Dr. Arham Muslim

Dr. Arham Muslim

Office location: LE 406

Office hours: By appointment only

Phone: +49 (0) 203 379-3707

E-Mail: arham.muslim@uni-due.de

Short CV

Arham Muslim is a senior researcher at the Department of Computer Science and Applied Cognitive Science at the University of Duisburg-Essen. He has a Master’s degree in software systems engineering and a Ph.D. in computer science from RWTH Aachen University. His main research areas encompass social computing, data science, and learning technologies, with the focus on human-centered learning analytics, information visualization, visual analytics, and big data.

Research Interests

  • Learning Technologies
  • Learning Analytics
  • Data Science
  • Information Visualization
  • Visual Analytics
  • Big Data

Academic Qualifications

Jul 2012 - Dec 2018
Ph.D. in Computer Science
RWTH Aachen University, Germany

Oct 2008 - May 2011
Master in Software Systems Engineering
RWTH Aachen University, Germany

Jan 2003 - Oct 2007
Bachelor in Software Engineering
University of Engineering & Technology Taxila, Pakistan 

Work Experience

since Feb 2018
Researcher in the Social Computing Group at the University of Duisburg-Essen, Germany

Jul 2012 - Jan 2018
Software developer at Center of Innovative Learning (CiL), RWTH Aachen University, Germany
Technologies: SharePoint 2013, C# .NET

Jan 2011 - Jun 2012 (Full position)
Nov 2008 - Dec 2011 (Student job)
Software developer at Fraunhofer Institute of Production Tech. (IPT), Aachen, Germany
Technologies: C# .NET, ANSYS, Abaqus

Mar 2007 - Feb 2008
Software developer at Zultec, Jeddah, Saudi Arabia
Technologies: VB .NET, Windows CE 5.0

Publications

  • Muslim, A., Chatti, M. A., and Schroeder, U. (In Review). Supporting Indicator Personalization and Platform Extensibility in Open Learning Analytics.
  • Muslim, A., Chatti, M. A., Bashir, M. B., Barrios Varela, O. E., and Schroeder, U. (2018). A Modular and Extensible Framework for Open Learning Analytics. Journal of Learning Analytics, 5(1):92–100.
  • Muslim, A., Chatti, M. A., Mughal, M., and Schroeder, U. (2017). The goal - question - indicator approach for personalized learning analytics. In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU (pp. 371-378). ScitePress.
  • Muslim, A., Chatti, M. A., Mahapatra, T., & Schroeder, U. (2016, April). A rule-based indicator definition tool for personalized learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 264-273). ACM.
  • Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an Open Learning Analytics Ecosystem. In Big Data and Learning Analytics in Higher Education (pp. 195-219). Springer International Publishing.
  • Chatti, M. A., Lukarov, V., Thüs, H., Muslim, A., Yousef, A. M. F., Wahid, U., Greven, C., Chakrabarti, A., Schroeder, U. (2014). Learning Analytics: Challenges and Future Research Directions. eleed, Issue 10.
  • Lukarov, V., Chatti, M. A., Thüs, H., Kia, F. S., Muslim, A., Greven, C., & Schroeder, U. (2014). Data Models in Learning Analytics. In DeLFI Workshops (pp. 88-95).
  • Dyckhoff, A. L., Lukarov, V., Muslim, A., Chatti, M. A., & Schroeder, U. (2013, April). Supporting action research with learning analytics. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 220-229). ACM.
  • Ansari, J., Meshkova, E., Masood, W., Muslim, A., Riihijärvi, J., & Mähönen, P. (2012, October). Confab: Component based optimization of wsn protocol stacks using deployment feedback. In Proceedings of the 10th ACM international symposium on Mobility management and wireless access (pp. 19-28). ACM.