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The 21st IEEE International Conference on Advanced Learning
Technologies (ICALT21) will be held on July 12-15, 2021 online:
https://tc.computer.org/tclt/icalt2021/.
Track 6 is on "Big Data in Education and Learning Analytics
(BDELA)".
More information on this track is available at:
https://tc.computer.org/tclt/icalt-2021-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. 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
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***
January 29, 2021 (Friday): Submissions of papers (Full paper,
Short
paper, Discussion paper)
April 16, 2021 (Friday): Authors’ Notification on the review
process results
May 14, 2021 (Friday): Author’s registration deadline
May 14, 2021 (Friday): Final Camera-Ready Manuscript and IEEE
Copyright Form submission
June 4, 2021 (Friday): Non-authors’ early bird registration
deadline
July 12-15, 2021: ICALT 2021 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, content analysis, sentiment analysis, immersive worlds,
social network analysis and interaction analysis, new
possibilities
arise to study these factors in a data-intensive context. 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
- computing infrastructure for big data – cloud, grid,
autonomic, stream, mobile, high performance computing
- search in big data
- artificial intelligence in big data analytics
- uncertainty handling in big data
- 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
- big data infrastructure for academic institutions and SMEs
- scalable knowledge management
- observational research methods for analytics
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