Journal of
Systemics, Cybernetics and Informatics

ISSN: 1690-4524 (Online)

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Editorial Advisory Board's Chair
William Lesso

Nagib C. Callaos

Sponsored by
The International Institute of
Informatics and Systemics

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No Warranty Express or Implied: Why Do We Have So Many Problems With the Computer Systems That Pervade Our Lives?
John W. Coffey
(pages: 1-6)

Can You Hear Me Now? An Innovative Approach to Assess and Build Connections with Online Learner’s
Tina M. Serafini, Risa Blair
(pages: 7-11)

End-to-end Security with Translation
Kevin E. Foltz
(pages: 12-17)

(Assistive) Technology at the Point of Instruction: Barriers and Possibilities
Lorayne Robertson
(pages: 18-24)

Supplementing Multiple Modalities and Universal Design in Learning with Goal-Setting
Russell Jay Hendel
(pages: 25-30)

Experts Informing Experts
Robert Hammond
(pages: 31-35)

Internet of Things – A New Epistemic Object
Rolf Dornberger, Terry Inglese, Safak Korkut
(pages: 36-44)

An Experiment in Interdisciplinary STEM Education: Insights from the Catholic Intellectual Tradition
Fr. Joseph R. Laracy, Thomas Marlowe, Fr. Gerald J. Buonopane
(pages: 45-53)

Big History Understanding of Complexity, Informatics and Cybernetics
John L. Motloch
(pages: 54-60)

Flourishing Organizations
Maria Jakubik
(pages: 61-72)

Pros & Cons of Smart ICT in Some Governmental Applications
Dusan Soltes
(pages: 73-75)

Information Exchange in Vehicles Ad-Hock Networks
Tomas Zelinka
(pages: 76-80)

Living in a Digital World: Improving Skills to Meet the Challenges of Digital Transformation Through Authentic and Game-Based Learning
Margit Scholl, Frauke Fuhrmann
(pages: 81-86)

Psychotherapy via the Internet as a Novel Tool for Clinical Use
Ulrich Sprick
(pages: 87-94)

Technology Intercepts for Cyber Security Applied to Critical Infrastructures
Mario La Manna
(pages: 95-100)

“And Then a Miracle Occurs …” – Engaging the Challenge of Operationalizing Theories of Success in Digital Transformation
Michael Von Kutzschenbach
(pages: 101-105)

Multidisciplinary Learning Extends Communication Skill, and Helps Cross Cultural Understandings: Biomedical Engineering
Shigehiro Hashimoto
(pages: 106-112)

Integrating Teaching, Research and Problem Solving: An Experience in Progress in the Mucuri Valley Region (Brazil)
Leônidas Conceição Barroso
(pages: 113-118)

Meeting Learning Challenges in Product Design Education with and through Additive Manufacturing
William Lavatelli Kempton, Steinar Killi, Andrew Morrison
(pages: 119-129)

Creating and Using Symbolic Mental Structures via Piaget’s Constructivism and Popper’s Three Worlds View with Falsifiability to Achieve Critical Thinking by Students in the Physical Sciences
Matthew E. Edwards
(pages: 130-134)

Creativity in Higher Education: Comparative Genetic Analyses on the Dopaminergic System in Relation to Creativity, Addiction, Schizophrenia in Humans and Non-Human Primates
Bernard Wallner, Sonja Windhager, Katrin Schaefer, Martin Fieder
(pages: 135-142)





Causal Bayes Model of Mathematical Competence in Kindergarten

Božidar Tepeš, Gordana Lešin, Ana Hrkac, Krunoslav Tepeš

In this paper authors define mathematical competences in the kindergarten. The basic objective was to measure the mathematical competences or mathematical knowledge, skills and abilities in mathematical education. Mathematical competences were grouped in the following areas: Arithmetic and Geometry. Statistical set consisted of 59 children, 65 to 85 months of age, from the Kindergarten Milan Sachs from Zagreb. The authors describe 13 variables for measuring mathematical competences. Five measuring variables were described for the geometry, and eight measuring variables for the arithmetic. Measuring variables are tasks which children solved with the evaluated results. By measuring mathematical competences the authors make causal Bayes model using free software Tetrad 5.2.1-3. Software makes many causal Bayes models and authors as experts chose the model of the mathematical competences in the kindergarten. Causal Bayes model describes five levels for mathematical competences. At the end of the modeling authors use Bayes estimator. In the results, authors describe by causal Bayes model of mathematical competences, causal effect mathematical competences or how intervention on some competences cause other competences. Authors measure mathematical competences with their expectation as random variables. When expectation of competences was greater, competences improved. Mathematical competences can be improved with intervention on causal competences. Levels of mathematical competences and the result of intervention on mathematical competences can help mathematical teachers.

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