Data Challenge

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: teachers, students and researchers. The previous years workshops at LAK19 and LAK20 focused on reading behavior in higher education, and LAK21, LAK22, LAK23 and LAK24 on secondary school reading behavior and pre/post COVID-19 pandemic changes. Participants of this year’s workshop will be given the opportunity to analyze several different datasets, including secondary school prediction of academic performance for more than one subject. As with previous years, 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. In addition, this workshop will accept a wide range of research topics on learning analytics, educational technology, and learning support systems in the post COVID-19 era, including applications of AI in education, proposals for new educational systems, new evaluation methods.

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 be encouraged to share their results and insights of analyzing the provided data or other research related to reading behavior analysis by submitting a paper for presentation at the workshop https://easychair.org/conferences/?conf=dclak25.

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.

Proceedings

The workshop proceedings will be made available on CEUR-WS.

Important Dates

• Initial paper submission: 4th December, 2024

• Notification of acceptance: 20th January, 2024

Schedule

• Monday March 3rd, 2025 at LAK 2025.

Program

Presentation Session (Morning):

• Opening 

• Towards Knowing Students' Emotional States from Their Voices while Interacting with Teachers
  Authors: Ho Tan Nguyen, Mohammad Nehal Hasnine and Hiroshi Ueda

• A Framework for Constructing Concept Maps from E-Books Using Large Language Models: Challenges and Future Directions
  Authors: Boxuan Ma and Li Chen

Tutorial Session:

• Reading Behavior Data Analysis: introduction and hands-on workshop

• Closing

Submissions

Initial paper submissions should at least give an outline of work in progress with some preliminary analysis.

Research track: Paper submissions should be fully finalized papers.

• Full paper: 8-10 pages (initial paper submission: 5 pages or more)

• Short paper: 5-6 pages (initial paper submission: 3 pages or more)

Submit papers using EasyChair: https://easychair.org/conferences/?conf=dclak25

Template

All submissions to the workshop must follow the format of the Workshop Proceedings Template (download). The workshop proceedings will not be published in the LAK companion proceedings this year (LAK25 policy), and instead will be published through CEUR-WS (http://ceur-ws.org/).

Organizing Committee

• Brendan Flanagan (Kyoto University, Japan)

• Owen H.T. Lu (National Chengchi University, Taiwan)

• Atsushi Shimada (Kyushu University, Japan)

• Namrata Srivastava (Vanderbilt University, USA)

• Albert C.M. Yang (National Chung-Hsing University, Taiwan)

• Hsiao-Ting Tseng (National Central University, Taiwan)

• Fumiya Okubo (Kyushu University, Japan)

• Eduardo Davalos Anaya (Vanderbilt University, USA)

• Hiroaki Ogata (Kyoto University, Japan)

PC Members

• TBA

For more information about BookRoll and the learning analytics platform on which the data was collected, please refer to the following:

• Brendan Flanagan, Hiroaki Ogata, Learning Analytics Platform in Higher Education in Japan, Knowledge Management & E-Learning (KM&EL), Vol.10, No.4, pp.469-484, 2018.

• Lu, O.H., Huang, A.Y., Flanagan, B., Ogata, H., & Yang, S.J. (2022). A Quality Data Set for Data Challenge: Featuring 160 Students’ Learning Behaviors and Learning Strategies in a Programming Course. In 30th International Conference on Computers in Education. (in press)

• Digital teaching material delivery system "BookRoll"

• Hiroaki Ogata, Misato Oi, Kousuke Mohri, Fumiya Okubo, Atsushi Shimada, Masanori Yamada, Jingyun Wang, and Sachio Hirokawa, Learning Analytics for E-Book-Based Educational Big Data in Higher Education, In Smart Sensors at the IoT Frontier, pp.327-350, Springer, Cham, 2017.