A Cognitive Computational Knowledge Representation Theory

2011 ◽  
pp. 247-264
Author(s):  
Mehdi Najjar ◽  
André Mayers
Keyword(s):  
Knowledge Representation ◽  
Learning Environments ◽  
Computational Models ◽  
Cognitive Activity ◽  
Computational Modelling ◽  
Virtual Learning ◽  
Psychological Research ◽  
The Novel ◽  
Novel Approach ◽  
Positive Results

Encouraging results of last years in the field of knowledge representation within virtual learning environments confirms that artificial intelligence research in this topic find it very beneficial to integrate the knowledge psychological research have accumulated on understanding the cognitive mechanism of human learning and all the positive results obtained in computational modelling theories. This chapter introduces a novel cognitive and computational knowledge representation approach inspired by cognitive theories which explain the human cognitive activity in terms of memory subsystems and their processes, and whose aim is to suggest formal computational models of knowledge that offer efficient and expressive representation structures for virtual learning. Practical studies both contribute to validate the novel approach and permit to draw general conclusions.

A Cognitive Computational Knowledge Representation Theory

2012 ◽  
pp. 1819-1838
Author(s):  
Mehdi Najjar ◽  
André Mayers
Keyword(s):  
Knowledge Representation ◽  
Learning Environments ◽  
Computational Models ◽  
Cognitive Activity ◽  
Computational Modelling ◽  
Virtual Learning ◽  
Psychological Research ◽  
The Novel ◽  
Novel Approach ◽  
Positive Results

Encouraging results of last years in the field of knowledge representation within virtual learning environments confirms that artificial intelligence research in this topic find it very beneficial to integrate the knowledge psychological research have accumulated on understanding the cognitive mechanism of human learning and all the positive results obtained in computational modelling theories. This chapter introduces a novel cognitive and computational knowledge representation approach inspired by cognitive theories which explain the human cognitive activity in terms of memory subsystems and their processes, and whose aim is to suggest formal computational models of knowledge that offer efficient and expressive representation structures for virtual learning. Practical studies both contribute to validate the novel approach and permit to draw general conclusions.

A Data Mining Approach to Diagnosing Student Learning Problems in Sciences Courses

2008 ◽  
pp. 2928-2942
Author(s):  
Gwo-Jen Hwang
Keyword(s):  
Data Mining ◽  
Distance Learning ◽  
Learning Environments ◽  
Learning Process ◽  
Learning Problems ◽  
The Novel ◽  
Science Courses ◽  
Data Mining Approach ◽  
Novel Approach ◽  
Distance Learning Environment

In recent years, researchers have attempted to develop more effective distance education systems. Nevertheless, students in network-based learning environments may need additional guidance and assistance when they encounter problems in learning certain concepts. Therefore, it is important to provide learning guidance in a distance learning environment. In this paper, we propose a data mining approach that is capable of assisting teachers to provide information needed for guiding students during the learning process. Several experiments on science courses have shown the effectiveness of applying the novel approach.

Exploring normative perceptions and misperceptions of private, environmentally impactful behaviours

2020 ◽  
Author(s):  
Elaine Gallagher ◽  
Bas Verplanken ◽  
Ian Walker
Keyword(s):  
Social Norms ◽  
Behaviour Change ◽  
Social Contexts ◽  
The Novel ◽  
Normative Influence ◽  
Change Mechanism ◽  
Novel Approach ◽  
Classical Studies ◽  
Referent Group

Social norms have been shown to be an effective behaviour change mechanism across diverse behaviours, demonstrated from classical studies to more recent behaviour change research. Much of this research has focused on environmentally impactful actions. Social norms are typically utilised for behaviour change in social contexts, which facilitates the important element of the behaviour being visible to the referent group. This ensures that behaviours can be learned through observation and that deviations from the acceptable behaviour can be easily sanctioned or approved by the referent group. There has been little focus on how effective social norms are in private or non-social contexts, despite a multitude of environmentally impactful behaviours occurring in the home, for example. The current study took the novel approach to explore if private behaviours are important in the context of normative influence, and if the lack of a referent groups results in inaccurate normative perceptions and misguided behaviours. Findings demonstrated variance in normative perceptions of private behaviours, and that these misperceptions may influence behaviour. These behaviours are deemed to be more environmentally harmful, and respondents are less comfortable with these behaviours being visible to others, than non-private behaviours. The research reveals the importance of focusing on private behaviours, which have been largely overlooked in the normative influence literature.

Exploring Self-Regulation Based Virtual Learning Environments Using Diary

2015 ◽  
Vol 3 (2) ◽  
pp. 66-75
Author(s):  
Mahmoud Mohamed Hussien Ahmed ◽  
Chaklam Silpasuwanchai ◽  
Naglaa Mohammed Fares ◽  
Zeinab Mohamed Amin ◽  
Abd El-Rahem Ahmed Ahmed Salama
Keyword(s):  
Learning Environments ◽  
Self Regulation ◽  
Virtual Learning ◽  
Virtual Learning Environments

Developing information literacy courses for students through virtual learning environments in Tanzania: Prospects and challenges

IFLA Journal ◽  
2021 ◽  
pp. 034003522110182
Author(s):  
Evans F Wema
Keyword(s):  
Learning Environments ◽  
Information Literacy ◽  
Teaching And Learning ◽  
Virtual Learning ◽  
Higher Learning ◽  
Virtual Learning Environments ◽  
Web Based ◽  
Face To Face ◽  
Institutions Of Higher Learning ◽  
The Face

This article reviews literature on the use of virtual learning environments by highlighting their potential and the challenges of introducing the same in Tanzania. It introduces the concept of virtual learning environments by demonstrating their applications to support teaching and learning. The article discusses the use of virtual learning environments in teaching information literacy courses by highlighting the success of using such tools in facilitating the teaching of information literacy courses to library users. In this review, special emphasis is placed on attempts by Tanzanian institutions of higher learning to introduce web-based teaching of information literacy and the challenges faced. The review reveals the need for Tanzanian institutions of higher learning to develop virtual learning environments to facilitate the teaching of information literacy courses to students and faculty so as to reach many of those who may not manage to attend the face-to-face information literacy sessions that are offered by librarians on a regular basis.

A Microservice-driven Collaborative Agent in Virtual Learning Environments: A Role Model for a Tracing Agent

2020 ◽  
Author(s):  
Jéferson Miguel Thalheimer ◽  
Aluizio Haendchen Filho ◽  
Fabio Julio Pereira Briks ◽  
Rafael Castaneda Ribeiro ◽  
Fernando Concatto ◽  
...  
Keyword(s):  
Learning Environments ◽  
Role Model ◽  
Virtual Learning ◽  
Virtual Learning Environments ◽  
Collaborative Agent

scholarly journals A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI

2021 ◽  
Vol 11 (2) ◽  
pp. 674
Author(s):  
Marianna Koctúrová ◽  
Jozef Juhár
Keyword(s):  
Visual Stimuli ◽  
Computer Interface ◽  
Single Subject ◽  
Detection Accuracy ◽  
The Novel ◽  
Activity Detection ◽  
Speech Activity ◽  
Novel Approach ◽  
Eeg Data ◽  
Speech Activity Detection

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

scholarly journals Collision Avoidance on Unmanned Aerial Vehicles Using Neural Network Pipelines and Flow Clustering Techniques

2021 ◽  
Vol 13 (13) ◽  
pp. 2643
Author(s):  
Dário Pedro ◽  
João P. Matos-Carvalho ◽  
José M. Fonseca ◽  
André Mora
Keyword(s):  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
High Demand ◽  
Novel Approach ◽  
Vision Sensors ◽  
Learning Techniques ◽  
Stationary Objects ◽  
Computationally Intensive ◽  
Unmanned Autonomous Vehicles ◽  
Positive Results

Unmanned Autonomous Vehicles (UAV), while not a recent invention, have recently acquired a prominent position in many industries, and they are increasingly used not only by avid customers, but also in high-demand technical use-cases, and will have a significant societal effect in the coming years. However, the use of UAVs is fraught with significant safety threats, such as collisions with dynamic obstacles (other UAVs, birds, or randomly thrown objects). This research focuses on a safety problem that is often overlooked due to a lack of technology and solutions to address it: collisions with non-stationary objects. A novel approach is described that employs deep learning techniques to solve the computationally intensive problem of real-time collision avoidance with dynamic objects using off-the-shelf commercial vision sensors. The suggested approach’s viability was corroborated by multiple experiments, firstly in simulation, and afterward in a concrete real-world case, that consists of dodging a thrown ball. A novel video dataset was created and made available for this purpose, and transfer learning was also tested, with positive results.

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