CV

Education

• B.S in Computer Science, Faculty of Computers and Information, Cairo University, 2018
• Masters of Engineering, Information Science and Electrical Engineering, Kyushu University, March 2022
• PhD, Information Science and Electrical Engineering, Kyushu University, expected March 2025

Work experience

• PhD Fellow
• Research Assistant at Mine Lab
  • ISEE, Kyushu University
  • Supervisor: Professor Tsunenori Mine
  • Worked on a Japanese NLP project to predict student grades using teacher observation reports using BERT.
• Summer 2017: Software Developer Intern
  • University of Cincinnati IT Solutions Center
  • Supervisor: Professor Hazem Said
  • Duties included: Built a cross-platform mobile application for the UC Corrections Institute. The app, made for youth in probation, allowed them to write down their thoughts, feelings and actions regarding situations that could lead to violence. The app gave immediate feedback on the user’s situations based on the potential consequences. (Cordova, Node.js, Javascript, MySQL, Bookshelf, Git)
• 2015-2017: Contestant at ACM Programming Contests
  • Cairo University
  • Competed at FCI Cairo University Programming Contest.
  • Competed at ACM Egyptian Collegiate Programming Contest and ranked 66 out of 159 teams (ICPC ID).
  • Participated in 25+ online contests. (Women’s Codesprint, Codeforces rounds)
  • Solved 275+ problems for practice using C++ and C.

Publications

Predicting Student Performance Using Teacher Observation Reports

Fateen, M., & Mine, T. (2021). Predicting Student Performance Using Teacher Observation Reports. International Educational Data Mining Society.

An Improved Model to Predict Student Performance using Teacher Observation Reports

Fateen, M., Ueno, K., & Mine, T. (2021) An Improved Model to Predict Student Performance using Teacher Observation Reports.

Extraction of Useful Observational Features from Teacher Reports for Student Performance Prediction

Fateen, M., & Mine, T. (2022) Extraction of Useful Observational Features from Teacher Reports for Student Performance Prediction.

Using Similarity Learning with SBERT to Optimize Teacher Report Embeddings for Academic Performance Prediction

Fateen, M., & Mine, T. (2023). Using Similarity Learning with SBERT to Optimize Teacher Report Embeddings for Academic Performance Prediction. In International Conference on Artificial Intelligence in Education (pp. 720-726). Cham: Springer Nature Switzerland.