Track 7. Big Data in Education and Learning Analytics (BDELA@ICALT2014)
|Vive Kumar, Athabasca University, Canada [Coordinator - email@example.com]|
|Jelena Jovanovic, University of Belgrade, Serbia|
|Riccardo Mazza, University of Lugano, Switzerland|
|Abelardo Pardo, University of Sydney, Australia|
|Miguel-Angel Sicilia, University of Alcala, Spain
Track Program Committee
- Jelena Jovanovic, University of Belgrade, Serbia
- Vive Kumar, Athabasca University, Canada,
- Riccardo Mazza, University of Lugano, Switzerland
- Abelardo Pardo, University of Sydney, Australia
- Miguel-Angel Sicilia, University of Alcala, Spain
- Mark Brown, Massey University, New Zealand
- Shane Dawson, University of South Australia, Australia
- Michael Derntl, RWTH Aachen University, Germany
- Stefan Dietze, L3S Research Center, Germany
- Alfred Essa, McGraw-Hill Education, USA
- Alejandra Martínez, University of Valladolid, Spain
- Negin Mirriahi, University of New South Wales, Australia
- Mimi Recker, Utah State University, USA
- Katrien Verbert, Technische Universiteit Einhoven, Holand
- Lanqin Zheng, Beijing Normal University, China
- Amal Zouaq, Royal Military College of Canada, Canada
- Vanda Luengo, University Joseph Fourier, France
- Christos Doulkeridis, University of Piraeus, Greece
- Anastasios Economides, University of Macedonia, Greece
Track Description and Topics of Interest
The analysis and discovery of relations between human learning and contextual factors that influence these relations have been one of the contemporary and critical global challenges facing researchers in a number of areas, particularly in Education, Psychology, Sociology, Information Systems, and Computing. These relations typically concern learner performance and the effectiveness of the learning context. Be it the assessment marks distribution in a classroom context or the mined pattern 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 and the need to make informed choices on a learning context’s instructional effectiveness. Significant advances have been made in a number of areas from educational psychology to artificial intelligence in education, which explored factors contributing to learning efficiency and instructional effectiveness. With the advent of new technologies such as eye-tracking, activities monitoring, video analysis, content analysis, sentiment analysis, social network analysis and interaction analysis, one could study these factors in a data-intensive fashion. This very notion is what is currently being explored under big data learning analytics, which includes related areas such as learning process analytics, institutional effectiveness, academic analytics, web analytics and information visualisation.
BDELA@ICALT2014 will explore continuous monitoring of learner progress and traces of skills development among individual learners across programs and institutions. It will discuss issues concerning continuous mapping of institutional learning related achievements to gauge alignment with strategic plans and alignment of governmental strategies. It will examine assessment frameworks of academic productivity to continuously measure impact of teaching. It will discuss concerns such as how quality of instruction, attrition, and measurement of curricular outcomes using big data as the premise.
Important dates about ICALT 2014 submissions can be found here.
The ICALT 2014 Author Guidelines can be found here.
The Track 7 CfP can be downloaded from here: