Maturity Models for Managing People in Software Development Teams: A Systematic Literature Review

Human factors are fundamental to software development, hence the need to understand people-oriented maturity models in development teams. This article aims to identify the maturity models for people management in software development teams cited in the literature, in order to identify evidence about their use, benefits and limitations, and the human aspects involved. A systematic literature review (SLR) was carried out, where automatic searches were done in four search engines, congresses in the area of ​​human factors within software engineering, in addition to a manual search. Evidence indicates that there are numerous models aimed at developing people in development teams, but few are applied. Models based on observation and informal discussion were found, as well as non-validated models, indicating the scarcity of models applicable to people in software engineering. However, complete but complex models were also found, indicating that a more transparent, dynamic and simple process is needed for people's development. In the observed human factors, emphasis is placed on communication, collaboration, knowledge, learning, self-management, motivation and skills in general. Keywords: Maturity models; Human Factors; Software Development Teams; Teams; People DOI: 10.7176/RHSS/12-14-02 Publication date:July 31st 202

Graduate Program Descriptions for 2006

Wright State University graduate program descriptions for the 2006 school year

Curriculum Subcommittee Agenda December 7, 2023

Program Proposals Semester Course Approval Reviews Other Business Adjour

Educational Policies Committee Agenda December 7, 2023

Subcommittee Reports Curriculum Subcommittee (Chad Simon) Academic Standards Subcommittee (Renee Galliher) General Education Subcommittee (Matt Sanders

Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals

The emerging of the fourth industrial revolution, also known as Industry 4.0 (I4.0), from the advancement in several technologies is viewed not only to promote economic growth, but also to enable a greener future. The 2030 Agenda of the United Nations for sustainable development sets out clear goals for the industry to foster the economy, while preserving social well-being and ecological validity. However, the influence of I4.0 technologies on the achievement of the Sustainable Development Goals (SDG) has not been conclusively or systematically investigated. By understanding the link between the I4.0 technologies and the SDGs, researchers can better support policymakers to consider the technological advancement in updating and harmonizing policies and strategies in different sectors (i.e., education, industry, and governmental) with the SDGs. To address this gap, academic experts in this paper have investigated the influence of I4.0 technologies on the sustainability targets identified by the UN. Key I4.0 element technologies have been classified to enable a quantitative mapping with the 17 SDGs. The results indicate that the majority of the I4.0 technologies can contribute positively to achieving the UN agenda. It was also found that the effects of the technologies on individual goals varies between direct and strong, and indirect and weak influences. The main insights and lessons learned from the mapping are provided to support future policy

Integrating FATE/critical data studies into data science curricula : where are we going and how do we get there?

There have been multiple calls for integrating topics related to fairness, accountability, transparency, ethics (FATE) and social justice into Data Science curricula, but little exploration of how this might work in practice. This paper presents the findings of a collaborative auto-ethnography (CAE) engaged in by a MSc Data Science teaching team based at University of Sheffield (UK) Information School where FATE/Critical Data Studies (CDS) topics have been a core part of the curriculum since 2015/16. In this paper, we adopt the CAE approach to reflect on our experiences of working at the intersection of disciplines, and our progress and future plans for integrating FATE/CDS into the curriculum. We identify a series of challenges for deeper FATE/CDS integration related to our own competencies and the wider socio-material context of Higher Education in the UK. We conclude with recommendations for ourselves and the wider FATE/CDS orientated Data Science community