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 and LAK22 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.

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=dclak23.

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: 16th December, 2022 9th January, 2023

• Notification of acceptance: 19th January, 2023

• Registration deadline: 27th January, 2023

• Camera-Ready deadline: TBA

Schedule

• 13th March at LAK 2023.

Program

March 13rd CST time zone (March 14th JST time zone in brackets)

20:00 - 20:15 (JST 10:00 - 10:15) - Opening (Brendan Flanagan, Fumiya Okubo, Owen Lu)

Session 1

• 20:15 - 20:45 (JST 10:15 - 10:45) - Applying an interpretable and accurate model to Learning analytics,
  Nelson Baloian, Javier Cobaise, Sergio Peñafiel, Brendan Flanagan, Rwitajit Majumdar and Hiroaki Ogata (PDF)

• 20:45 - 21:15 (JST 10:45 - 11:15) - LSTM with Attention Mechanism for Students’ Performance Perdiction,
  Sukrit Leelaluk, Tsubasa Minematsu, Yuta Taniguchi, Fumiya Okubo, Takayoshi Yamashita and Atsushi Shimada (PDF)

Break

Session 2

• 21:30 - 22:00 (JST 11:30 - 12:00) - Analyzing Student Programming Propensity with SHAP to Classify Future Performance,
  Adrian Li Li, Min-Jia Li, Matthew Bobea, Anna Y.Q. Huang, Stephen J.H. Yang and Owen H.T. Lu (PDF)

• 22:00 - 22:30 (JST 12:00 - 12:30) - Improving the Prediction Accuracy of Student Performance in a Cross-Semester Scenario Utilizing a Domain Adaptation Approach,
  Matthew Bobea, Soyeong Park, Brendan Flanagan and Hsin Tse Lu (PDF)

22:30 - 22:55 (JST 12:30 - 12:55) Discussion & Wrap-up session

22:55 - 23:00 (JST 12:55 - 13:00) - Closing

Submissions

Data challenge track: 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 (data challenge initial paper submission: 6 pages or more)

• Short paper: 5-6 pages (data challenge initial paper submission: 4 pages or more)

• Poster paper: 2-3 pages (data challenge initial paper submission: 1 page or more)

Submit papers using EasyChair: https://easychair.org/conferences/?conf=dclak23

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 (LAK22 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)

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

• Stephen J.H. Yang (National Central University, Taiwan)

• 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.