Data Collection on Educational Biographies of Students in Compulsory Education and Beyond |
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| Coordinator 1 | Dr Christoph Homuth (Leibniz Institute for Educational Trajectories (LIfBi)) |
| Coordinator 2 | Dr Monja Schmitt (Leibniz Institute for Educational Trajectories (LIfBi)) |
Understanding the educational trajectories of students is crucial for shaping effective educational policies and practices. This session invites researchers to explore the multifaceted process of data collection on educational biographies of students in compulsory education and beyond. We seek abstracts that delve into various aspects of this complex task, including but not limited to:
1. Lessons Learnt: What strategies proved successful in past projects, and what challenges were encountered? How were these challenges addressed?
2. Greatest Pain Points and How to Overcome Them: What are most significant obstacles in collecting educational biographical data? What are innovative solutions and methodologies that have been developed to mitigate these issues?
3. Dos and Don’ts: What practical advice can we give on what to do and what to avoid when collecting data on educational biographies.
4. Best Practices: What methods have yielded the most reliable and valid data? How can these practices be adapted to different contexts?
5. Register Data vs. Survey Data: What are the strengths and limitations of using register data or survey data? (How) can these data types be effectively integrated?
6. How to Get Reliable and Valid Data in Surveys: What techniques and strategies are there for ensuring the reliability and validity of survey data? What measures can be taken to enhance data quality?
7. Innovative Ways of Data Collection: How can technology and novel approaches be leveraged to gather comprehensive and accurate data?
We encourage submissions that provide a deep dive into these topics, offering valuable insights and practical guidance for researchers in the field. Join us in advancing the understanding of educational biographies and contributing to the development of robust data collection methodologies.