Dr. Sarah Alturki completed her Ph.D. in Artificial Intelligence at the University of Mannheim, Germany, in December 2022, where her thesis focused on predicting student performance in interdisciplinary programs using educational data mining techniques. Her research interests are primarily in the field of educational data mining, student performance prediction, and the application of artificial intelligence in education. She has contributed to several notable publications on these topics.
In her teaching role, Dr. Sarah Alturki has taught a range of courses, including Software Engineering, Introduction to Programming, Introduction to networking and Project Management courses. She is dedicated to providing a solid foundation in these subjects while integrating practical applications and real-world examples to enhance student engagement and learning. Her teaching philosophy emphasizes interactive learning, critical thinking, and the application of technology to solve complex problems. She is committed to promoting the development of both technical skills and soft skills among her students, preparing them for the demands of the modern workplace.
Before her current role, Dr. Sarah Alturki held academic positions at Princess Nourah Bint Abdul Rahman University and served as a research assistant at the University of Mannheim.
- 1. Alturki, S., Cohausz, L., & Stuckenschmidt, H. (2022). Predicting Master’s Students' Academic Performance: An Empirical Study in Germany. Smart Learning Environments, 9(38). https://doi.org/10.1186/s40561-022-00220-y
- 2. Alturki, S., Alija, S., & Stuckenschmidt, H. (2022). Online Delivery in Higher Education during Pandemics: Students’ Perspective. Technology Education Management Informatics Journal, 11(2), 882–892. https://doi.org/10.18421/TEM112‐49
- 3. Alturki, S., & Stuckenschmidt, H. (2021). Assessing Students' Self-Assessment Ability in An Interdisciplinary Domain. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-01-2021-0034
- 4. Alturki, S., Alturki, N., & Stuckenschmidt, H. (2021). Using Educational Data Mining to Predict Students’ Academic Performance for Applying Early Interventions. Journal of Information Technology Education: Innovations in Practice.
- 5. Alturki, S., Hulpus, I., & Stuckenschmidt, H. (2020). Predicting Academic Outcomes: A Survey from 2007 till 2018. Technology, Knowledge and Learning.
