Conceptualizing Hybrid Human-Machine Systems and Interaction

Buxbaum-Conradi, Sonja; Redlich, Tobias; Branding, Jan-Hauke

doi:10.1109/hicss.2016.75

Cited by 3 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Rich variety of interactivity types to aid data visualization  Interactivity improves visualizations (Heer and Shneiderman, 2012)  Detecting emotions possible (Hibbeln et al, 2017;Kratzwald et al, 2018;Kurzhals et al, 2015)  Augmented reality for businesses (Olshannikova et al, 2015;Porter and Heppelmann, 2017)  Hybrid human-machine systems (Buxbaum-Conradi et al, 2016) • Support for collaborative data visualization and exploration  Collaboration via interactive visual analytics (Isenberg et al, 2011)  Interactive visualization for distributed cognition (Liu et al, 2008) While the practical data science processes typically commence with data, then application of methods and interfaces, the overarching objective is enhanced cognitive understanding of a phenomenon. As such, the review commences with cognition as pivotal in the data science domain.…”

Section: Limitationsmentioning

confidence: 99%

Data science in organizations: Conceptualizing its breakthroughs and blind spots

Cybulski

Scheepers

2021

Journal of Information Technology

View full text Add to dashboard Cite

The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.

show abstract

Section: Limitationsmentioning

confidence: 99%

Data science in organizations: Conceptualizing its breakthroughs and blind spots

Cybulski

Scheepers

2021

Journal of Information Technology

View full text Add to dashboard Cite

show abstract

“…Beyond educational contexts, there is an on-going "fusion" between human and system control, for example in aviation systems and self-driving cars. A defining characteristic of hybrid human-systems is that the boundaries between human and system decision making fluctuate [13]. In line with this, embedded learning analytics techniques can support a new generation of SRL support that adjusts external regulation based on insights gained from data.…”

Section: New Forms Of Regulation Supportmentioning

confidence: 99%

Towards Hybrid Human-System Regulation

Molenaar

Horvers

Baker

2019

Proceedings of the 9th International Conference on Learning Analytics &Amp; Knowledge

View full text Add to dashboard Cite

This paper proposes a new approach to translate learner data into self-regulated learning support. Learning phases in blended classrooms place unique requirements on students' self-regulated learning (SRL). Learning path graphs merge moment-by-moment learning curves and learning phase data to understand student' SRL support needs. Results indicate 4 groups with different SRL support needs. Students in the self-regulated learning group are capable of learning without external regulation. In the teacher regulation group students need initial teacher regulation but rely on SRL thereafter. Students in the system regulation group require teacher and system regulation to learn. Finally, the advanced system support group is in need of support beyond the current level of system regulation. Based on these insights, the application of personalized dashboards and hybrid human-system regulation is further specified.

show abstract

Enhancing Self-Regulated Learning With Artificial Intelligence-Powered Learning Analytics

Yildirim-Erbasli,

Gorgun,

Bulut

2023

Advances in Early Childhood and K-12 Education

View full text Add to dashboard Cite

This chapter explores the dynamic synergy between artificial intelligence (AI) and learning analytics (LA) as catalysts for self-regulated learning (SRL). By examining the intersection of AI, LA, and SRL, this chapter sheds light on the evolving landscape of education and the opportunities it offers for tailored, data-driven learning experiences. It delves into the transformative potential of AI-powered LA, providing personalized insights, prediction, recommendation, and interactive scaffolding; thus promoting SRL development among learners. It also addresses ethical considerations, challenges, and the need for interdisciplinary collaboration among educators, data scientists, and policymakers. Ultimately, this chapter underscores the importance of responsible implementation and continuous research to harness the full benefits of AI-powered LA in supporting SRL and improving educational outcomes.

show abstract

Conceptualizing Hybrid Human-Machine Systems and Interaction

Cited by 3 publications

References 39 publications

Data science in organizations: Conceptualizing its breakthroughs and blind spots

Data science in organizations: Conceptualizing its breakthroughs and blind spots

Towards Hybrid Human-System Regulation

Enhancing Self-Regulated Learning With Artificial Intelligence-Powered Learning Analytics

Contact Info

Product

Resources

About