In this survey, we study how the choice of problem domains in modelling education affect motivation and learning among students. In a parallel questionnaire, we are evaluating the views of educators in the same area. If you are a teacher and NOT a student, please use this survey link instead: https://www.soscisurvey.de/modelMotivation/?q=qnr3

INVESTIGATORS
This study is conducted by:

• Christopher Lazik, Humboldt-Universität zu Berlin, Germany. Email: lazikchr@informatik.hu-berlin.de
  
• Grischa Liebel, Reykjavik University, Iceland. Email: grischal@ru.is
• Isabella Grassl, University of Cambridge, UK. Email: ig466@cam.ac.uk
  
• Miguel Goulão, NOVA School of Science & Technology, Portugal. Email: mgoul@fct.unl.pt
  
• Shalini Chakraborty, University of Bayreuth, Germany. Email: shalini.chakraborty@uni-bayreuth.de

TARGET GROUP
We are looking for adult students enrolled in Computer Science or a related field of study at university. Additionally, participants should have taken courses that covered software modelling (e.g., UML, Business Process Modelling, or similar) at least in part.

STUDY PROCEDURES AND INFORMED CONSENT
By proceeding to the next page, you consent to the study procedures described below.

You will be presented with 6 pages of optional questions, in which we ask you about demographic data such as age and experience, followed by questions related to the study topic. Filling in this survey will take approximately 15-20 minutes.

RISKS
The are no particular risks associated with this study. However, we are available for each of your requests. You may decline to answer any or all questions. You may terminate your involvement at any time if you choose and without providing any explanation.

BENEFITS
The benefits of this research lie in a deeper understanding of how students experience modelling education. This enables educators to improve educational practices in Computer Science.

CONFIDENTIALITY and DATA PRIVACY
While the entire survey is anonymous, a combination of answers might make it possible to identify an individual (e.g., age group, country, as well as potential answers to free-text questions). On our end, we will handle this data as follows:

- Free-text answers that clearly de-anonymises a person will be anonymised immediately when data analysis begins. The original answer will be replaced with the anonymous answer.
- Country of work will be changed into the continent once analysis is completed, and only the continent retained.
- Free-text answers will be aggregated after analysis is completed, i.e., disconnected from the specific participant.
- The resulting dataset is anonymous in the sense of GDPR. We will publish the anonymised dataset in the interest of scientific transparency.

COMPENSATION
No compensation is offered for participating in this study.

VOLUNTARY PARTICIPATION
Your participation in this study is voluntary. If you decide to take part in this study, you will be asked to proceed to the next page. Even if you proceed, you are still free to withdraw at any time and without giving a reason. Withdrawing from this study will not affect the relationship you have, if any, with the researcher. If you withdraw from the study before submitting the survey, your data will be deleted.