Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


Smart Cities: Challenges and Opportunities
Mohammad Ilyas
(pages: 1-6)

Bridging the Gap: Communicating to Increase the Visibility and Impact of Your Academic Work
Erin Ryan
(pages: 7-12)

Cross-Cultural Online Networking Based on Biomedical Engineering to Motivate Transdisciplinary Communication Skills
Shigehiro Hashimoto
(pages: 13-17)

Interdisciplinary Approaches to Learning Informatics
Masaaki Kunigami
(pages: 18-22)

The Impact of Artificial Intelligence and the Importance of Transdisciplinary Research
R. Cherinka, J. Prezzama, P. O'Leary
(pages: 23-28)

Emotional Communication as Complex Phenomenon in Musical Interpretation – Proposal for a Systemic Model That Promotes a Transdisciplinary Process of Self-Formation and Reflection Around Expressiveness as a Lived Experience
Fuensanta Fernández de Velazco, Eduardo Carpinteyro-Lara, Saúl Rodríguez-Luna
(pages: 29-33)

A Multi-Disciplinary Cybernetic Approach to Pedagogic Excellence
Russell Jay Hendel
(pages: 34-41)

The Ethics of Artificial Intelligence in the Era of Generative AI
Vassilka D. Kirova, Cyril S. Ku, Joseph R. Laracy, Thomas J. Marlowe
(pages: 42-50)

Trans-Disciplinary Communication: Context and Semantics
Maurício Vieira Kritz
(pages: 51-57)

A Brave New World: AI as a Nascent Regime?
Jasmin Cowin, Birgit Oberer, Cristo Leon
(pages: 58-66)

The Role of Art and Science – Relational Dynamics in Human Ecology
Giorgio Pizziolo, Rita Micarelli
(pages: 67-75)

Advancing Entrepreneurship Education: An Integrated Approach to Empowering Future Innovators
Birgit Oberer, Alptekin Erkollar
(pages: 76-81)

Harmonizing Horizons: The Symphony of Human-Machine Collaboration in the Age of AI
Birgit Oberer, Alptekin Erkollar
(pages: 82-86)

How Do Students Learn Artificial Intelligence in Interdisciplinary Field of Biomedical Engineering?
Shigehiro Hashimoto
(pages: 87-91)

What is ChatGPT and its Present and Future for Artificial Intelligence in Trans-Disciplinary Communications?
Richard Segall
(pages: 92-98)


 

Abstracts

 


ABSTRACT


Dynamic Boundaries of Action Based Learning: the Longitudinal Impact (Invited Paper)

D. Song, S. Nousala, P. Aibeo


How do communities and group-based efforts create, learn and evolve? This paper argues that communities are dynamic, continuously creating connections through cyclical learning processes, regardless of how tight or loosely formulated group based efforts are (Hall et al. 2012).

Learning cycles or epicycles processes are relevant for action-based investigation within organizational and social structures. The question of behaviors across boundaries or groups maybe influenced by their positioning within a larger adaptive system, including the type of focus, determined goals and the type of connections that have been developed over time (longitudinally).

These types of community or group efforts can be described as autopoietic systems, which operate within larger adaptive societal webs (Nousala 2014). The learning methodologies involved in investigating these types of dynamic phenomena need themselves to be dynamic. These methods can be viewed through longitudinal cycles, (which are essentially feedback loops that include extensive reflective time lines, integration before repetition) exposing these epicycles at work. The continuous recording of various processes through epicycles (which are the basis for learning cycles) provide a means to “qualitatively measuring” change, which would normally go unseen (Hall et. al 2012; Hall et al. 2005; Nousala and Hall 2008; Wenger and Synder 2000; Garduno et al. 2015).

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