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. 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 reaserch 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. 1

Each paper submitted to the workshop underwent a rigorous double-blind review by at least two reviewers. Each paper was evaluated with respect to four criteria: 1) quality of content, 2) significance for theory and practice, 3) originality and level of innovativeness, 4) fitting to the workshop theme. The review results of each paper were subsequently discussed by the workshop chairs resulting in a decision of acceptance or rejection. As a result, we accepted 11 papers to be published in this workshop. We thank the authors for their submissions and the program committee for their hard work.