scholarly journals An Intelligent Adaptive cMOOC “IACM” for Improving Learner’s Engagement

2021 ◽  
Vol 16 (13) ◽  
pp. 82
Author(s):  
Soumaya El Emrani ◽  
Ali El Merzouqi ◽  
Mohamed Khaldi
Keyword(s):  
Learning Styles ◽  
Statistical Data ◽  
Intelligent System ◽  
Online Course ◽  
Human Machine Interaction ◽  
Massive Open Online Course ◽  
Regression Algorithms ◽  
Massive Number ◽  
Machine Interaction ◽  
Very High

Despite the massive number of enrollments in MOOC (Massive Open Online Course) platforms, dropout rates are very high. This problem can be due to several factors: Social, pedagogical, prior knowledge as well as a demotivation. To deal with this type of problems, we have designed an adaptive cMOOC (Connectivist MOOC) platform for each registered learner’s profile. From the first human-machine interaction, the process adapts the learner's need according to a pre-established model. It is based on the processing of statistical data collected by correspondence analysis and regression algorithms. Each generated learner’s profile will provide an adaptive navigation and pedagogical activities. The intelligent system presented in this work will be able to classify learners according to their preferences and learning styles.

scholarly journals Control of Robot Directions Based on Online Hand Gestures

2018 ◽  
Vol 14 (1) ◽  
pp. 41-50
Author(s):  
Mohammed Tawfeeq ◽  
Ayam Abbass
Keyword(s):  
Communication Technology ◽  
High Performance ◽  
Robotic Systems ◽  
Wireless System ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Wheeled Robot ◽  
Short Time ◽  
Machine Interaction ◽  
Very High

The evolution of wireless communication technology increases human machine interaction capabilities especially in controlling robotic systems. This paper introduces an effective wireless system in controlling the directions of a wheeled robot based on online hand gestures. The hand gesture images are captured and processed to be recognized and classified using neural network (NN). The NN is trained using extracted features to distinguish five different gestures; accordingly it produces five different signals. These signals are transmitted to control the directions of the cited robot. The main contribution of this paper is, the technique used to recognize hand gestures is required only two features, these features can be extracted in very short time using quite easy methodology, and this makes the proposed technique so suitable for online interaction. In this methodology, the preprocessed image is partitioned column-wise into two half segments; from each half one feature is extracted. This feature represents the ratio of white to black pixels of the segment histogram. The NN showed very high accuracy in recognizing all of the proposed gesture classes. The NN output signals are transmitted to the robot microcontroller wirelessly using Bluetooth. Accordingly the microcontroller guides the robot to the desired direction. The overall system showed high performance in controlling the robot movement directions.

scholarly journals An Unsupervised Method for Discovering How Does Learners' Progress Toward Understanding in MOOCs

2021 ◽  
Vol 10 (5) ◽  
pp. 40-49
Author(s):  
El mezouary Ali ◽  
Hmedna Brahim ◽  
Omar Baz
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Large Scale ◽  
Global Dimension ◽  
Online Course ◽  
Access To Education ◽  
Massive Open Online ◽  
Learning Style Preferences ◽  
Massive Open Online Course ◽  
Style Preferences

Massive Open Online Course (MOOC) seems to expand access to education and it present too many advantages as: democratization of learning, openness to all and accessibility on a large scale, etc. However, this new phenomenon of open learning suffers from the lack of personalization; it is not easy to identify learners’ characteristics because their heterogeneous masse. Following the increasing adoption of learning styles as personalization criteria, it is possible to make learning process easier for learners. In this paper, we extracted features from learners' traces when they interact with the MOOC platform in order to identify learning styles in an automatic way. For this purpose, we adopted the Felder-Silverman Learning Style Model (FSLSM) and used an unsupervised clustering method. Finally, this solution was implemented to clustered learners based on their level of preference for the sequential/global dimension of FSLSM. Results indicated that, first: k-means is the best performing algorithm when it comes to the identification of learning styles; second: the majority of learners show strong and moderate sequential learning style preferences.

Human-Machine Trust Interaction

2010 ◽  
Vol 1 (4) ◽  
pp. 61-74 ◽  
Author(s):  
Conghui Liu
Keyword(s):  
Electronic Commerce ◽  
Intelligent System ◽  
Fast Growth ◽  
Human Machine Interaction ◽  
Current State ◽  
Main Factors ◽  
Machine Systems ◽  
Machine Interaction

Improving user’s trust appropriately could help in designing an intelligent system and make it work effectively, especially with the fast growth of Web-base technology. This chapter introduces the solutions of improving user’s trust in human-machine interaction (HMI), especially for electronic commerce (e-commerce). The author firstly reviews the concept of trust and the main factors that affects the appropriateness of user’s trust in human-machine interaction, such as the properties of machine systems, the properties of human, and context. On the basis of these, the author further discusses the current state, challenges, problems and limitations of establishing and improving the user’s trust in human-machine interaction. Finally, the author summarizes and evaluates the existing solutions for improving the user’s trust appropriately in e-commerce environment.

Human-Machine Trust Interaction

2010 ◽  
pp. 471-486
Author(s):  
Conghui Liu
Keyword(s):  
Electronic Commerce ◽  
Intelligent System ◽  
Fast Growth ◽  
Human Machine Interaction ◽  
Current State ◽  
Main Factors ◽  
Machine Systems ◽  
Machine Interaction

Improving user’s trust appropriately could help in designing an intelligent system and make it work effectively, especially with the fast growth of Web-base technology. This chapter introduces the solutions of improving user’s trust in human-machine interaction (HMI), especially for electronic commerce (e-commerce). The author firstly reviews the concept of trust and the main factors that affects the appropriateness of user’s trust in human-machine interaction, such as the properties of machine systems, the properties of human, and context. On the basis of these, the author further discusses the current state, challenges, problems and limitations of establishing and improving the user’s trust in human-machine interaction. Finally, the author summarizes and evaluates the existing solutions for improving the user’s trust appropriately in e-commerce environment.

scholarly journals Design and Implementation of a Smart House System with Voice Control

2020 ◽  
Vol 9 (3) ◽  
pp. 2067-2073
Keyword(s):  
Life Support ◽  
Intelligent System ◽  
Sleep Mode ◽  
Human Machine Interaction ◽  
Gas Leakage ◽  
Design And Implementation ◽  
Voice Control ◽  
Single Complex ◽  
Smart House ◽  
Machine Interaction

Smart House is an intelligent management system that integrates all equipment into a single complex. It solves various tasks in the field of security, life support, entertainment, and communication. This paper presents a complete design and implementation of a Smart House system with voice control, describes the hardware and software parts as well as the interaction between them. Voice control performed with simple instructions using Microsoft Speech Platform. Recognized commands will be encrypted on the software side and then will be sent via Bluetooth HC-06 module to the hardware side for execution. Among the developed features for the created prototype are lighting control, home temperature control, sleep mode control, the possibility of setting an alarm clock, security mode and gas leakage check. In case of problems, a user will receive a notification via email and/or SMS. Finally, this paper presents the results of experiments for voice control, which shows that voice control in Smart House is the next step in improving this intelligent system, is the next step in improving human-machine interaction and it provides great help for people with special needs and disabilities

Valor dos massive open online course (MOOC) na educação para a cidadania

2018 ◽  
Vol 13 (2) ◽  
Author(s):  
Hermano Carmo ◽  
Teresa Maia e Carmo
Keyword(s):  
Online Courses ◽  
Massive Open Online Courses ◽  
Online Course ◽  
Massive Open Online ◽  
Massive Open Online Course ◽  
Tecnología Educativa

A sociedade contemporânea é marcada por três macrotendências que a identificam como uma sociedade singular na história humana: processo de mudança acelerada, desigualdade crescente e fibrilhação dos sistemas de poder. Tais tendências têm tido como efeitos um quadro de ameaças e oportunidades que tanto têm constituído gigantesco desafio aos sistemas educativos quanto configuram a urgência de ressocialização de todas as gerações vivas no sentido da construção de uma cidadania global. Nesse contexto, propõe-se um modelo que configura uma estratégia de educação para a cidadania, com dois eixos, quatro vertentes e dez áreas-chave. Seguidamente, descreve-se e discute-se a emergência quase explosiva dos Massive Open Online Courses (MOOC) a partir de instituições de ensino superior internacionalmente reconhecidas, no quadro do novo paradigma digital, sua diversidade e seu potencial ainda em aberto. Confrontando a nova abordagem educativa com o modelo de educação para a cidadania proposto, conclui-se constituir um meio robusto para o potenciar.Palavras-chave:Conjuntura. Macrotendências. Educação para a cidadania. MOOC. Tecnologia educativa. Paradigma digital.Link: http://revista.ibict.br/inclusao/article/view/4171/3642

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

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