-------- Forwarded Message -------- Subject: [AISWorld] CFP: ICALT2021 Track 6. Big Data in Education and Learning Analytics, deadline Jan. 29, 2021 Date: Mon, 18 Jan 2021 08:37:47 +0100 From: Jelena Jovanovic jeljov@gmail.com To: aisworld@lists.aisnet.org

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 _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org