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 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:

Schedule

Important Dates

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

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 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 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 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 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 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