[AISWorld] CFP: ICALT2023: Track 6. Big Data in Education and Learning Analytics, deadline Feb 10, 2023

[Jelena Jovanovic]

Dear All,

Apologies for cross-posting!

The 23rd IEEE International Conference on Advanced Learning Technologies
(ICALT23) will be held on July 10-13, 2023 online:
https://tc.computer.org/tclt/icalt-2023/.
It is ranked as the *2nd by saliency in Educational Technology *area,
and *offers
student support for attending the conference*.

I am writing to invite you to submit to Track 6 on "Big Data in Education
and Learning Analytics (BDELA)". More information on this track is
available at:
https://tc.computer.org/tclt/icalt-2023-track-6-bdela/

***SUBMISSION TYPES***

All papers will be double-blindly peer-reviewed. Author guidelines and
formatting templates can be accessed at ICALT Author guidelines webpage (
https://tc.computer.org/tclt/icalt-2023-author-guidelines/). Complete
papers are required to be reviewed. The expected types of submissions
include:

Full paper: 5 pages
Short paper: 3 pages
Discussion paper: 2 pages
Doctoral Consortium paper: 3 pages

Proceedings accepted in Xplore are available to indexing partners,
including EI, Scopus, and Conference Proceedings Citation Index; these
indexing partners have final say on what they include and the process can
take anywhere between 4 and 12 months, depending on how busy the indexing
partner is at the time.

***IMPORTANT DATES (Pacific Time)***

*Feb 10, 2023 (Friday): Submissions of papers (Full paper, Short paper,
Discussion paper, Doctoral Consortium)*
April 7, 2023 (Friday): Authors’ Notification on the review process results
May 5, 2023 (Friday): Author’s registration deadline
May 5, 2023 (Friday): Final Camera-Ready Manuscript and IEEE Copyright Form
submission
May 19, 2023 (Friday): Non-authors’ early bird registration deadline
July 10-13, 2023: ICALT 2023 Conference

***Track description and topics of interest***

The analysis and discovery of relations characterizing human learning, and
contextual factors that influence these relations have been one of the
contemporary and critical global challenges faced by researchers in a
number of areas, particularly in Education, Psychology, Sociology,
Information Systems, and Computing. These relations typically focus on
learners’ achievements and the overall learning experience, and the
effectiveness of learning environments. Be it the assessment mark
distribution in a classroom context or the mined patterns of best practices
in an apprenticeship context, analysis and discovery have always addressed
the elusive causal question about the need to best serve learners’ learning
efficiency, learning effectiveness, as well as the overall quality of the
learning experience, and the need to make informed choices on improving
learning environments.

Significant advances have been made in a number of areas from educational
psychology to artificial intelligence in education, which explored factors
contributing to learners’ proactive role in the learning process and
instructional effectiveness. With the advent of new technologies such as
eye-tracking, activity monitoring, video analysis, computer vision, content
analysis, sentiment analysis, immersive worlds, social network analysis and
interaction analysis, new possibilities arise to study these factors in
data-intensive contexts. This very notion is what is currently being
explored at the intersection of big data and learning analytics, which
includes related areas such as learning process analytics, institutional
effectiveness, academic analytics, text/web analytics and information
visualization.

BDELA explores monitoring of learner progress and tracing of skill
development of individual learners as well as learning groups, both within
and across programs and institutions. It will discuss issues concerning
evaluation of achievements resulting from institutional educational
practices to gauge alignment with strategic plans at different levels. It
will examine assessment frameworks of academic productivity to measure
impact of teaching. It will discuss concerns such as quality of
instruction, attrition, and measurement of curricular outcomes using big
data and associated methods and techniques as the premise.

Topics include but are not limited to:

Big data theory, science and technology for education and learning

   - security, privacy, inclusivity, fairness and ethics of big data
   analytics
   - veracity in big data
   - scalability of machine learning and data mining algorithms for big data
   - big data infrastructure for academic institutions and education
   companies  – cloud, grid, autonomic, stream, mobile, high performance
   computing
   - search in big data
   - artificial intelligence in big data analytics
   - uncertainty handling in big data
   - Internet of Things (IoT) and big data analytics


Applications of big data in education and learning analytics

   - detecting student’s approach to learning
   - analytics in academic administration
   - data-informed learning and instructional design
   - gaming analytics and sports analytics
   - evidence-driven instruction in inter- and individual disciplines
   - analytics in academic strategic planning
   - cultural analytics
   - large-scale social networks
   - educational data literacy
   - technological literacy and analytics
   - human literacy and analytics


Techniques of big data in education, knowledge and learning analytics

   - emerging standards in learning analytics
   - analysis of unstructured and semi-structured data
   - sentiment analysis
   - social network analysis
   - multimodal learning analytics
   - large-scale productivity analysis
   - scalable knowledge management
   - research methods for learning analytics


Looking forward to your submissions. Very grateful for your support!


Jelena Jovanovic, PhD
Professor, University of Belgrade, Serbia
---------------
Adjunct Professor, Monash University, Australia
---------------
E-mail: jeljov at gmail.com, jelena.jovanovic at fon.bg.ac.rs
URL: jelenajovanovic.net <http://www.jelenajovanovic.net/>
Google Scholar: citation profile
<https://scholar.google.com/citations?user=2RDe9sYAAAAJ&hl=en>