scholarly journals Comparative Analysis of Clustering Algorithms and Moodle Plugin for Creation of Student Heterogeneous Groups in Online University Courses

2021 ◽  
Vol 11 (13) ◽  
pp. 5800
Author(s):  
Giacomo Nalli ◽  
Daniela Amendola ◽  
Andrea Perali ◽  
Leonardo Mostarda
Keyword(s):  
Machine Learning ◽  
Group Formation ◽  
Clustering Algorithms ◽  
Homogeneous Groups ◽  
University Courses ◽  
Heterogeneous Groups ◽  
E Learning ◽  
Learning Platforms ◽  
The Creation ◽  
Collaborative Activities

Online learning environments such as e-learning platforms are often used to encourage collaborative activities amongst students. In this context, group work is often used to improve the learning outcomes. Group formation is often performed randomly since university courses can be composed of a large number of students. While random formation saves time and resources, the student heterogeneity in terms of learning capabilities is not guaranteed. Although advanced e-learning platforms such as Moodle are widely used, they lack plugins that allow the automatic formation of heterogeneous groups of students. This work proposes a novel intelligent plugin for Moodle that allows the creation of heterogeneous groups by using Machine Learning. This intelligent application can be used in order to improve the students’ performance in collaborative activities. Our machine learning approach first uses clustering algorithms on Moodle data to identify homogeneous groups that are composed of students having similar behavior. Heterogeneous groups are then created by combining students selected from different homogeneous groups. To this end, a novel algorithm and the corresponding software, which allow the creation of heterogeneous groups, have been developed. We have implemented our approach by realizing a Moodle plugin where teachers can create heterogeneous groups.

scholarly journals A New Learning Path Model for E-Learning Systems

2021 ◽  
Vol 19 (2) ◽  
pp. 20-40
Author(s):  
David Brito Ramos ◽  
Ilmara Monteverde Martins Ramos ◽  
Isabela Gasparini ◽  
Elaine Harada Teixeira de Oliveira
Keyword(s):  
Student Behavior ◽  
Group Formation ◽  
Learning Systems ◽  
Learning System ◽  
Path Model ◽  
New Approach ◽  
Learning Path ◽  
E Learning ◽  
Learning Paths ◽  
Collaborative Activities

This work presents a new approach to the learning path model in e-learning systems. The model uses data from the database records from an e-learning system and uses graphs as representation. In this work, the authors show how the model can be used to represent visually the learning paths, behavior analysis, help to suggest group formation for collaborative activities, and thus assist the teacher in making decisions. To validate the practical utility of the model, the authors created two tools, one to visualize the learning paths and another to suggest groups of students for collaborative activities. Both tools were tested in a real environment, presenting useful results. The authors carried experiments with students from three programs: physics, electrical engineering, and computer science. Experiments show that it is possible to use the proposed learning path to analyze student behavior patterns and recommend group formation with positive results.

scholarly journals Uses Of Communicative And Collaborative Activities In E-Learning: Moodle Platform As A Model/ استخدامات الأنشطة التواصلية والتعاونية في التعليم الالكتروني: منصة Moodle نموذجا

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Abdelghani Bendridi
Keyword(s):  
Learning Process ◽  
Learning Management Systems ◽  
Hybrid Learning ◽  
Management Systems ◽  
Educational Activities ◽  
Descriptive Research ◽  
E Learning ◽  
Learning Platforms ◽  
Collaborative Activities ◽  
Better Than

This study aims to defining the uses of communicative and collaborative activities in learning process via Moodle platform, through a descriptive research. This platform is a kind of the learning management systems (LMS), it is specially designed to manage interactive electronic lessons, with the aim of supporting the learning process, whether hybrid learning (in Classroom and via platform) or completely virtual learning. In this research, we will focus on the most important uses and features it provides, as well as the contribution of the educational activities to creating new tools of communication between students and teachers and how to make learning process more interactive than classroom courses, so we describe and analyze in this study the most useful educational activities in Moodle platform (forum, chat, wiki, glossary, survey and database) and the different uses through the examples that we created.The most important results of this study are: The E-learning platforms Moodle provided an environment for active learning through communicative and collaborative activities that simulate the Learning process in the classroom. Indeed, these activities made it easier for teachers to follow the learners better than what is available in the classroom, and enabled their learning to be assessed better. Therefore, the challenge is how to design educational activities for achieving the objectives of the lessons in E-learning platforms.

Recommender Systems for E-Learning

2021 ◽  
pp. 221-247
Author(s):  
Mohamed Abdullah Amanullah ◽  
Abdessalem Khedher
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommendation Systems ◽  
Machine Learning Techniques ◽  
User Needs ◽  
Learning Techniques ◽  
E Learning ◽  
Minimal Deviation ◽  
Learning Platforms

The recommender systems are really important in this phase because the users want to be concentrated and to be focused on the domain in which they are interested. There should be minimal deviation in the topics suggested by the recommendation engines. Some of the famous e-learning platforms suggest recommendations based on tags such as highest rated, bestsellers, and so on in various domains. This ultimately makes the users deviate from the domain in which they have to master, and it results in not satisfying the user needs. So, to address this problem, effective recommendation engines will help provide recommendations according to the users by implementing the machine learning techniques such as collaborative filtering and content-based techniques. In this chapter, the authors discuss the recommendation systems, types of recommendation systems, and challenges.

scholarly journals The Application of Machine Learning and Neural Networks to Automated Text and Visual Assignment Verification Used as Assistance to Educators

2020 ◽  
pp. 39-45
Author(s):  
Н. О. Бесшапошников ◽  
М. С. Дьяченко ◽  
А. Г. Леонов ◽  
М. А. Матюшин ◽  
А. E. Орловский
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Teaching Methods ◽  
Online Teaching ◽  
Assessment Tools ◽  
Automated Assessment ◽  
Distant Learning ◽  
E Learning ◽  
Extensive Introduction ◽  
Learning Platforms

Процесс цифровизации образования, активно проводимый в нашей стране и по всему миру, позволил более широко применить в учебном процессе современные приемы преподавания, перенося часть педагогической нагрузки с очного формата на дистанционный. Проектируемые и используемые цифровые образовательные платформы уже сейчас включают в себя не только оцифрованный лекционный видеоматериал и электронные формы учебников, но и элементы автоматизации проверки выполненных учащимися заданий. Расширение области применения автоматической проверки решенных учащимися задач и выполненных упражнений является объективной необходимостью, в противном случае при дистанционных формах образовательного процесса резко возрастает нагрузка на педагога, который должен выделять значительное время на проверку увеличившегося самостоятельной работы школьников и студентов. Кроме того, при дистанционном преподавании снижается эффект личного присутствия педагога, когда учитель и ученики разделены экранами компьютеров. Существенной помощью может стать использование интеллектуальных помощников преподавателя и автоматизированных систем проверки, построенных методами машинного обучения и технологии нейронных сетей. В настоящей статье рассмотрены подходы к решению поставленных задач по автоматической проверке графических заданий и выявлению заимствований в текстовом виде. Показаны возможные варианты реализации этих функций с использованием технологий искусственного интеллекта. The digitalization of education in Russia and worldwide enables a more extensive introduction of advanced teaching methods through a partial switch from offline to online teaching. The existing and coming e-learning platforms feature not only digital lecture videos and e-textbooks, but some automated assessment/grading tools. There is a need to expand the coverage of such tools to avoid the extreme burden of online teaching as the educator has to allocate significant time for assessing the increased amount of high school/university student assignments. Also, distant learning diminishes the effect of the educator personal presence since the teacher and the student are separated by their computer screens. Smart educator assistants and automated assessment tools based on machine learning and neural networks can significantly alleviate the problem. This study offers some strategies for automated assessment of graphic assignments and checks for plagiarism. Possible AI-based implementations of such features are presented.

Platform for Analysing and Encouraging Student Activity on Contest and E-learning Systems

2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Educational Policy ◽  
Learning Systems ◽  
Data Sources ◽  
Or Education ◽  
Student Activity ◽  
The World ◽  
E Learning ◽  
Analyse Data

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.

Automated system for creating training programs for mass professional users

2020 ◽  
pp. 21-27
Author(s):  
V.K. Grigoriev ◽  
A.A. Biryukova ◽  
A. Yu. Volk ◽  
A.S. Ilyushechkin
Keyword(s):  
Experimental Verification ◽  
Training Programs ◽  
Automated System ◽  
Software Product ◽  
Learning Programs ◽  
E Learning ◽  
The Creation ◽  
The Impact

The article discusses the automation of the creation and use of e-learning programs. The impact of automating the learning of a large number of users on the effectiveness of the introduction of a new software product is analyzed. The methods and algorithms that increase the efficiency of creating electronic training programs on example of the author’s automated system “Tutor Builder” are described. The results of experimental verification of the automated system are provided.

scholarly journals Multi-Agent System Observer: Intelligent Support for Engaged E-Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1370
Author(s):  
Igor Vuković ◽  
Kristijan Kuk ◽  
Petar Čisar ◽  
Miloš Banđur ◽  
Đoko Banđur ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Intelligent Support ◽  
Course Success ◽  
States Of Emergency ◽  
Learning Platform ◽  
Rapid Spread ◽  
E Learning ◽  
Multi Agent ◽  
Observer System

Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.

scholarly journals Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
José Castela Forte ◽  
Galiya Yeshmagambetova ◽  
Maureen L. van der Grinten ◽  
Bart Hiemstra ◽  
Thomas Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Mortality Risk ◽  
Clustering Algorithms ◽  
Kidney Injury ◽  
Risk Groups ◽  
Admission Diagnosis ◽  
Dutch Hospital ◽  
Cluster Membership

AbstractCritically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25–56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.

scholarly journals Do machine learning platforms provide out-of-the-box reproducibility?

2021 ◽  
Author(s):  
Odd Erik Gundersen ◽  
Saeid Shamsaliei ◽  
Richard Juul Isdahl
Keyword(s):  
Machine Learning ◽  
Learning Platforms
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