Minitrack Introduction: Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response

Proceedings of the 54th Hawaii International Conference on System Sciences 2021The article of record as published may be found at http://http://hdl.handle.net/10125/70746Defense and emergency first responders must make rapid, consequential decisions and machine learning can aid analytics to support these decisions. Machine learning offers enormous promise, yet well publicized struggles reveal the need for better datasets and for opportunities to learn in challenging settings. Field experimentation offers the potential to meet these needs through iterative interactions in complex scenarios. Field experimentation can provide live action to facilitate high fidelity datasets that can support machine learning and artificial/augmented intelligence applications. These experiments may incorporate participants from academia; government agencies; militaries; first responders at all levels; and global industry partners. This minitrack explores the interplay between machine learning, field experimentation, and optimization analytics, whether exploratory, theoretical, experimental, in such critical areas as Defense and Emergency Response

Learning Education: An ‘Educational Big Data’ approach for monitoring, steering and assessment of the process of continuous improvement of education

Changing regulations, pedagogy and didactics worldwide, have ensured that the educational system has changed severely. But the entrance of Web 2.0 and other technologies had a significant impact on the way we educate and assess our education too. The Web 2.0 applications also increase the cooperation between stakeholders in education and has led to the phenomenon ‘Learning Education’. Learning Education is a term we use for the phenomenon where educational stakeholders (i.e. teachers, students, policy-makers, partners etc.) can learn from each other in order to ultimately improve education. The developments within the Interactive Internet (Web 2.0) enabled the development of innovative and sophisticated strategies for monitoring, steering and assessing the ‘learning of education’. These developments give teachers possibilities to enhance their education with digital applications, but also to monitor, steer and assess their own behavior. This process can be done with multiple sources, for example questionnaires, interviews, panel research, but also the more innovative sources like big social data and network interactions. In this article we use the term ‘educational big data’ for these sources and use it for monitoring, steering and assessing the developments within education, according to the Plan, Do, Check, Act principle (PDCA). We specifically analyze the Check-phase and describe it with the Learning Education Check Framework (LECF). We operationalize the LECF with a Learning Education Check System (LECS), which is capable to guide itself and change those directions as well in response to changing ways and trends in education and their practices. The system supports the data-driven decision making process within the learning education processes. So, in this article we work on the LECF and propose and describe a paper-based concept of the – by educational big data driven – LECS. Besides that, we show the possibilities, reliability and validity for measuring the ‘Educational Big Data’ within an educational setting

Learning within Digital Media: Investigating the Relationships Between Student Citation Networks, Assignment Structures, and Learning Outcomes

Students are comfortable sharing digital content with others, yet the effect of sharing of digital media for learning remains largely unexplored. Building on research in social network analysis and learning analytics, this research explores the use and sharing of digital media in learning activities, analyzing the effects of the design of the learning activities on the resulting networks of students and their cited resources, and exploring relationships between attributes of these citation networks and students’ perceptions of the learning outcomes. Results suggest that the extent to which an assignment is well-structured and converges towards a single solution positively influences the density and clustering coefficient of the resulting citation network, and that these network measures in turn have a positive influence on students’ perceptions of learning from the assignment

From knowledge co-creation to value co-creation and beyond: challenging global emergency in smart service systems.

The study seeks to investigate the impact of pandemic on teaching and learning processes involved in Higher Education (HE) by analysing the way in which knowledge exchange and value co-creation are reframed through ICTs and technology. The adoption of the interpretative lens of Service Science permits to reread HE as a smart service system. The empirical research, based on content analysis as an inquiry, analyses: 1) the transformations introduced in technology adoption, information sharing, knowledge and value co-creation to comply with the disruption “imposed” by the the sanitary emergency; 2) the way in which this transformation can introduce novelties in Higher education system. The results identify the different drivers for value and knowledge co-creation that can be implemented in technology-enhanced teaching and learning and the different novelties that can be generated from the emergence of innovation

Rebuilding Evolution: A Service Science Perspective

This paper explores a simple idea and asks a simple question: What determines the speed limit of evolutionary processes, and might there be ways to speed up those processes for certain types of systems under certain conditions? Or even more simply, how rapidly can complex systems be rebuilt? To begin with, the universe can be viewed as an evolving ecology of entities. Entities correspond to types of systems - from atoms in stars to organisms on Earth to ideas in the heads of people. Service science is the study of the evolving ecology of service system entities, complex socio-technical systems with rights and responsibilities – such as people, businesses, and nations. We can only scratch the surface in this paper, but our explorations suggest this is an important research question and direction, especially as we enter the cognitive era of smart and wise service systems. For example, it takes a child multiple years of experience to learn language and basic social interactions skills, but could machine learning algorithms with the proper data sets learn those capabilities in a fraction of the time

Building Dynamic Service Analytics Capabilities for the Digital Marketplace

Service firms are now interacting with customers through a multitude of channels or touchpoints. This progression into the digital realm is leading to an explosion of data, and warranting advanced analytic methods to manage service systems. Known as big data analytics, these methods harness insights to deliver, serve, and enhance the customer experience in the digital marketplace. Although global economies are becoming service-oriented, little attention is paid to the role of analytics in service systems. As such, drawing on a systematic literature review and thematic analysis of 30 in-depth interviews, this study aims to understand the nature of service analytics to identify its capability dimensions. Integrating the diverse areas of research on service systems, big data and dynamic capability theories, we propose a dynamic service analytics capabilities (DSAC) framework consisting of management, technology, talent, data governance, model development, and service innovation capability. We also propose a future research agenda to advance DSAC research for the emerging service systems in the digital marketplace

Data Mining in Learning Analytics

The goal of the Master's thesis was to perform the Data Mining part of a project "Learning Analytics for Secondary Schools" developed in inLab at Technical University of Catolonia

ICT Support for Refugees and Undocumented Immigrants

Immigrant integration has become a primary political concern for leaders in Germany and the United States. The information systems (IS) community has begun to research how information and communications technologies can assist immigrants and refugees, such as by examining how countries can facilitate social-inclusion processes. Migrants face the challenge of joining closed communities that cannot integrate or fear doing so. We conducted a panel discussion at the 2019 Americas Conference on Information Systems (AMCIS) in Cancun, Mexico, to introduce multiple viewpoints on immigration. In particular, the panel discussed how technology can both support and prevent immigrants from succeeding in their quest. We conducted the panel to stimulate a thoughtful and dynamic discussion on best practices and recommendations to enhance the discipline’s impact on alleviating the challenges that occur for immigrants in their host countries. In this panel report, we introduce the topic of using ICT to help immigrants integrate and identify differences between North/Central America and Europe. We also discuss how immigrants (particularly refugees) use ICT to connect with others, feel that they belong, and maintain their identity. We also uncover the dark and bright sides of how governments use ICT to deter illegal immigration. Finally, we present recommendations for researchers and practitioners on how to best use ICT to assist with immigration

Holding Space for Voices that Do Not Speak: Design Reform of Rating Systems for Platforms in GREAT Economies

Researchers can examine ethical implications of online rating systems to understand how they function as ‘knowledge instruments’ and affect social relations and networks connected with them. Research should address the fact that the underlying economic structures that design and deploy knowledge producing ‘technical objects’ on online platforms are not egalitarian and may create new circles of exclusion. Exploring implications of this for a starkly unequal country like India, we illustrate our ideas by integrating induction and abduction to study rating systems on a pan-India food discovery and delivery platform. Rating systems are borrowed from WEIRD contexts and our findings imply that the instrument studied here is designed to hear only some of many voices. Consequently, they might be ‘institutionalizing’ knowledge that is problematic for GREAT domains in which they are imposed. We highlight the need for decolonization of research approaches for GREAT domains and critical research of technical knowledge objects