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

Deciphering the assembly pathway of type IV pili in Myxococcus xanthus

Type IV pili (T4P) are hairlike surface structures, present on a variety of different bacteria. They are polymers involved in diverse functions such as motility, adherence, protein secretion, DNA uptake and in many pathogens they are found to be the primary colonization factor. Especially their role in virulence makes T4P particularly relevant for studying pilus function and assembly. The T4P machinery consists of 12 conserved proteins building an envelope-spanning macromolecular machinery, which localizes polarly in Myxococcus xanthus. Although most of the proteins have been known and studied for a long time, the precise mechanism of how and in which order the individual components are assembled to generate a macromolecular machinery remain largely unknown. Here we uncovered a sequential, outside-in assembly pathway starting with the outer membrane (OM) PilQ secretin, and proceeding inwards over the periplasm and inner membrane (IM) to the cytoplasm. Specifically, by taking advantage of the cell biology tools for studying T4P in M. xanthus, we carried out one of the largest screens comprising 11 of the 12 proteins of the T4P machinery by systematically profiling the stability and localization of T4P proteins in the absence of each individual other T4P protein in combination with mapping direct protein-protein interactions. Using these approaches, we show that assembly of the T4P machinery initiates with the formation of the PilQ secretin ring, assisted by its pilotin Tgl, in the OM. Oligomeric PilQ serves as an assembly platform for further T4P components. PilQ recruits TsaP, a peptidoglycan binding protein, as well as PilP by direct interactions with PilP. PilP, in turn, recruits the IM proteins PilN and PilO. PilP/PilO/PilN likely make up a complex aligning IM and OM components of the T4P machinery. The PilP/PilO/PilN complex recruits cytoplasmic PilM by direct interaction between PilN and PilM and recruits PilC, presumably by direct interaction between PilC and PilO. Finally, the ATPases PilB and PilT that power extension and retraction of T4P, localize independently of other T4P machinery proteins. In this study, we elucidate the assembly process and functional interactions between T4P proteins. This work lays the basis for further understanding of these functionally highly versatile surface structures. Interestingly, the assembly of the type II and III secretion systems also initiates from the OM secretin and proceeds inwards. Thus, an outside-in assembly pathway is emerging as a conserved feature in secretin-containing trans-envelope export machines