LEAF demo@ICCE2023
Interactive Event on Bridging Learning Analytics Research and Practice With LEAF System
held in conjunction with International Conference on Computers in Education (ICCE 2023)
13:20 -15:20, December 4th 2023 @Room A, Kunibiki Messe
Thank you for joining our interactive event on Data-driven learning with LEAF
In the interactive event, you can:
Get an overview of the data-driven infrastructure LEAF
Experience key modules of LEAF such as e-book reader, quiz module, etc.
Experience data-driven group learning based on GLOBE
Communicate with participants from all over the world
Get a souvenir from Ogata lab for your participation (AI-sensei key holder ===>)
...
Slide of LEAF introduction on Dec. 04
LEAF ICCE2023.pptx
Learning and Evidence Analytics Framework (LEAF)
The Learning and Evidence Analytics Framework (LEAF) is a comprehensive technical framework that seamlessly integrates research and production systems, facilitating educational data science research and AI-driven services for users User management is handled by the learning management system (LMS), and various tools, including the e-book reader, BookRoll, and the learning dashboard LogPallet, are connected through the Learning Tools Interoperability (LTI) protocol. Interactions within the LMS and tools are logged as standard xAPI statements in the Learning Record Store (LRS). The data stored in the LRS is then utilized for visualization in the learning dashboard and for creating various data-driven services.
demo: Group formation & Peer feedback with LEAF
During the interactive event, we will showcase how grouping can be easily done in the LEAF system based on learner model attributes that are captured in the learning activities. The following figure shows different learner model attributes which can be used to automatically create either homogeneous or heterogeneous groups within the data-driven group learning module in LEAF. Teachers can select any number of attributes and group sizes to create groups. Any students’ attributes that are external such as test scores (performance data) and survey responses (perception data) can be uploaded to the dashboard. The system also incorporates those attributes apart from the reading attributes computed from the BookRoll logs. Another function of the group work module is to assist peer and teacher evaluation with rating and feedback. All the evaluation and feedback data are also synchronized to the student model and further used for subsequent algorithmic grouping.
Agenda
Introduction and demonstration of components of the LEAF system [25 min]
Participants explore LEAF (BookRoll, LogPallet) and group formation based on learning logs [20-25 min]
Group discussion regarding learning material on human interaction design of educational tools with LEAF [25-30 min]
Presentations by groups to share ideas [25-30 min]
Wrap-up and future collaboration opportunities with the LEAF system [15 min]
Total time: around 2 hours
We are open to participants from both research and practice with multidisciplinary background. We plan to have on-site participants be part of the interactive event. Face-to-face discussions in group activity are encouraged, but we will keep provision for online participation. We will request the face-to-face participants to bring their personal computers for exploring LEAF system.
Organizing Committee
The organizers of this event are learning analytics researchers who are also developing and implementing the LEAF system from Learning and Educational Technologies Research Unit, Kyoto University (https://www.let.media.kyoto-u.ac.jp/en/).
Changhao Liang, Kyoto University, Japan
Yiling Dai, Kyoto University, Japan
Izumi Horikoshi, Kyoto University, Japan
Rwitajit Majumdar, Kyoto University, Japan
Hiroaki Ogata, Kyoto University, Japan