Special Issue "Data Mining and Computational Intelligence for E-learning and Education"

A special issue of Data (ISSN 2306-5729). This special issue belongs to the section "Information Systems and Data Management".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 760

Special Issue Editors

Prof. Dr. Antonio Sarasa Cabezuelo
E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, Madrid 28040, Spain
Interests: NonSQL databases; machine learning; artificial intelligence; e-learning; programming languages
Special Issues, Collections and Topics in MDPI journals
Dr. Ramón González del Campo Rodríguez Barbero
E-Mail Website
Guest Editor
Facultad de Informática, Universidad Complutense de Madrid, 28040 Madrid, Spain
Interests: fuzzy logic; artificial intelligence; algorithmic optimization; databases

Special Issue Information

Dear Colleagues,

In recent decades, the rise of artificial intelligence has driven its application in various fields, including education. Applications can be found aimed at analyzing the data of the learning-teaching activity, both in the face-to-face environment and in distance-learning environments, through intelligent algorithms with the aim of extracting information about the educational process. From this information, it is possible to infer aspects such as the reasons for the success or failure of students, patterns of behavior and learning, and other predictions. Likewise, applications have also been developed that implement intelligent algorithms with the aim of automating the educational process. Related to this last point is the development of chatbots and approaches to ethics in the use of artificial intelligence. In this sense, an area of interest has developed relating to the application of artificial intelligence to problem solving in education. The objective of this Special Issue is to bring together works that show the latest advances in the application of artificial intelligence to the educational field, as well as those describing specific experiences and applications to certain problems.

The objective of this Special Issue is to serve as a meeting point for all researchers working in these fields, both theoretically and applied. The topics of interest include but are not limited to:

  • Machine learning applied to e-learning and education;
  • Artificial intelligence applied to e-learning and education;
  • Big data and e-learning;
  • Intelligent learning systems;
  • Data analysis applied to e-learning and education;
  • Intelligent systems for e-learning;
  • Ethical aspects of the application of AI in education;
  • E-learning analytics;
  • Data mining for e-learning and education;
  • Chatbots for education.

Both review articles on the state of the art and experimental or theoretical articles are welcome.

Prof. Dr. Antonio Sarasa Cabezuelo
Dr. Ramón González del Campo Rodríguez Barbero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • e-learning
  • machine learning
  • artificial intelligence
  • data analysis
  • algorithms
  • big data

Published Papers (1 paper)

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Research

Article
Development of a Model Using Data Mining Technique to Test, Predict and Obtain Knowledge from the Academics Results of Information Technology Students
Data 2022, 7(5), 67; https://doi.org/10.3390/data7050067 - 23 May 2022
Viewed by 582
Abstract
Due to the huge amount of data obtained from students’ academic results in most tertiary institutions such as the colleges, polytechnics and universities, data mining has become one of the most effective tools for discovering vital knowledge from students’ dataset. The discovered knowledge [...] Read more.
Due to the huge amount of data obtained from students’ academic results in most tertiary institutions such as the colleges, polytechnics and universities, data mining has become one of the most effective tools for discovering vital knowledge from students’ dataset. The discovered knowledge can be productive in understanding numerous challenges in the scope of education and providing possible solutions to these challenges. The main objective of this research is to utilize the J48 decision algorithm model to test, classify and predict the students’ dataset by identifying some important attributes and instances. The analysis was conducted on the final year students’ academic results in C# programming amongst five universities which was imported in csv excel file dataset in WEKA environment. These training datasets contained the scores obtained in the examinations, grade remarks, grades, gender, and department. The knowledge extracted for the prediction model will help both the tutors and students to determine the success grade performance in the future. Flow lines, J48 decision trees, confusion matrices and a program flowchart were generated from the students’ dataset. The KAPPA value obtained from the prediction in this research ranges from 0.9070–0.9582 which perfectly agrees with the standard for an ideal analysis on datasets. Full article
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