Special Issue "Data Analytics and Machine Learning in Education"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Prof. Dr. Juan A. Gómez-Pulido
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Guest Editor
Department of Technologies of Computers and Communications, Universidad de Extremadura, Cáceres, Spain
Interests: optimization and computational intelligence; machine learning; reconfigurable computing and FPGAs; wireless communications; bioinformatics
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Prof. Dr. Young Park
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Guest Editor
Department of Computer Science and Information Systems, Bradley University, Peoria, IL 61625, USA
Interests: personalized recommender systems and prediction systems; personalized and advanced Web search; formal concept analysis and its applications to software engineering; Web search and data mining; software reuse; semantics-based program analysis
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Prof. Dr. Ricardo Soto
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Guest Editor
School of Computer Engineering, Pontificia Universidad Católica de Valparaíso, Valparaiso, Chile
Interests: constraint programming; compilers and languages design; global optimization
Special Issues and Collections in MDPI journals
Prof. Dr. José M. Lanza-Gutiérrez
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Guest Editor
Department of Electronic Technology, Carlos III University of Madrid, Leganés, Spain
Interests: machine learning; data science; optimization; edge computing; cognitive systems

Special Issue Information

Dear Colleagues,

The generalization of the use of advanced technological tools in the field of educational is leading to the generation of big data related to academic activities which involve students and teachers. For example, the inclusion of virtual campuses as a regular educational management tool encourages the virtualization of teaching, the online management of grades, the monitoring of student progress, the recording of all kinds of educational variables, etc. In this way, technology-enhanced learning (TEL) platforms allow one to generate and store data that stand out, not only for their huge amount and heterogeneity, but above all, for their link to a time dimension that allows one to analyze and predict student behaviour in its dynamic context, among other purposes.

There are many interesting research lines that deserve to be explored in the education area, such as analyzing and predicting students' behaviour, developing advanced tools for supporting learning stages, recommending activities, predicting dropout, optimizing resources, etc. For these purposes, there are advanced methods from computational science that have demonstrated a high effectiveness when handling data and processes that are strongly interconnected. Data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to artificial intelligence, allow for the development of advanced techniques that provide a significant potential for the above purposes, leading to new applications and more effective approaches in academic analysis and prediction.

This Special Issue provides a collection of papers of original advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, data science, data analytics, big data, and machine learning, especially in the TEL context. Papers about these topics are welcomed.

Prof. Dr. Juan A. Gómez-Pulido
Prof. Dr. Young Park
Prof. Dr. Ricardo Soto
Prof. Dr. José M. Lanza-Gutiérrez
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 papers will be 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. Applied Sciences is an international peer-reviewed open access semimonthly 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 1800 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

  • Technology-enhanced learning and teaching
  • Personalized learning
  • Intelligent tutoring Systems
  • Data science and analytics
  • Data mining and big data analysis
  • Intelligent systems
  • Machine and deep learning
  • Recommender systems
  • Collaborative filtering
  • Deep learning-based recommendations
  • Review-based recommendations
  • Performance prediction
  • Knowledge analysis
  • Optimization

Published Papers

This special issue is now open for submission.
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