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


Transdisciplinary Communication as a Meta-Framework of Digital Education
Rusudan Makhachashvili, Ivan Semenist
(pages: 1-6)

Multidisciplinary Learning Using Online Networking in Biomedical Engineering
Shigehiro Hashimoto
(pages: 7-12)

Augmented Intelligence for Advancing Healthcare
Mohammad Ilyas
(pages: 13-19)

A Transdisciplinary Approach to Refereeal
Russell Jay Hendel
(pages: 20-25)

The Impact of Convictions on Interlocking Systems
Teresa Henkle Langness
(pages: 26-33)

Collaborative Convergence: Finding the Language for Trans-Disciplinary Communication to Occur
Cristo Leon, James Lipuma
(pages: 34-37)

Bridging the Gap Between the World of Education and the World of Business via Standards to Develop Competences of the Future at Universities
Paweł Poszytek
(pages: 38-42)

Multidisciplinary Learning for Multifaceted Thinking in Globalized Society
Shigehiro Hashimoto
(pages: 43-48)

From Spirituality to Technontology in Education
Florent Pasquier
(pages: 49-52)

Differentiated Learning and Digital Game Based Learning: The KIDEDU Project
Eleni Tsami
(pages: 53-57)

Emerging Role of Artificial Intelligence
Mohammad Ilyas
(pages: 58-65)

Practicing Transdisciplinarity and Trans-Domain Approaches in Education: Theory of and Communication in Values and Knowledge Education (VaKE)
Jean-Luc Patry
(pages: 66-71)

Reflexive Practice for Inter and Trans Disciplinary Research in the Third Millennium
Maria Grazia Albanesi
(pages: 72-76)


 

Abstracts

 


ABSTRACT


Predicting Performance of Students in a Flipped Classroom Using Machine Learning: Towards Automated Data-Driven Formative Feedback

Jalal Nouri, Mohammed Saqr, Uno Fors


Learning analytics (LA) is a relatively new research discipline that uses data to try to improve learning, optimizing the learning process and develop the environment in which learning occurs. One of the objectives of LA is to monitor students activities and early predict performance to improve retention, offer personalized feedback and facilitate the provision of support to the students. Flipped classroom is one of the pedagogical methods that find strength in the combination of physical and digital environments i.e. blended learning environments. Flipped classroom often make use of learning management systems in which video-recorded lectures and digital material is made available, which thus generates data about students interactions with these materials. In this paper, we report on a study conducted with focus on a flipped learning course in research methodology. Based on data regarding how students interact with course material (video recorded lectures and reading material), how they interact with teachers and other peers in discussion forums, and how they perform on a digital assessment (digital quiz), we apply machine learning methods (i.e. Neural Networks, Nave Bayes, Random Forest, kNN, and Logistic regression) in order to predict students overall performance on the course. The final predictive model that we present in this paper could with fairly high accuracy predict low- and high achievers in the course based on activity and early assessment data. Using this approach, we are given opportunities to develop learning management systems that provide automatic datadriven formative feedback that can help students to selfregulate as well as inform teachers where and how to intervene and scaffold students.

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