日本データベース学会の皆様 （重複してお受け取りの際はご容赦ください） 京都大学のフラナガンと申します。 LAK19 （2019年3月4日～8日）で教育データ分析に関するワークショップが開催されます。 ワークショップサイトで公開されるデジタル教科書の学習ログデータを分析して，学習者の 成績予測や教科書の閲覧パターン解析，可視化など，参加者が独自の視点で分析した結果を 持ち寄って議論を行うことを目的としたワークショップです。京都大学，九州大学，亜州大学 （台湾）で同じシステムにより収集された学習ログが同じフォーマットで公開されますので， 教育機関を横断した分析も可能です。 12月3日の投稿時点では，分析の方針や3月のワークショップでの発表に向けた分析の過程段階で の投稿も受け付けておりますので，みなさまからのご投稿，ご参加をお待ちしております。 詳しくは，ワークショップのサイトをご覧ください。 -------------------------------------------------------------------------- LAK19 Workshop: Predicting Performance Based on the Analysis of Reading Behavior: A Data Challenge March 4~5th at LAK 2019 in Arizona State University, Tempe, USA. https://sites.google.com/view/lak19datachallenge -------------------------------------------------------------------------- :::Important Dates::: Initial paper submission: December 3, 2018 (This can be an outline of work in progress with preliminary analysis) Notification of acceptance: January 4, 2019 Registration deadline: January 8, 2019 Camera-Ready deadline: February 5, 2019 :::Workshop Overview::: As the adoption of digital learning materials in modern education systems is increasing, the analysis of reading behavior and their effect on student performance gains attention. The main motivation of this workshop is to foster research into the analysis of students’ interaction with digital textbooks, and find new ways in which it can be used to inform and provide meaningful feedback to stakeholders, such as: teachers, students and researchers. In this workshop, participants will be offered a chance to analyze the event logs from three different universities datasets with information on over 1000 students reading behaviors. Additional information on lecture schedules will also enable the analysis of learning context for further insights into the preview, in-class, and review reading strategies that learners employ. We welcome submissions on some of the following topics(though not restrictive): • Student performance/at-risk prediction • Student reading behavior self-regulation profiles spanning the entire course • Preview, in-class, and review reading patterns • Student engagement analysis; and behavior change detection • Visualization methods to inform and provide meaningful feedback to stakeholders Participants will also be encouraged to contribute their programs/source code created in the workshop to an ongoing open learning analytics tool development project for inclusion as an analysis feature. :::Publication::: All accepted papers will appear in the LAK2019 Companion Proceedings. :::Organizing committee::: Brendan Flanagan (Kyoto University, Japan) Atsushi Shimada (Kyushu University, Japan) Stephen Yang (National Central University, Taiwan) Bae-Ling Chen (Asia University, Taiwan) Yang-Chia Shih (Asia University, Taiwan) Hiroaki Ogata (Kyoto University, Japan) --- フラナガン ブレンダン ------------------------------------------ 特定講師 京都大学学術情報メディアセンター メール: flanagan.brendanjohn.4n@kyoto-u.ac.jp ------------------------------------------