LA@ICCE2021
8th Workshop on Learning Analytics (LA) Technologies & Practices for Evidence-based Education
held in conjunction with International Conference on Computers in Education (ICCE 2021)
23 November 2021, Online Virtual Conference
The increasing amount of data generated in digital learning contexts provides opportunities to benefit from learning analytics as well as challenges related to interoperability, privacy, and pedagogical and organizational models. As a consequence, new methodologies and technological tools are necessary to analyze and make sense of these data and provide personalized scaffolding and services to stakeholders including students, faculty/teachers and administrators, as well as parents. Pedagogical and organizational models must also be incorporated in order to take advantage of the personalized scaffolding and services to ensure productive learning and teaching. In addition, access to data from different sources raises a number of concerns related to data sharing and interoperability, and protection of privacy for individuals and business interests for institutions. The objective of the proposed workshop is to gather researchers as well as stakeholders, including educational technologists, researchers, and practitioners who are involved in the analysis and deployment process and to discuss the challenges and approaches for scaling up evidence- based institutional LA practices and integrating LA into STEAM education.
Analyzing evidence on the effectiveness of learning analytics can be carried out at a number of different levels within educational institutions. To conceptualize a framework of analyzing evidence of improvement in the teaching-learning practices through learning analytics, we look at three different levels as illustrated from an institutional perspective.
The Micro level looks at the impact on the individual students when LA systems or techniques are implemented in a single class or a single course.
The Meso level focuses on institutional implementations. In such cases, practice is adopted and evaluated by more than one faculty member.
The Macro level goes beyond one institution and involves policies or practices mandated by the state or national level regulatory body. Hence it looks at practices which are followed at multiple institutions.
The three levels of implementation and adoption (macro-meso-micro) of learning analytics each present unique problem, and therefore the evidence to be examined may be very different. This impacts the generalization of evidence, with many issues that need to be overcome as analysis is scaled up to include multiple institutions across different facets of education, such as: K-12, Higher Education, and corporate education in enterprises.
Participants are encouraged to share their research as a paper on either analyzing evidence on effective LA, or relating their contribution from the perspective of the three levels of implementation and adoption of LA. We also call for papers that cover technical, theoretical, pedagogical, as well as organizational issues in learning analytics. We also welcome submissions on some of the topics concerning LA from the following (though not restrictive) list:
Making sense of Learning Analytics
Software systems and tools
Implementation and organizational development
Pedagogical models and learning analytics
Gathering diverse learning data across platforms
Algorithms for analytics based on gathered data
Predictive models, visualization and statistical analysis
Privacy concerns and policy aspects related to Learning Analytics
Data sharing for learning analytics
Evaluation and Assessment
Standardization and Interoperability
Challenges and approaches for scaling up LA in education practices
Learning Analytics in Humanities and Design education
Accessible Learning Analytics
Learning Analytics to understand and support emergency remote teaching
Schedule
online schedule TBA.
Important Dates
Final paper submission: July 22, 2021 August 2, 2021 August 16, 2021
Notification of acceptance: August 2, 2021 August 16, 2021 August 30, 2021
Camera-Ready deadline: September 20, 2021
Author Registration deadline: September 24, 2021
Submissions
All submissions to the workshop must follow the format of the Proceedings Template
https://icce2021.apsce.net/wp-content/uploads/2021/02/ICCE2021-PaperTemplate.doc
Submission Categories
Full paper: 8-10 pages
Short paper: 5-6 pages
Discussion paper: 2-3 pages
Submit papers using EasyChair: https://easychair.org/conferences/?conf=laicce2021
Publication
All accepted papers will be published in one volume of workshop proceedings, which will be submitted to Elsevier for inclusion in SCOPUS. Also, published workshop papers will be made available on the official ICCE 2021 website.
Organizing Committee
Huiyong Li, Kyoto University, Japan (li.huiyong.2t@kyoto-u.ac.jp)
Huiyong Li is a Program-Specific Researcher at the Academic Center for Computing and Media Studies in Kyoto University. He received a Ph.D in Informatics at Kyoto University. His research interests include: learning analytics, self-regulated learning, and self-directed learning.
Rwitajit Majumdar, Kyoto University, Japan (majumdar.rwitajit.4a@kyoto-u.ac.jp)
Rwitajit Majumdar is a Program-Specific Senior-Lecturer at the Academic Center for Computing and Media Studies in Kyoto University. He received a Ph.D in Educational Technology at Indian Institute of Technology Bombay before moving to Kyoto as a Postdoc. His research interests include: learning analytics, visual analytics and HCI. He co-organised the ICCE workshop on LA in Kenting and Darwin.
Brendan Flanagan, Kyoto University, Japan (flanagan.brendanjohn.4n@kyoto-u.ac.jp)
Brendan Flanagan is a Program-Specific Senior-Lecturer at the Academic Center for Computing and Media Studies, and the Graduate School of Informatics, Kyoto University. He received a Ph.D in Information Science at Kyushu University before moving to Kyoto as a Postdoc. His research interests include: learning analytics, text mining, machine learning, and language learning.
Weiqin Chen, Oslo Metropolitan University, Norway (weiqin.chen@oslomet.no)
Weiqin Chen is a professor in Computer Science. She received her Ph.D in the Chinese Academy of Sciences and worked as a researcher in Osaka University before moving to Norway. Her research interests include pedagogical agents, computer supported collaborative learning, and learning analytics. She has served in different roles for many years in APSCE and the organisation of ICCE conferences. She co-organised the first seven ICCE workshop on LA.
Hiroaki Ogata, Kyoto University, Japan (ogata.hiroaki.3e@kyoto-u.ac.jp)
Hiroaki Ogata is a Professor at the Academic Center for Computing and Media Studies, and the Graduate School of Informatics, Kyoto University, Japan. His research includes Computer Supported Ubiquitous and Mobile Learning, CSCL, CSCW, CALL, and Learning Analytics. He has published more than 300 peer-reviewed papers including SSCI Journals and international conferences. He has received several Best Paper Awards and given keynote lectures in several conferences all over the world.
Program Committee
Gökhan Akçapınar, Hacettepe University, Turkey
Muhammad Nehal Hasnine, Hosei University, Japan
Shitanshu Mishra, Indian Institute of Technology Bombay, India
Jayakrishnan Warriem, Indian Institute of Technology Madras, India
Ramkumar Rajendran, Indian Institute of Technology Bombay, India
Rekha Ramesh, Mumbai University, India
Kyosuke Takami, Kyoto University, Japan
Yiling Dai, Kyoto University, Japan
Atsushi Shimada, Kyushu University, Japan
Victoria Abou Khalil, Kyoto University, Japan
Mei-Rong Alice Chen, National Taiwan University of Science and Technology, Taiwan