Data Challenge

Predicting Performance Based on the Analysis of Reading Behavior

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. In this workshop, participants will be offered a chance to analyze the event logs from three different universities datasets with information on over 1000 students reading behaviors. Additional information on lecture schedules will also enable the analysis of learning context for further insights into the preview, in-class, and review reading strategies that learners employ.

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

Aligned with LAK’19 interest in “ways in which learning analytics can be used to promote inclusion and success”, in this workshop we will pay special attention to how different groups within the student cohort can be identified and analysis of reading logs can provide hints for how groups can be scaffolded appropriately to achieve success.

Participants will be encouraged to share their results and insights by submitting a paper for presentation at the workshop

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.


  • March 5th at LAK 2019 in Tempe, USA.


Presentation time for full and short papers is as follows:

  • (F) Full Paper: 25 min presentation + 5 min Q&A
  • (S) Short Paper: 15 min presentation + 5 min Q&A

9:00 - 9:10 Welcome (Brendan Flanagan)

9:10 - 10:00 Paper Session 1 (Chair: Tsubasa Minematsu)

  1. Investigating Reading Behaviors within Students Reading Sessions to Predict their Performance (Jihed Makhlouf and Tsunenori Mine) (F) PDF
  2. Investigating Subpopulation of Students in Digital Textbook Reading by Clustering (Christopher Yang, Brendan Flanagan, Gökhan Akçapınar and Hiroaki Ogata) (S) PDF

10:00 - 10:30 Coffee Break

10:30 - 12:00 Paper Session 2 (Chair: Brendan Flanagan)

  1. Using machine learning to explore the associations among e-reader operations and their predictive validity of learning performance (Mei-Wen Nian, Yuan-Hsuan Lee and Jiun-Yu Wu) (S) PDF
  2. Characterization of Fuzziness in Sequence-based Grade Prediction (Hongrui Kelvin Ng Ng, Sivanagaraja Tatinati Tatinati and Andy Wai Hoong Khong Khong) (S) PDF
  3. Using Learning Analytics to Detect Off-Task Reading Behaviors in Class (Gökhan Akçapınar, Mohammad Nehal Hasnine, Rwitajit Majumdar, Brendan Flanagan and Hiroaki Ogata) (S) PDF
  4. Feature Engineering for Learning Log Analysis (Sachio Hirokawa and Chengjiu Yin) (F) PDF

12:00 - 13:00 Lunch

13:00 - 14:30 Paper Session 3 (Chair: Chengjiu Yin)

  1. Analytics of the relationship between quiz scores and reading behaviors in face-to-face courses (Tsubasa Minematsu, Atsushi Shimada and Rin-Ichiro Taniguchi) (S) PDF
  2. How Students Flip Pages during Lectures? -Comparison between Power Users and Normal Users- (Takuro Owatari and Atsushi Shimada) (S) PDF
  3. Extracting E-book Reading Patterns using Stochastic Block Model (Kanishka Khandelwal and Hiroshi Tamano) (F) PDF
  4. BoB: A Bag of eBook Click Behavior Based Grade Prediction Approach (Alexander Askinadze, Matthias Liebeck and Stefan Conrad) (S) PDF

14:30 - 15:00 Coffee Break

15:00 - 16:30 Session 4 (Chair: Atsushi Shimada)

  1. An Investigation of Academic Performance, Mindless Reading and Its Reading Behavior Indicators (Michelle Banawan) (F) PDF
  2. Learning Analytics to Share and Reuse Authentic Learning Experiences in a Seamless Learning Environment (Mohammad Nehal Hasnine, Hiroaki Ogata, Gökhan Akçapınar, Kousuke Mouri and Noriko Uosaki) (F) PDF
  3. Knowledge Map Creation for Modeling Learning Behaviors in Digital Learning Environments (Brendan Flanagan, Rwitajit Majumdar, Gökhan Akçapınar, Jingyun Wang and Hiroaki Ogata) (F) PDF

16:30 - 17:00 Discussion, Brain Storming and Future work

Important Dates

  • Initial paper submission: December 3, 2018 (This can be an outline of work in progress with preliminary analysis)
  • Notification of acceptance: January 4, 2019
  • Registration deadline: January 8, 2019
  • Camera-Ready deadline: January 31, 2019


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

  • Full paper: 8-10 pages (Initial paper submission: 6 pages or more)
  • Short paper: 5-6 pages (Initial paper submission: 4 pages or more)
  • Poster paper: 2-3 pages (Initial paper submission: 1 page or more)

Submit papers using EasyChair:

All submissions to the workshop must follow the format of the Companion Proceedings Template (

Organizing Committee

  • Brendan Flanagan (Kyoto University, Japan)
  • Atsushi Shimada (Kyushu University, Japan)
  • Stephen Yang (National Central University, Taiwan)
  • Bae-Ling Chen (Asia University, Taiwan)
  • Yang-Chia Shih (Asia University, Taiwan)
  • Hiroaki Ogata (Kyoto University, Japan)

PC Members

  • Gökhan Akçapınar (Kyoto University)
  • Chester S. J. Huang (National Kaohsiung University of Science and Technology)
  • Mohammad Nehal (Kyoto University)
  • Jiun-Yu Wu (National Chiao Tung University)
  • Tsubasa Minematsu (Kyushu University)
  • Shitanshu Mishra (Vanderbilt University)
  • Hui-Chun Hung (Taipei Medical University)
  • Yuichi Ono (University of Tsukuba)
  • Fumiya Okubo (Takachiho University)
  • Sachio Hirokawa (Kyushu University)
  • Masanori Yamada (Kyushu University)
  • Rekha Ramesh (Mumbai University)
  • Yuta Taniguchi (Kyushu University)
  • Jingyun Wang (Kyushu University)
  • Erlend Øverby (Karde AS)
  • Rwitajit Majumdar (Kyoto University)
  • Kousuke Mouri (Tokyo University of Agriculture and Technology)
  • Ivica Boticki (University of Zagreb)
  • Addison Su (National Central University)


By downloading our dataset and using our dataset you have agreed to our Terms of Use.

The dataset for this data challenge includes 4 types of files:

   - Data of the logged activity data from students' interactions with the BookRoll system.

   - Information about the length of the lecture materials used.

   - Information about the schedule of the lectures. This can be used to analyze the preview/in-class/review reading behaviors.

   - Data on the final score for each student. This can be used as a label for training and testing prediction models.

For a more description of the columns, please refer to the README file in the dataset download.

A link to download the dataset will be provided after your contact information has been registered and agreement with the terms of use have been met.

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

  • Hiroaki Ogata, Chengjiu Yin, Misato Oi, Fumiya Okubo, Atsushi Shimada, Kentaro Kojima, and Masanori Yamada, E-Book-based learning analytics in university education, Proceedings of the 23rd International Conference on Computer in Education (ICCE 2015) pp.401-406, 2015.
  • Digital teaching material delivery system "BookRoll"
  • Brendan Flanagan, Hiroaki Ogata, Integration of Learning Analytics Research and Production Systems While Protecting Privacy, Proceedings of the 25th International Conference on Computers in Education (ICCE2017), pp.333-338, 2017.
  • 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.