Improvement of the Intelligent Tutor by Identifying the Face of the E-Learner's

As part of our project which aims at the realization of a system named ASTEMOI. In this article, we display a new and productive facial image representation based on the Local Sensitive Hash (LSH). This technique makes it possible to recognize the learners who follow their training in our learning platform. Once recognized, the student must be oriented towards an appropriate profile that takes into account his strengths and weaknesses. We also use a light processing module on the client device with a compact code so that we don’t need a lot of bandwidth, a lot of network transmission capacity to send the feature over the network, and to be able to index many pictures in a huge database in the cloud

A Multi-Agent Architecture for An Intelligent Web-Based Educational System

An intelligent educational system must constitute an adaptive system built on multi-agent system architecture. The multi-agent architecture component provides self-organization, self-direction, and other control functionalities that are crucially important for an educational system. On the other hand, the adaptiveness of the system is necessary to provide customization, diversification, and interactional functionalities. Therefore, an educational system architecture that integrates multi-agent functionality [50] with adaptiveness can offer the learner the required independent learning experience. An educational system architecture is a complex structure with an intricate hierarchal organization where the functional components of the system undergo sophisticated and unpredictable internal interactions to perform its function. Hence, the system architecture must constitute adaptive and autonomous agents differentiated according to their functions, called multi-agent systems (MASs). The research paper proposes an adaptive hierarchal multi-agent educational system (AHMAES) [51] as an alternative to the traditional education delivery method. The document explains the various architectural characteristics of an adaptive multi-agent educational system and critically analyzes the system’s factors for software quality attributes

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

Contextual support of social engagement and reflection on the Web

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Smart education: an event framework for cognitive blended learning

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Mobile Telecommunication and Innovation at (MSc.MTI) at Strathmore UniversityDigital learning has increasingly been adopted around the world, evidenced by large scale deployment of online learning platforms. More specifically, the use of hand held devices such as mobile phones and tablets has disrupted learning as we traditionally knew it. Blended learning,which extends classroom learning with computer mediated learning, is increasingly being adopted by education systems around the world. However, the two (blended learning and traditional classroom learning) have not been well integrated. For example, there is limited or no information sharing between digital learning mostly carried out by an automated tutor and the traditional classroom conducted by a human instructor. This leads to fragmentation in the overall teaching and learning experience.Existing blended learning platforms have tried to address this issue by focusing on performance management. This approach ignores the bigger challenge in public and private schools: the large number of students to teacher and the inability to offer personalised learning that is essential for students to excel academically. Understanding how personalized technical interventions can be designed requires understanding of where issues intersects. We present the overall architecture and design of event framework. The first version supporting a core set of capabilities for blended learning has been implemented as mobile applications for teachers and students. We conducted a limited pilot to test the technology in an actual classroom setting. We also report on a usability study of the event framework that demonstrates user awareness and support for data-driven cognitive decision-making in education

Examining the Relationship Between Mobile Phone Utilization on Self-Directed Online Professional Development Among Early Childhood Practitioners: A Preregistered Study

This study examines Arkansas early childhood practitioners\u27 propensity for self-directed learning when using mobile phones to participate in self-paced online professional development. Further, the activity system model provides context for understanding practitioners\u27 willingness to use technology, like smartphones, to adopt new information when interacting in online learning formats. The problem is that although we must offer professional development to ensure we have competent early childhood practitioners statewide, we are uncertain whether those practitioners have the propensity to use mobile phones to direct their online learning. The purpose of this cross-sectional regression study is to examine the relationship between early childhood practitioners\u27 mobile phone usage and their self-directed learning while considering the moderating influence of technology readiness when participating in asynchronous online professional development. This study aims to go beyond simply looking at the effect of mobile phone use on practitioners\u27 propensity for self-directed learning. It also examines the potential for technology readiness to moderate the relationship between these factors through a multiple linear regression analysis utilizing survey data. The study\u27s implications regarding practitioners\u27 self-directed online learning when using mobile phones may provide insights for professional development stakeholders who govern, develop, and implement early childhood practitioner continuing education and workforce training

A survey on internet of things enabled smart campus applications

The fictional future home, workspace or city, as predicted by science TV shows of the 1960s, is now a reality. Modern microelectronics and communication technologies offer the type of smart living that looked practically inconceivable just a few decades ago. The Internet of Things (IoT) is one of the main drivers of the future smart spaces. It enables new operational technologies and offers vital financial and environmental benefits. With IoT, spaces are evolving from being just 'smart' to become intelligent and connected. This survey paper focuses on how to leverage IoT technologies to build a modular approach to smart campuses. The paper identifies the key benefits and motivation behind the development of IoT-enabled campus. Then, it provides a comprehensive view of general types of smart campus applications. Finally, we consider the vital design challenges that should be met to realise a smart campus

Estimating Air Pollution Levels Using Machine Learning

Air pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2.5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2.5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air quality estimation techniques, which need several instruments and setup and have large financial expenditures for equipment acquisition and maintenance