Competency-Based Learning Object Sequencing Using Particle Swarms

Abstrac

PERSONALIZED LEARNING PATH GENERATION BASED ON GENETIC ALGORITHMS

A substantial disadvantage of traditional learning is that all students follow the same curriculum sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases orient students to follow fixed sequences during the learning process, thus impairing their performance. Curriculum sequencing is an important research issue for learning process because no fixed learning paths will be appropriate for all learners. For this reason, many research papers are focused on the development of mechanisms to offer personalization on learning sequences, considering the learner needs, interests, behaviors, and abilities. In most cases, these researches are totally focused on the student\u27s preferences, ignoring the level of difficulty and the relation degree that exists between various concepts in a course. This work presents a genetic algorithm-based model to offer personalization on learning paths, considering the level of difficulty and relation degree of the constituent concepts of a course. The experimental result shows that the genetic algorithm is suitable to generate optimal learning paths based on learning object difficulty level, duration, rating, and relation degree between each learning object. Furthermore, it indicates that using the proposed genetic-based approach for personalized learning path generation is superior to the traditional curriculum sequencing

Moodle HEODAR implementation and its implantation in an academic context.

[EN]One of the most important aspects of a 'continuously in change’ society is to improve everything everywhere. In order to obtain the best products, they should be periodically evaluated and reengineered. So the evaluation task and of course, the adequate results interpretation, can make all the difference between competitors. E-learning is similar to these products. Different issues can be evaluated to make learning process getting better and better, such as tutors, platform software and contents. In this last issue, it can be included the minimum knowledge unit: the learning object (LO) (De Marcos et al., 2008). There exist different models and methods for LO evaluation. What is pretended with this work is to choose one model and implement a singular tool, in order to automatically evaluate these LOs and produce a set of information, that can be used to improve those LOs. In this case, it is implemented in the evaluation model called HEODAR (Morales et al., 2008a) and after that the model is implanted in Studium, the Moodle campus of Salamanca University

An Automated Adaptive Mobile Learning System Using Optimal Shortest Path Algorithms

Technological innovation opens the door to create a personal learning experience for any student. In this research, we discuss adaptive learning techniques and the style of learning that integrates existing learning techniques combined with new ideas. To create an effective user friendly learning environment, adaptive learning techniques should be used in order to identify the personal needs of students and reduce their individual knowledge gaps. The result will produce learning path containing relevant content that will provide a better learning direction for each student. This dissertation explores the opportunity of using adaptive learning techniques to identify the personal needs of each student by combining different learning styles, student profiles and individualized course content. By using a directed graph, we are able to represent an accurate picture of the course descriptions for online courses through computer-based implementation of various educational systems. E-learning (electronic learning) and m-learning (mobile learning) systems are modeled as a weighted directed graph where each node represents a course unit. The Learning Path Graph represents and describes the structure of the domain knowledge, including the learning goals, and all other available learning paths. In this research, we propose a system prototype that implements optimal adaptive learning path algorithms using students’ information from their profiles and their learning style. Our goal is to improve students’ learning performances through the m-learning system in order to provide suitable course contents sequenced in a dynamic form for each student

Computer Science 2019 APR Self-Study & Documents

UNM Computer Science APR self-study report and review team report for Spring 2019, fulfilling requirements of the Higher Learning Commission

Disruptive Technologies with Applications in Airline & Marine and Defense Industries

Disruptive Technologies With Applications in Airline, Marine, Defense Industries is our fifth textbook in a series covering the world of Unmanned Vehicle Systems Applications & Operations On Air, Sea, and Land. The authors have expanded their purview beyond UAS / CUAS / UUV systems that we have written extensively about in our previous four textbooks. Our new title shows our concern for the emergence of Disruptive Technologies and how they apply to the Airline, Marine and Defense industries. Emerging technologies are technologies whose development, practical applications, or both are still largely unrealized, such that they are figuratively emerging into prominence from a background of nonexistence or obscurity. A Disruptive technology is one that displaces an established technology and shakes up the industry or a ground-breaking product that creates a completely new industry.That is what our book is about. The authors think we have found technology trends that will replace the status quo or disrupt the conventional technology paradigms.The authors have collaborated to write some explosive chapters in Book 5:Advances in Automation & Human Machine Interface; Social Media as a Battleground in Information Warfare (IW); Robust cyber-security alterative / replacement for the popular Blockchain Algorithm and a clean solution for Ransomware; Advanced sensor technologies that are used by UUVs for munitions characterization, assessment, and classification and counter hostile use of UUVs against U.S. capital assets in the South China Seas. Challenged the status quo and debunked the climate change fraud with verifiable facts; Explodes our minds with nightmare technologies that if they come to fruition may do more harm than good; Propulsion and Fuels: Disruptive Technologies for Submersible Craft Including UUVs; Challenge the ammunition industry by grassroots use of recycled metals; Changing landscape of UAS regulations and drone privacy; and finally, Detailing Bioterrorism Risks, Biodefense, Biological Threat Agents, and the need for advanced sensors to detect these attacks.https://newprairiepress.org/ebooks/1038/thumbnail.jp