Michael Ashenafi

Download Hi-Res Photo

Project Scientist, Penn Center for Learning Analytics

Contact

mogessie@upenn.edu

(412) 419-6800

Education

Ph.D. (Information Technology) University of Trento, 2017
M.S.C. (Computer Science) University of Trento, 2012
B.Sc. (Information Systems) Addis Ababa University, 2006

Areas of Expertise

Educational Technology

Programs

Data Science Methods for Digital Learning Platforms

Bio Nav

Biography

Professional Biography

Dr. Ashenafi’s work at the Penn Center for Learning Analytics focuses on the creation of cutting-edge educational software and insightful learning analytics tools. Notably, he recently worked on the development of the MORF 2.0 platform, a pioneering solution enabling researchers to securely access and analyze data from MOOCs while safeguarding learner privacy.

Publications

Selected Publications

Hutt, S., Baker, R. S., Ashenafi, M. M., Andres‐Bray, J. M., & Brooks, C. (2022). Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data. British Journal of Educational Technology, 53(4), 756–775. https://doi.org/10.1111/bjet.13231

McLaren, B. M., Richey, J. E., Nguyen, H. A., & Mogessie, M. (2022). A digital learning game for mathematics that leads to better learning outcomes for female students: Further evidence. In Costa, C. (Ed.), Proceedings of the 16th European Conference on Game Based Learning (ECGBL 2022) (pp. 339–348). Curran Associates.

McLaren, B. M., Richey, J. E., Nguyen, H. A., & Mogessie, M. (2022). Focused self-explanations lead to the best learning outcomes in a digital learning game. In Chin, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference on Learning Science (ICLS 2022) (pp. 1229–1232). International Society of the Learning Sciences.

Richey, J. E., Zhang, J., Das, R., Andres-Bray, J. M., Scruggs, R., Mogessie, M., Baker, R. S., & McLaren, B. M. (2021). Gaming and confrustion explain learning advantages for a math digital learning game. In Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., & Dimitrova, V. (Eds.), Artificial Intelligence in Education: 22nd international conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021, Proceedings, Part I (pp. 342–355). Springer. https://doi.org/10.1007/978-3-030-78292-4_28

Baker, R. S., McLaren, B. M., Hutt, S., Richey, J. E., Rowe, E., Almeda, M. V., Mogessie, M., & Andres, J. M. A. (2021). Towards sharing student models across learning systems. In Roll, I., McNamara, D., Sosnovsky, S., Luckin, R., & Dimitrova, V. (Eds.), Artificial Intelligence in Education: 22nd international conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021, Proceedings, Part II (pp. 60–65). Springer. https://doi.org/10.1007/978-3-030-78270-2_10

Richey, J. E., Andres-Bray, J. M. L., Mogessie, M., Scruggs, R., Andres, J. M., Star, J. R., Baker, R. S., & McLaren, B. M. (2019). More confusion and frustration, better learning: The impact of erroneous examples. Computers & Education, 139, 173–190. https://doi.org/10.1016/j.compedu.2019.05.012