scholarly journals Advanced Techniques in the Analysis and Prediction of Students’ Behaviour in Technology-Enhanced Learning Contexts

2020 ◽  
Vol 10 (18) ◽  
pp. 6178
Author(s):  
Juan A. Gómez-Pulido ◽  
Young Park ◽  
Ricardo Soto
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Intelligent Systems ◽  
Technology Enhanced Learning ◽  
Learning Tools ◽  
Special Issue ◽  
Learning Context ◽  
Learning Collaborative ◽  
Academic Field ◽  
Enhanced Learning

The development and promotion of teaching-enhanced learning tools in the academic field is leading to the collection of a large amount of data generated from the usual activity of students and teachers. The analysis of these data is an opportunity to improve many aspects of the learning process: recommendations of activities, dropout prediction, performance and knowledge analysis, resources optimization, etc. However, these improvements would not be possible without the application of computer science techniques that have demonstrated a high effectiveness for this purpose: data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to intelligent systems. This Special Issue provides 17 papers that show advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, big data, and machine learning in the teaching-enhanced learning context.

scholarly journals Analyzing and Predicting Students’ Performance by Means of Machine Learning: A Review

2020 ◽  
Vol 10 (3) ◽  
pp. 1042 ◽  
Author(s):  
Juan L. Rastrollo-Guerrero ◽  
Juan A. Gómez-Pulido ◽  
Arturo Durán-Domínguez
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Technology Enhanced Learning ◽  
Learning Collaborative ◽  
Academic Tasks ◽  
Learning Contexts ◽  
The Relationship ◽  
Enhanced Learning

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

scholarly journals Online, Blended And Technology-Enhanced Learning: Tools To Facilitate Community College Student Success In The Digitally-Driven Workplace

2017 ◽  
Vol 10 (4) ◽  
pp. 255-262 ◽  
Author(s):  
Dawn Levy
Keyword(s):  
Community College ◽  
Online Learning ◽  
Value Added ◽  
Community College Student ◽  
Retention Rates ◽  
Technology Enhanced Learning ◽  
Learning Tools ◽  
Self Directed Learning ◽  
Online Learning Resources ◽  
Enhanced Learning

Community colleges have embraced distance education as a means to provide increased flexibility and access to their large numbers of non-traditional students. Retention rates and student achievement measures alone may not reflect all of the benefits and opportunities that online learning, blended or hybrid learning, and technology-enhanced learning may afford these students. Online learning resources should be viewed as a tremendous value-added benefit for community college students, not only for the content conveyed, but also for fostering the digital readiness, cultivating the professional personas, and encouraging the self-directed learning needed to succeed in the digitally-driven workplace.

Big Data Analytics with Machine Learning Tools

2017 ◽  
pp. 49-97 ◽  
Author(s):  
T. P. Fowdur ◽  
Y. Beeharry ◽  
V. Hurbungs ◽  
V. Bassoo ◽  
V. Ramnarain-Seetohul
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Tools

Call for Special Issue Papers: Big Data Analytics and Intelligent Systems for Cybersecurity

Big Data ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 146-147
Author(s):  
Ahmed A. Abd El-Latif ◽  
Lo'ai Tawalbeh ◽  
Yassine Maleh ◽  
Gokay Saldamli
Keyword(s):  
Big Data ◽  
Intelligent Systems ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Special Issue

Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

2019 ◽  
pp. 161-194
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Data Analytics ◽  
Big Data Analysis ◽  
Customer Segmentation ◽  
Learning Context ◽  
Feature Vectors

Data analytics has grown in a machine learning context. Whatever the reason data is used or exploited, customer segmentation or marketing targeting, it must be processed first and represented on feature vectors. Many algorithms, such as clustering, regression, classification, and others, need to be represented and clarified in order to facilitate processing and statistical analysis. If we have seen, through the previous chapters, the importance of big data analysis (the Why?), as with every major innovation, the biggest confusion lies in the exact scope (What?) and its implementation (How?). In this chapter, we will take a look at the different algorithms and techniques analytics that we can use in order to exploit the large amounts of data.

Technology-Enhanced Learning in the Corporate Context

2011 ◽  
pp. 306-323
Author(s):  
Anoush Margaryan ◽  
Betty Collis
Keyword(s):  
Informal Learning ◽  
Opportunity To Learn ◽  
Technology Enhanced Learning ◽  
Learning Resources ◽  
Learning Context ◽  
Corporate Learning ◽  
Corporate Context ◽  
Formal And Informal Learning ◽  
Key Issues ◽  
Enhanced Learning

This paper focuses on tools and strategies to integrate the strengths of formal and informal learning in the corporate context via the use of work-based activities within courses. The following proposition is argued: An effective course in the corporate context becomes a blend of formal and informal learning, a guided opportunity to learn from and share experiences gained through work-based activities, and to contribute one’s own experiences as learning resources for others, for use in both formal and informal learning settings. Examples from practice in a multinational corporate learning context where a number of courses have been redesigned to allow integration of formal and informal learning are given. Key issues and challenges arising from this experience are discussed.

Technology Enhanced Learning Tools

2008 ◽  
pp. 1-27
Author(s):  
Goran Shimic
Keyword(s):  
Main Part ◽  
Learning Management Systems ◽  
Systems Development ◽  
Short Description ◽  
Learning Systems ◽  
Technology Enhanced Learning ◽  
Learning Tools ◽  
Intelligent Behavior ◽  
E Learning ◽  
Enhanced Learning

This chapter emphasizes the variety of today’s e-learning systems. They have both positive and negative characteristics. Several useful tools are common for these systems. The main part of this chapter contains a detailed description of e-learning systems and their tools. If a system is appropriate for the needs of the learner then it has more intelligent behavior and its tools are more specialized. Some systems have separate tools that act as standalone applications. Others contain built in tools. In this chapter, the e-learning tools are grouped by their functions. Owing to standardization efforts, the differences between the e-learning tools become their advantages, and the e-learning systems become interoperable. The intelligent learning management systems (ILMS) become a new way to integrate the benefits of the different e-learning systems. At the end of the chapter there is a short description of an ILMS named Multitutor. This represents a possible way of future e-learning systems development.

Sign in / Sign up

Export Citation Format

Share Document