ICCE2024 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 ICCE18, LAK19 and LAK20 focused on reading behavior in higher education, and LAK21, LAK22, LAK23 and LAK24 on secondary school reading behavior, pre/post COVID-19 pandemic changes and students’ coding behavior. Participants of this year’s workshop at ICCE2024 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, and so on.

While we welcome research questions from all participants, and we expect to emphasize the following topic which the organizers feel attention should be paid. Low retention and high failure rates are important problems in education. However, studies have shown that timely interventions for at-risk students can be effective in helping change their behaviors. Therefore, focusing on the early detection of at-risk students is an essential step to changing students’ behavior for greater success. This broader task may be approached from the following perspectives: 

• 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

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, and so on.

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=dcicce24.

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.

Important Dates

• Initial paper submission: 19th August, 2024

• Notification of acceptance: 2nd September, 2024

• Registration deadline: 16th September, 2024

• Camera-Ready deadline: 16th September, 2024

Schedule

TBA

Program

TBA

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=dcicce24

Template

All submissions to the workshop must follow the format of the Proceedings Template (download). 

Organizing Committee

• Brendan Flanagan (Kyoto University, Japan)

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

• Atsushi Shimada (Kyushu University, Japan)

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

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

• Fumiya Okubo (Kyushu University, Japan)

• 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.