[hpc-announce] SI on Data Analytics and Machine Learning in Education. AppSci (IF 2.217)

Juan A. Gómez-Pulido jangomez at unex.es
Tue Jun 16 05:19:43 CDT 2020


Special Issue "Data Analytics and Machine Learning in Education"

Applied Sciences
ISSN: 2076-3417 / Impact factor (2018): 2.217
Section "Computing and Artificial Intelligence"


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

Prof. Dr. Juan A. Gómez-Pulido
Prof. Dr. Young Park
Prof. Dr. Ricardo Soto
Prof. Dr. José M. Lanza-Gutiérrez
Guest Editors


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

Deadline for manuscript submissions:

31 July 2021

Special Issue Editors:

Prof. Dr. Juan A. Gómez-Pulido
Department of Technologies of Computers and Communications, Universidad de
Extremadura, Cáceres, Spain

Prof. Dr. Young Park
Department of Computer Science and Information Systems, Bradley University,
Peoria, IL 61625, USA

Prof. Dr. Ricardo Soto
School of Computer Engineering, Pontificia Universidad Católica de
Valparaíso, Valparaiso, Chile

Prof. Dr. José M. Lanza-Gutiérrez
Department of Electronic Technology, Carlos III University of Madrid,
Leganés, Spain

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. 

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