International Conference on Learning Analytics in Asia
Toward Technology-Enhanced and Evidence-based Teaching and Learning
It is very important to utilize data and evidence related to education and learning activities effectively in order to promote research for improving education and for nurturing human resources. 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.
As the field of learning analytics continues to mature, it is increasingly important to analyze evidence of effective LA implementations and facilitate dissemination through the community throughout Asia. This conference aims to bring together the LA community in Asia to discuss unique challenges faced in the region and foster developments in the field.
Dates: August 7-9, 2020
Venue: Kyoto Research Park, Kyoto, Japan
We had welcome submissions on any of the following topics (though not restrictive):
- Making sense of learning analytics
- Software systems and tools
- Implementation and organizational development
- Pedagogical models and learning analytics
- Gathering and analyzing multimodal learning data
- Algorithms for analytics based on gathered data
- Predictive models, visualization and statistical analysis
- Privacy concerns and policy aspects related to LA
- Data sharing for learning analytics
- Evaluation and Assessment
- Standardization and Interoperability
- Challenges and approaches for scaling up LA in education practices
- Exploring critical factors that affect students’ learning performance
- Estimating the influence of prevention or intervention
- Psychology-supported learning analytics
Main conference track
- Full paper: 6 pages
- Short paper: 4 pages
- Poster paper: 2 pages
Paper format: All submissions to the main conference must follow the format of the Proceedings Template
Important Dates (tentative)
Submission due date: end of March 2020
Review notification: end of April 2020
Camera-ready: end of May 2020
All submissions to the research track will undergo a double blind peer review by at least 3 reviews. Workshop and tutorial proposals will go through a single blind peer review process.
Some of the high quality papers will be invited to submit the special issue on Precise Education in Education Technology and Society Journal (deadline will be Sep 1, 2020)
Pre-conference event track
Workshops (4 pages) provide an efficient forum for community building, sharing of perspectives, and idea generation for specific and emerging research topics or viewpoints. Proposals should be explicit regarding the kind of activity participants should expect, for example from interactive/generative participatory sessions to mini-conference or symposium sessions.
Tutorials (4 pages) aim to educate stakeholders on a specific learning analytics topic or stakeholder perspective. Proposals should be clear about the need for the particular knowledge, the target audience and their prior knowledge, and the intended learning outcomes.
August 7: Friday
9:00 - 17:00 Workshop sessions
17:30 Welcome Reception
August 8: Saturday
9:00 Opening Ceremony
10:30 - 17:00 Main conference session
August 9: Sunday
9:00 Panel Discussion
10:00-16:30 Main conference session
16:30-17:00 Closing Ceremony
Institute for Research Excellence of Learning Sciences and Program of Learning Sciences, National Taiwan Normal University, Taiwan
Current trends and future suggestions of learning analytics research
The advancement of technology allows researchers to collect much more fine-grained data from a large sample size of learners. A variety of learning analytics techniques or approaches are employed to get a deeper understanding of student learning process and how this is related to learning outcome. In this talk, a series of learning analytics papers will be reviewed. The current trends of learning analytics approaches will be discussed such as data-driven approach versus theory-driven approach. Based on these trends, future suggestions are proposed for the field of learning analytics for researchers.