KI und Bildung
Emotionen und Neurowissenschaften
Emotionen und Bildung in e-Projekten
Soziale und Emotionale Kompetenz mit digitalen Projekten bilden
Publikationen zu KI, Big Data und Lernen
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Neuerscheinungen im Bereich (smartes ) Lernen und neue Technologien
The new book of Cambridge Scholars Publishing about education in the Big Data era with my contribution within, was published.
The introduction, table of contents and the summary description are available under this link:
Potenzielle Nutzer von Online-Portale
Inteligente Technologien können unterstützen die neuen Entdeckungen der Neurowissenschaften hinsichtlich der Emotionen in Lernprozess zu integrieren. Das ist ein wichitiger Schritt für die Verbesserung der Collaboration zwischen KI- und Bildungsforschung. Die Modellierung und Erforschung von Tools für Emotionserkennung , die Lernprozesse begleiten, ist eine zukünftige Aufgabe in der Bildungsbranche.
Relevanz der E-learning-Angebote
Relevanz der E-learning-Angebote
Technologien in Bildung haben hohe Konjunktur.
Viele Portale bieten Möglichkeiten das Lernverhalten der Schüler besser zu beobachten und kontrollieren, um die Qualität zu steigern.
Learning Analytics
Automatisierte Lernsysteme
Automatisierte Lernsysteme
Automatisierte Lernsysteme
Automatisierte Lernsysteme unterstützen den Lernprozess mit Möglichkeiten der vielfältigen Erfassung von Lerninhalten und Lernfeedback. Sie unterstützen die Kommunikation und die Bewertung zwischen Lehrenden und Lernenden und verbessern damit die Aussichten auf Steigerung der Motivation im Lernprozess.
Open-Source Plattformen (Collaboratives Lernen)
Open-Source Plattformen (Collaboratives Lernen)
Die automatisierte Erkennung und Integration von Emotionen in Lernanwendungen, basiert auf Künstlicher Intelligenz
Affective Computing
Affective Computing
Hadjiski, M., Kaltenborn, R. (2022). Big Data and Artificial Intelligence Based on Personalized Learning – Conformity with Whitehead’s Organismic Theory. In: Riffert, F. & Petrov, V. (2002) (eds.) Education and Learning in a World of Accelerated Knowledge Growth: Current Trends in Process Thought, Cambridge Scholars Publishing, 336 p. ISBN: 1-5275-8381-3, pp. 191-224 (part of “European Studies in Process Thought” Ed., vol. 9), available online at: https://www.cambridgescholars.com/product/978-1-5275-8381-8
Auszug aus der veröffentlichen Einführung der Herausgeber: https://www.cambridgescholars.com/resources/pdfs/978-1-5275-8381-8-sample.pdf
"Conceiving the Personal Learning (PL) approach as a system of systems, Mincho Hadjiski and Rossitza Kaltenborn undertake a comparison of Whitehead’s process-organismic philosophy and contemporary systems theory in their paper to elaborate on their value for the necessary education transformations in our digital (big data, AI) epoch. Their analysis reveals that Personal Learning shows a “close congruence with a number of basic Whiteheadian concepts”. As such, the authors argue that many of Whitehead’s concepts – after being adapted and expanded according to the needs of big data and artificial intelligence research – can be used successfully for developing advanced personal learning systems that pay special attention to multi-modality in personalized learning."
Abstract:
The study shows the existence of a broad conformity between Whitehead’s organismic cosmology and the contemporary theory of complex systems at a relevant level of abstraction. One of the most promising directions of educational transformation in the age of big data and artificial intelligence – personalized learning – is conceived as a system of systems and reveals its close congruence with a number of basic Whiteheadian concepts. A new functional structure of personalized learning systems is proposed, including all the core elements of a full learning sequence. A multiobjective optimization problem, which is subject to strong constraints, uncertain outcomes, and continued development, is under consideration. It is argued that many of Whitehead’s concepts can be used constructively in designing and implementing advanced personalized learning systems after being adapted and expanded and account for the requirements of emerging big data and artificial intelligence research.
Special attention is paid to the main factors that determine the multi-modality of personalized learning – learning styles, contexts, and didactic variability. The effecttiveness of personalized learning is a data-driven problem.
Keywords: Artificial intelligence; big data; learning style; personalized learning; system theory; process philosophy; Whitehead
Publikation im Band:
Kaltenborn, R., Hadjiski, M., Koynov, S. (2022). Stimuli-Based Control of Negative Emotions in a Digital Learning Environment. In: Sgurev, V., Jotsov, V., Kacprzyk, J. (eds) Advances in Intelligent Systems Research and Innovation. Studies in Systems, Decision and Control, vol 379. Springer, Cham. https://doi.org/10.1007/978-3-030-78124-8_18
Abstract:
The proposed system for coping negative emotions arising during the learning process is considered as an embedded part of the complex intelligent learning system realized in a digital environment. By applying data-driven procedures on the current and retrospective data the main didactic-based stimuli provoking emotion generation are identified. They are examined as dominant negative emotions in the context of learning. Due to the presence of strong internal and output interconnections between teaching and emotional states, an intelligent decoupling multidimensional control scheme is accepted to overcome the lack of sample effective independent control actions that separately affect the states of learning and emotions. To avoid existing drawbacks in emotion-focused partial control, an approach with integrating emotions with a low-dimensional representation is accepted. Two-stage procedure is proposed to compensate the negative impact of emotions on the learning process.
Keywords: Data, Emotion, Intelligent control, Learning, Stimulus
References: https://link.springer.com/chapter/10.1007/978-3-030-78124-8_18
Kaltenborn, Rossitza (2021). Integration of learning and neuroscience theories with AI-based technologies in intelligent learning system in accordance of Whiteheadean tradition and contemporary process theory, in: Balkan Journal of Philosophy, ISSN 1313 – 888X, eISSN 2367-5438 (Online), Vol. 13, Issue 2, 2021, – Scopus (SJR 0.120), Quartile: Q3, online available at: https://www.pdcnet.org/bjp/content/bjp_2021_0013_0002_0161_0174
Abstract:
The purpose of the article is to present the possibility of integrating basic learning theories into the Extended Intelligent Learning System for data processing, optimization, adaptation and decision making in learning, which is based on the combination of teacher and intelligent tutor with artificial intelligence implemented, which supports the target formation, learning strategy, pedagogy and control. As a framework for the creation of the integrative model of theories, process philosophy is used, which enables a better understanding and explanation of the different paradigms and their functional combination. The article explores the strengths and weaknesses of selected theories and focuses primarily on constructivism, as numerous studies on learning theories have found that a constructive approach to learning is at the heart of many models in both traditional and digital learning in the Era of Big Data. The article explores certain influential learning theories, including the AI methods, their advantages, flaws and fields of intersection with neurosciences in terms of their application in intelligent training systems. The goal of developing the integrative model is to realize the learner's potential in personalized knowledge formation in an intelligent learning environment and to enhance the efficiency of learning.
Kaltenborn, Rossitza (2019). “Embedding the assessment of emotion in the learning process with AI-driven technologies”, in: Petrov, Vesselin and Katie Andersen (Eds. and preface). Traditional Learning Theories, Process Philosophy and AI. Brussels: Chromatikon – 220 p., ISBN 978-2-930517-60-5, pp. 145-167.
Buchbeschreibung der Herausgeber:
Artificial intelligence research connected with learning theory ("deep learning," “machine learning,” analysis of the quality of learning, etc.) has existed for many years; however, there have been few investigations in that area conducted from a robust philosophical methodological basis.
This book provides such a basis with the help of Whitehead's cyclic learning theory and its process ontology, making it possible to integrate the dominant learning theories of our time. It is the outcome of a project sponsored by the Bulgarian National Science Fund.
Abstract:
This paper examines the possibility of an objective evaluation of emotions occurring within the learning process and methods for embedding such an evaluation in advanced learning systems. The main conceptual understandings of emotion in learning and teaching are systematized, with an emphasis on the process philosophy approach. Different models of emotion are considered and the possible generalization of Whitehead’s approach to the role of emotion in education is examined. Special attention is given to significant developments in artificial intelligence in identifying the entire spectrum of emotions and their quantitative estimation as sensor-based variables in data-driven technology. This emotional identification is also explored with respect to data acquisition, processing and classification in computer-based systems for educational purposes. The correlation between emotion and performance outcome in learning is studied to inform an interdisciplinary approach which can improve the learning process. As a result, a complex system for emotion measurement and management is proposed. This can be of interest for the further development of intelligent autonomous tutors.
Keywords
Artificial intelligence, educational philosophy, emotion management, learning, reflection, Whitehead
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