituições Processo de Análise dos Dados • Etapa 1: Obter panorama dos estudos sobre gestão de pessoas atualmente e ação criativa Suporte para estabelecimento das categorias analíticas • Etapa 2: Acessar os documentos Codificação dos dados e agrupamento nas categorias para posterior discussão à luz das teorias, norteando-se pela técnica de análise de conteúdo (Bardin, 2011;Krippendorff, 2018;Merriam, 2009) Reflexões e Conclusões (Conto et al 2016;Picchi 2019;Vasilieva, 2018). Como consta no relatório do Itaú Unibanco Holding S.A. (2019, p. 12): "em um mundo no qual as relações estão cada vez mais digitais, o desenvolvimento de produtos e serviços deve ser feito pensando no perfil dos clientes, inclusive nos tradicionais".…”

Section: Tabela 1 Categorias Analíticasunclassified

“…as pessoas. Em um contexto em que a inteligência artificial tem se mostrado uma ameaça ao modo de trabalhar, dentre os desafios vividos pelos líderes começa a surgir a necessidade de explorar aquilo que não pode ser feito pelas máquinas (International Labour Organization, 2019;Vasilieva, 2018). Segundo Picchi (2019), esta realidade demanda um perfil diferente de trabalhador e o desenvolvimento de habilidades que permitam a atuação no cenário corrente, o que também modifica os planejamentos de carreira desses profissionais.…”

Section: Introductionunclassified

Subsídios à ação criativa e intencional em instituições financeiras brasileiras

Firmino¹,

Vasconcelos

2021

R.G.Secr.,GESEC

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O objetivo deste artigo foi analisar as práticas de gestão de pessoas (GP) associadas às ações criativa e intencional. Para esta investigação, utilizaram-se os relatórios administrativos e as notas explicativas do Itaú Unibanco S.A., Banco Bradesco S.A. e Banco do Brasil S.A, instituições financeiras com ações na bolsa de valores brasileira (B3) possuidoras dos maiores índices de felicidade no trabalho. Como resultados, destaca-se o compromisso das organizações com as adaptações necessárias ao contexto competitivo atual marcado pelos avanços tecnológicos, o que configurou uma remodelagem das práticas de GP visando o desenvolvimento das potencialidades humanas. Percebeu-se também que estas iniciativas dão suporte às ações criativas e intencionais das pessoas. Este trabalho contribui principalmente por pesquisar os vínculos entre as práticas de gestão de pessoas e os facilitadores de ações criativas e intencionais, evidenciando como um subsidia o desenvolvimento do outro.

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“…From the end-user perspective, these challenges may mean that MMs may not be user-friendly or approachable enough to guarantee use and acceptance. Some have suggested that such problems are rooted in communication and training as opposed to user-friendliness (Cartwright et al, 2016), and protocols to improve the user experience, such as customer journey mapping, are recommended (Vasilieva, 2018). Tedeschi (2019) reflected that innovation in MM for ruminant nutrition had gone stagnant since 2010 and proposed several reasons why, including (1) the field had reached a certain level of maturity or that (2) students are not being properly introduced to the required 'systems thinking' approach.…”

Section: Approach Limitations: Mechanistic Modelsmentioning

confidence: 99%

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

Ellis

Jacobs²,

Dijkstra

et al. 2020

Animal

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Mechanistic models (MMs) have served as causal pathway analysis and ‘decision-support’ tools within animal production systems for decades. Such models quantitatively define how a biological system works based on causal relationships and use that cumulative biological knowledge to generate predictions and recommendations (in practice) and generate/evaluate hypotheses (in research). Their limitations revolve around obtaining sufficiently accurate inputs, user training and accuracy/precision of predictions on-farm. The new wave in digitalization technologies may negate some of these challenges. New data-driven (DD) modelling methods such as machine learning (ML) and deep learning (DL) examine patterns in data to produce accurate predictions (forecasting, classification of animals, etc.). The deluge of sensor data and new self-learning modelling techniques may address some of the limitations of traditional MM approaches – access to input data (e.g. sensors) and on-farm calibration. However, most of these new methods lack transparency in the reasoning behind predictions, in contrast to MM that have historically been used to translate knowledge into wisdom. The objective of this paper is to propose means to hybridize these two seemingly divergent methodologies to advance the models we use in animal production systems and support movement towards truly knowledge-based precision agriculture. In order to identify potential niches for models in animal production of the future, a cross-species (dairy, swine and poultry) examination of the current state of the art in MM and new DD methodologies (ML, DL analytics) is undertaken. We hypothesize that there are several ways via which synergy may be achieved to advance both our predictive capabilities and system understanding, being: (1) building and utilizing data streams (e.g. intake, rumination behaviour, rumen sensors, activity sensors, environmental sensors, cameras and near IR) to apply MM in real-time and/or with new resolution and capabilities; (2) hybridization of MM and DD approaches where, for example, a ML framework is augmented by MM-generated parameters or predicted outcomes and (3) hybridization of the MM and DD approaches, where biological bounds are placed on parameters within a MM framework, and the DD system parameterizes the MM for individual animals, farms or other such clusters of data. As animal systems modellers, we should expand our toolbox to explore new DD approaches and big data to find opportunities to increase understanding of biological systems, find new patterns in data and move the field towards intelligent, knowledge-based precision agriculture systems.

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Developing the Creative Abilities and Competencies of Future Digital Professionals

Cited by 13 publications

References 1 publication

Can Academic Achievement in Primary School Students Be Improved Through Teacher Training on Emotional Intelligence as a Key Academic Competency?

Can Academic Achievement in Primary School Students Be Improved Through Teacher Training on Emotional Intelligence as a Key Academic Competency?

Subsídios à ação criativa e intencional em instituições financeiras brasileiras

Review: Synergy between mechanistic modelling and data-driven models for modern animal production systems in the era of big data

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