Data Challenge

Predicting Performance Based on the Analysis of Reading and Learning 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: 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):

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

Schedule

Program

Session 1 (12 minutes presentation, 3 minutes discussion)

Break (20 minutes)

Session 2 (12 minutes presentation, 3 minutes discussion)

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.

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

PC Members

Dataset

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

There are two main types of datasets available for analysis (click the links for more details):

OpenLA: Open-source library for e-Book Log Analysis

We provide a Python library that can read log files, extract data, transform data, and perform simple visualization for BookRoll provided in this contest. (Developed by: Image and Media Understanding Laboratory, Kyushu University)

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

Support