Title: Learning analytics and evidence-based K12 education in Japan: usage of data-driven services for mobile learning across two years

Authors: Hiroaki Ogata; Rwitajit Majumdar; Brendan Flanagan; Hiroyuki Kuromiya

Addresses: Academic Center for Computing and Media Studies, Kyoto University, Yoshida Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan ' Academic Center for Computing and Media Studies, Kyoto University, Yoshida Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan ' Academic Center for Computing and Media Studies, Kyoto University, Yoshida Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan ' Graduate School of Informatics, Kyoto University, Yoshida Nihonmatsu-cho, Sakyo-ku, Kyoto 606-8501, Japan

Abstract: Learning and evidence analytics framework (LEAF) is a technology framework for data-driven services in education. LEAF helps to form an educational eco-system with new digital technologies with capabilities such as integrating AI-driven models for learning recommendations and connecting learning logs through blockchain technologies to support lifelong learning. Since 2018, its implementation in Japan has led to more than 1,000 students of Japanese public schools using LEAF on mobile tablets for their daily learning activities both inside and outside school. The data collected at the school level in LEAF further enabled the creation of computational models to support teaching and self-learning. This article presents the data-driven services built on the platform and how it was used in Japanese K-12 Mathematics and English classes. This study evaluates the usage and user perception of data-driven educational practices in the Japanese context and discusses its greater implications and challenges for learning analytics research.

Keywords: evidence-based education; learning analytics; K-12 education; e-book platform; AI-driven services; Japanese school; learning and evidence analytics framework; LEAF; BookRoll; Japan.

DOI: 10.1504/IJMLO.2024.135123

International Journal of Mobile Learning and Organisation, 2024 Vol.18 No.1, pp.15 - 48

Received: 14 Dec 2021
Accepted: 31 Jan 2022
Published online: 01 Dec 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article