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 and LAK23 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, and a multi-source dataset for higher education programming classes consisting of reading behavior and coded programming logs. 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. 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. 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. 

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=dclak24.

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 are available on CEUR-WS as a part of the LAK24 joint workshop proceedings.

Important Dates

• Initial paper submission: 4th December, 2023 15th December, 2023

• Notification of acceptance: 8th January, 2024 12th January, 2024

• Registration deadline: 29th January, 2024

• Camera-Ready deadline: 29th January, 2024

Schedule

• 18th March, 1:30 PM TO 5:00 PM JST at LAK 2024.

Program

• 1:30 PM Opening

Session 1 (12 minutes presentation, 3 minutes discussion)

• 1:40 PM To 1:55 PM JST - Trustworthy and Explainable AI for Learning Analytics,
  Min-Jia Li, Shun-Ting Li, Albert C. M. Yang, Anna Y.Q. Huang and Stephen J.H. Yang

• 1:55 PM To 2:10 PM JST - Using a Hierarchical Clustering Algorithm to Explore the Relationship Between Students' Program Debugging and Learning Performance,
  Chao-Hung Liu and Ting-Chia Hsu

• 2:10 PM To 2:25 PM JST - Predicting Learning Achievement through Self-Regulated Learning Strategies, Motivation, and Programming Behaviors,
  Pei-Xuan Wang and Ting-Chia Hsu

• 2:25 PM To 2:40 PM JST - Personalized Navigation Recommendation for E-book Page Jump,
  Boxuan Ma, Li Chen and Min Lu

• 2:40 PM To 2:55 PM JST - Enhancing Personalized Learning with MBTI Forecasts and ChatGPT's Tailored Study Advice,
  Yi-Chun Hsieh and Albert C.M. Yang

Break (20 minutes)

Session 2 (12 minutes presentation, 3 minutes discussion)

• 3:15 PM To 3:30 PM JST - Educational Data Analysis using Generative AI,
  Abdul Berr, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo and Atsushi Shimada

• 3:30 PM To 3:45 PM JST - The Feasibility of Utilizing ChatGPT in Learning Analytics for the Identification of At-Risk Students,
  Zhi Qi Liu, Owen H.T. Lu and Hsiao-Ting Tseng

• 3:45 PM To 4:00 PM JST - Explore the Explanation and Consistency of Explainable AI in the LBLS Data Set,
  Tiffany T.Y. Hsu and Owen H.T. Lu

• 4:00 PM To 4:15 PM JST - A Deep learning Grade Prediction Model of Online Learning Performance Based on knowledge learning representation,
  Shuaileng Yuan, Sukrit Leelaluk, Cheng Tang, Li Chen, Fumiya Okubo and Atsushi Shimada

• 4:15 PM To 4:30 PM JST - Classroom Monitoring using Emotional Data,
  Mohammad Nehal Hasnine, Ho Tan Nguyen, Gökhan Akçapınar, Ryugo Morita and Hiroshi Ueda

• 4:30 PM To 4:45 PM JST - Chatbots and English as a Foreign Language Learning: A Systematic Review,
  Steve Woollaston, Brendan Flanagan and Hiroaki Ogata

4:45 PM To 5:00 PM JST - Discussion & Wrap-up session

5:00 PM JST - 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=dclak24

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 (LAK24 policy), and instead will be published through CEUR-WS (http://ceur-ws.org/).

Organizing Committee

• Brendan Flanagan (Kyoto University, Japan)

• Atsushi Shimada (Kyushu University, Japan)

• Fumiya Okubo (Kyushu University, Japan)

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

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

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

• Hiroaki Ogata (Kyoto University, Japan)

PC Members

• Anna Huang (NCU, Taiwan)

• Christopher Yang (Kyoto University, Japan)

• Hiroki Nakayama (Yamagata University, Japan)

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

• Mohammad Nehal (Hosei University, Japan)

• Taisuke Kawamata (Seikei University, Japan)

• Yosuke Morimoto (The Open University of Japan, Japan)

• 上田 浩 (Hosei University, Japan)

• 宮崎 誠 (Teikyo University, Japan)

• 畠山 久 (Tokyo Institute of Technology, Japan)

• 浜元 信州 (Gunma University, Japan)

• 重田 勝介 (Hokkaido University, Japan)

• 久保田 真一郎 (Kumamoto University, Japan)

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.