学会員メーリングリストアーカイブ (2019年)

[dbjapan] 教育データ分析国際ワークショップのご案内


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。

LAK20(2020年3月23~24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。

12月15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。

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Call for Papers

LAK20 Workshop:
The 2nd Workshop on Predicting Performance Based on the Analysis of Reading Behavior (DC@LAK20)
March 23rd or 24th at LAK 2020 in Frankfurt, Germany.

Please see the workshop website to download the dataset and see more details:
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:::Important Dates:::

Initial paper submission: December 15, 2019
(This can be an outline of work in progress with preliminary analysis)
Notification of acceptance: January 5, 2020
Registration deadline: TBA
Camera-Ready deadline: TBA

:::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. Building on the success of last years workshop at LAK19, this year we will offer participants a chance to take part in a data challenge to predict the performance of 300 students based on the reading behaviors of over 1000 students from the previous year in the same course. Additional information on lecture schedules and syllabus will also enable the analysis of learning context for further insights into the preview, in-class, and review reading strategies that learners employ. Participant contributions will be collected as evidence in a repository provided by the workshop and will be shared with the wider research community to promote the development of research into reading analysis systems.

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 LAK2020 Companion Proceedings.

:::Organizing committee:::

Brendan Flanagan (Kyoto University, Japan)
Rwitajit Majumdar (Kyoto University, Japan)
Atsushi Shimada (Kyushu University, Japan)
Hiroaki Ogata (Kyoto University, Japan)

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Brendan Flanagan
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特定講師
京都大学学術情報メディアセンター
Program-Specific Senior Lecturer
Academic Center for Computing and Media Studies
Kyoto University
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