A novel standard for graphical representation of mental models and processes in cognitive sciences

Cognitive Science has positioned itself to be a common ground in which models of mental processes from multiple disciplines merge, situating itself as a common field for new learning theories, or for formalizing existing ones. However, the authors have identified a need for updating the existing graphical representations by incorporating more accessible understanding for teachers and researchers in cross- multidisciplinary fields. In this regard, the present investigation attempts to generate a standard graphical language to represent complex mental processes by the introduction of functional principles, schemes and models that have been successfully used in technical areas such as adaptive control systems, algorithm flow charts, and artificial intelligence. This graphical representation, entitled “Cognitive Functional Representation” (CFR), is further shown to be efficacious in incorporating the essence of complex cognitive theories

Technology of mental functional representations as a first stage of conceptualization and implementation of complex scientific knowledge in innovation processes

In innovation processes, it is common to deal with highly cross-multidisciplinary topics. For example, an innovation process may integrate psychological, neuroscientific, biological and engineering disciplines, among many others dealing with bio-cybernetic systems. One specific type of those theories is related to cognitive processes, knowledge representation, and self-learning systems. Therefore, there is a need to easily and rapidly understand, as well as apply and share knowledge of complex theories by innovation managers, engineers, scholars, training practitioners, computational modelers, managers, and stakeholders, among others. In this regard, the present article provides with a graphical tool to represent complex cross- multidisciplinary theories, concepts and processes in a simple, concise, and logical manner, by using functional principles and graphical representations that have been successfully used in engineering and technology areas such as adaptive control systems, algorithmic flow charts, and computational cognitive neuroscience. Once described the models that have been typically used to represent and model knowledge and cognition, functional cognitive modeling is introduced, and then applied to represent and model complex cognitive theories from psychology and neuroscience such as Jean Piaget’s Theory of Intellectual Growth, Antonio Damasio’s Somatic Marker Hypothesis, and Dante Dorantes’ Soft Skills Model

Personalised E-Learning: The Assessment of Students Prior Knowledge in Higher Education

Society’s use of mobile applications that instantaneously dynamically adapt to input has had the effect of users expecting immediate feedback from all applications based on their specific needs. The traditional concept of a one size fits all approach to managing an online learning environment could perhaps be improved by the inclusion of personalised learning experiences for students based on their prior knowledge. The purpose of personalised e-learning is to tailor learning content to the specific learning requirements of individual students. The focus of this chapter is to review the topic of personalised e-learning and discuss the issues and problems educators may encounter in assessing students’ prior knowledge. Information on students’ prior knowledge is required to inform the process to facilitate personalised e-learning experiences based on prior knowledge

Goal-Driven Process Navigation for Individualized Learning Activities in Ubiquitous Networking and IoT Environments

Abstract: In the study, we propose an integrated adaptive framework to support and facilitate individualized learning through sharing the successful process of learning activities based on similar learning patterns in the ubiquitous learning environments empowered by Internet of Things (IoT). This framework is based on a dynamic Bayesian network that gradually adapts to a target student's needs and information access behaviours. By analysing the log data of learning activities and extracting students' learning patterns, our analysis results show that most of students often use their preferred learning patterns in their learning activities, and the learning achievement is affected by the learning process. Based on these findings, we try to optimise the process of learning activities using the extracted learning patterns, infer the learning goal of target students, and provide a goal-driven navigation of individualized learning process according to the similarity of the extracted learning patterns

Challenges Encountered in Creating Personalised Learning Activities to Suit Students Learning Preferences

This book chapter reviews some of the challenges encountered by educators in creating personalised e-learning activities to suit students learning preferences. Technology-enhanced learning (TEL) alternatively known as e-learning has not yet reached its full potential in higher education. There are still many potential uses as yet undiscovered and other discovered uses which are not yet realisable by many educators. TEL is still predominantly used for e-dissemination and e-administration. This chapter reviews the potential use of TEL to provide personalised learning activities to suit individual students learning preferences. In particular the challenges encountered by educators when trying to implement personalised learning activities based on individual students learning preferences

A Review of Personalised E-Learning: Towards Supporting Learner Diversity

The realisation of personalised e-learning to suit an individual learner’s diverse learning needs is a concept which has been explored for decades, at great expense, but is still not achievable by non-technical authors. This research reviews the area of personalised e-learning and notes some of the technological challenges which developers may encounter in creating authoring tools for personalised e-learning and some of the pedagogical challenges which authors may encounter when creating personalised e-learning activities to enhance the learning experience of their students. At present educators who wish to create personalised e-learning activities require the assistance of technical experts who are knowledgeable in the area. Even with the help of an expert the creation of personalised e-learning activities still remains a complex process to authors who are new to the concept of tailoring e-learning to suit learner diversity. Before the successful utilisation of adaptive authoring tools can be realised, academic authors need to learn how to effectively use these tools. All learners come to education with a diverse set of characteristics; educators need to decide which learner characteristic(s) they wish to focus on addressing through the use of personalised e-learning activities. Further investigation, evaluation and analyses of authoring tools is required before personalised e-learning to support learner diversity can be achieved by many academics. Research members of the AMAS (2013) project team are currently involved in developing an authoring tool for adaptive activities for e-learning

Design, Implementation, and Measurement of Personalized Learning Through the Lens of Universal Design for Learning

This dissertation project aims to explore the design, implementation, and measurement of personalized learning (PL) environments through the lens of Universal Design of Learning (UDL). The dissertation highlights utilizing UDL as an instructional framework to guide and inform improvement in the design of PL environments. The dissertation consists of five chapters. Chapter One introduces the project and key concepts associated with the topic. Chapter Two provides an analysis of historical and contemporary issues associated with educating diverse learners in standardized learning environments. The analysis sets the stage for the argument that there is a need for applying the framework of UDL to designing PL environments wherein learners can be empowered to become more self-determined. Chapter Three reports on a synthesis of PL research conducted from 2006 to 2019. In order to provide considerations for PL design that supports dynamic development of individual learners, this synthesis integrates the current empirical PL practices into the UDL framework. Chapter Four describes the development of a learner self-report instrument—the Learner-Centered Sensor, which can be used to measure students’ learning experiences in UDL-based environments. This chapter also reports on a pilot study designed to conceptually evaluate the tool by generating validity evidence based on its content structure. Chapter Five concludes the dissertation project by providing implications for future research on the design, implementation, and measurement of PL through the lens of UDL