scholarly journals Tecnologías disruptivas en educación virtual

2021 ◽  
Vol 10 (7) ◽  
pp. 185-200
Author(s):  
Yolanda González Castro ◽  
Omaira Manzano Duran ◽  
Marleny Torres Zamudio
Keyword(s):  
Machine Learning ◽  
Educación Virtual ◽  
E Learning

La cuarta revolución industrial está impactando el ámbito empresarial, por lo tanto, se hace necesario que las organizaciones educativas virtuales estén preparadas para innovar en sus procesos. La metodología empleada para la presente investigación responde a un enfoque mixto. En lo cualitativo se realizó una revisión documental de bases de datos científicas y en lo cuantitativo se realizó un diseño no experimental longitudinal de tipo descriptivo apoyado en las fases de la vigilancia tecnológica. Mediante el empleo de la base de datos de Scopus y el software VOSViewer se determinaron los siguientes clústeres a) características del e-learning, (b) características de la industria 4.0, (c) avances tecnológicos tradicionales para la educación y la industria y (d) avances tecnológicos disruptivas para la educación virtual y la industria. Entre las tecnologías que tienen mayor incidencia en el campo de la educación virtual están: el machine learning, la inteligencia artificial, la minería de datos, el internet de las cosas, la realidad virtual, realidad aumentada y sistemas embebidos.

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.

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.

Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis

2012 ◽  
pp. 1779-1798
Author(s):  
Dumitru Dan Burdescu ◽  
Marian Cristian Mihaescu
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Input Data ◽  
Software System ◽  
Business Logic ◽  
Self Assessment ◽  
Student Activities ◽  
Learning Platform ◽  
Engineering Department ◽  
E Learning

Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).

Dropout prediction in e-learning courses through the combination of machine learning techniques

2009 ◽  
Vol 53 (3) ◽  
pp. 950-965 ◽  
Author(s):  
Ioanna Lykourentzou ◽  
Ioannis Giannoukos ◽  
Vassilis Nikolopoulos ◽  
George Mpardis ◽  
Vassili Loumos
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
E Learning

Semantically enhanced machine learning approach to recommend e-learning content

2020 ◽  
Vol 15 (4) ◽  
pp. 389
Author(s):  
Rachita Misra ◽  
Rojalina Priyadarshini ◽  
Rawaa Alatrash ◽  
Hadi Ezaldeen
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Learning Content ◽  
Machine Learning Approach ◽  
E Learning ◽  
Semantically Enhanced
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