Hello,
I am a fifth year PhD student in the Human-Computer Interaction Institute (HCII) in the School of Computer Science at Carnegie Mellon University (CMU). I am advised by Dr. Amy Ogan. My research focuses on developing technologies that support and help instructors improve their teaching. Prior to CMU, I have completed my undergraduate studies at Lafayette College, where I majored in Computer Science and minored in Mathematics.
I am very passionate about teaching and I am currently in the job market for teaching positions.
Newell-Simon Hall 4617
Resume
Education
Ph.D. Human-Computer Interaction, 2015 - present
Human-Computer Interaction Institute
School of Computer Science
Carnegie Mellon University
Advisor: Dr. Amy Ogan
M.S. Human-Computer Interaction, 2015 - 2017
Human-Computer Interaction Institute
School of Computer Science
Carnegie Mellon University
PIER Associate, 2015 - present
Carnegie Mellon University
B.S. Computer Science, 2011 - 2015
Minor in Mathematics
Summa Cum Laude, Honors in Computer Science
Lafayette College
Awards and Honors
The Alan J. Perlis Graduate Student Teaching Award
School of Computer Science, Carnegie Mellon University, 2019
The Graduate Student Assembly Departmental Appreciation Award
Carnegie Mellon University, 2019
Summa Cum Laude, Honors in Computer Science
Lafayette College, 2015
Upsilon Pi Epsilon Scholarship Award
Lafayette College, 2014
James P. Schwar Prize
Lafayette College, 2014
Walter Oechsle Scholarship
Lafayette College, 2011-2015
Grace Hopper Celebration of Women in Computing Scholarship
Grace Hopper Conference, 2012, 2013
Programming
Java, Python, JavaScript, HTML, CSS, C++, C, R, Jess, Intel IA32
Tools
CTAT, Django, Ajax, jQuery, Heroku, NodeJs, SQLite, LaTex, Mathematica, WordPress, Sketch, InVision, Adobe: Photoshop, Flash Player, InDesign
Methods
Contextual Inquiry, Interpretation Sessions, Affinity Diagramming, Speed Dating, Storyboarding, Prototyping, Think Alouds, Classroom Studies
Languages
English, Albanian, Italian, French, Greek, Korean
Teaching
Carnegie Mellon University
CS Pedagogy (15-890), Spring 2020
Instructor of 15-890, a course targeted toward students who are interested in improving their ability to teach computer science and who are interested in the science of teaching and learning.
Principles of Computing (15-110), Summer 2 2019
Instructor of the intro CS course 15-110, a course in fundamental computing principles (~50 students).
User-Centered Research and Evaluation (UCRE), Fall 2018
Took part in course and curriculum redesign including: deciding topics/concepts for the course, creating learning goals for projects and individual assignments, structuring projects and assignments over the semester. Created assignments, projects and questions for the final. Designed grading rubrics for assignments/the exam. Taught recitation of 20 students, supervised and graded individual and group student work, held office hours.
Programming Usable Interfaces (PUI), Spring 2018
Taught recitation of 20 students, designed some labs and homework assignments, created some quiz and exam questions, graded student work, held office hours.
Grad research seminar on Second Language Acquisition, Fall 2019
Lecture on my work on the ClassInSight project and using data to help and support teachers' teaching and practices.
Programming Usable Interfaces (PUI), Spring 2018
Lecture on user-centered design methods and examples of their use in my own research.
Undergraduate Independent Study and Research Assistants, Spring 2020
Mentored two undergraduate students in their Independent Study projects and six undergraduate students as Research Assistants on the ClassInSight project.
Research Assistants, Fall 2019
Mentored sixteen undergraduate students as Research Assistants on the ClassInSight project.
Undergraduate Independent Study, all 2018, Spring 2019
Mentored two undergraduate students each semester in their Independent Study projects.
LearnLab Summer School at CMU, ITS Track, Summer 2015 - 2018
Mentored groups of students in developing Intelligent Tutoring Systems for various domains.
Research Experience for Undergraduates (REU), Summer 2016, Summer 2019
Mentored five students each summer as they conducted research and software development.
Lafayette College
Algorithms and Data Structures (CS150), 2013 - 2014
Led and oversaw lab sessions, built some assignments and lab worksheets.
Projects
EduSense: Using data to help instructors support and improve their teaching.
Providing university teachers with high-quality opportunities for professional development cannot happen without data about the classroom environment. Currently, the most effective mechanism is for an expert to observe lectures and provide personalized formative feedback to the instructor. Of course, this is expensive and unscalable, and perhaps most critically, precludes a continuous learning feedback loop for the instructor. We present the development of EduSense, a comprehensive sensing system that produces a plethora of theoretically-motivated visual and audio features correlated with effective instruction, which could feed professional development tools in much the same way as a Fitbit sensor reports step count to an end user app. EduSense is the first to unify multiple features into a cohesive, real-time, in-the-wild evaluated, and practically-deployable system.
Helping Teachers Help their Students: Teacher's Use of Intelligent Tutoring Software Analytics to Imporve Student Learning
Although learning with Intelligent Tutoring Systems (ITS) has been well studied, little research has investigated what role teachers can play, if empowered with data. Many ITSs provide student performance reports, but they often are not designed to support teachers and their practices. A dashboard with analytics about students’ learning processes might help in this regard. In this project, through a variety of user-centered design methods, we initially investigated what student data is most helpful to teachers and how teachers use data to adjust and individualize instruction. We then explored through a quasi-experimental classroom study how Luna, a dashboard prototype designed for an ITS and used with real data, affects teachers and students.
Integrating Intelligent Tutoring Systems (ITSs) in MOOCs
A key challenge in ITS research is to support tutoring at scale, for example by embedding tutors in MOOCs. An obstacle to at scale deployment is that ITS architectures tend to be complex, not easily deployed in browsers without significant server-side processing, and not easily embedded in a learning management system (LMS). In our study we modify a widely used ITS authoring tool suite, CTAT TutorShop, so that tutors can be embedded in MOOCs. The inner loop (the example-tracing tutor engine) was moved to the client by re-implementing it in JavaScript, and the tutors were made compatible with the LTI e-learning standard. The feasibility of this approach to integration was demonstrated with simple tutors in an edX MOOC “Data Analytics and Learning.”
Intelligent Tutors and Granularity: A New Approach To Red Black Trees
Red black trees are an important data structure with many applications. However, they are quite difficult for the students to learn and master due to the complexity of the rules and concepts involved. I explored the process of designing, developing and evaluating an Intelligent Tutoring System, the RedBlackTree Tutor, that aims to help students learn and practice the top-down insertion algorithm in red black trees. The RedBlackTree Tutor has been experimentally tested and evaluated in the CS 150 Data Structures and Algorithms course at Lafayette College during the Fall 2014 and Spring 2015 semesters. The results of employing the intelligent tutor in teaching top-down insertion in red black trees show significant learning gains by students.
Publications
Conference Publications
Ahuja, K., Kim, D., Xhakaj, F., Varga, V., Xie A., Zhang, S., Townsend, J. E., Harrison, Ch., Ogan, A., & Agarwal, Y. (2019). EduSense: Practical Classroom Sensing at Scale. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3, 3, Article 71 (September 2019), 26 pages. DOI: https://doi.org/10.1145/3351229
Xhakaj, F., Aleven, V. (2018). Towards Improving Introductory Computer Programming with an ITS for Conceptual Learning. In International Conference on Artificial Intelligence in Education (pp. 535-538). Springer, Cham.
Bodily, R., Kay, J., Aleven, V., Davis, D., Jivet, I., Xhakaj, F. & Verbert, K. (2018). Open learner models and learning analytics dashboards: A systematic review. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (LAK), pp. 41-50. ACM, 2018.
Xhakaj, F., Aleven, V., McLaren, B.M. (2017). Effects of a Teacher Dashboard for an Intelligent Tutoring System on Teacher Knowledge, Lesson Planning, Lessons and Student Learning. In É Lavoué, H. Drachsler, K. Verbert, J. Broisin, M. Pérez-Sanagustín (Eds.), Proceedings of the 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, (pp. 315-329). Springer International Publishing Switzerland.
Xhakaj, F., Aleven, V., McLaren, B.M. (2017). Effects of a dashboard for an intelligent tutoring system on teacher knowledge, lesson plans and class sessions. In E. Andre, R. Baker, X. Hu, Ma. M. T. Rodrigo, B. du Boulay (Eds.), Proceedings of the 18th International Conference on Artificial Intelligence in Education, AIED 2017, (pp. 582-585). Springer International.
Xhakaj, F., Aleven, V., McLaren, B.M. (2016). How teachers use data to help students learn: Contextual Inquiry for the design of a dashboard. In K. Verbert, M. Sharples, T. Klobučar (Eds.), Proceedings of the 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, (pp. 340-354). Springer International Publishing Switzerland.
Aleven, V., Sewall, J., Popescu, O., Xhakaj, F., Chand, D., Baker, R. S., & Gasevic, D. (2015). The beginning of a beautiful friendship? Intelligent tutoring systems and MOOCs. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on AI in Education, AIED 2015 (pp. 525–528). New York: Springer.
Liew, C. W., & Xhakaj, F. (2015). Teaching a complex process: Insertion in Red Black Trees. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Proceedings of the 17th International Conference on Artificial Intelligence in Education, AIED 2015 (pp. 698–701). New York: Springer International Publishing.
Xhakaj, F., & Liew, C. W. (2015). A new approach to teaching Red Black Trees. In V. Dagienė, C. Schulte, & T. Jevsikova (Eds.), Proceedings of the 20th ACM Annual Conference on Innovation and Technology in Computer Science Education, ITiCSE ‘15 (pp. 278–283). New York: ACM.
Workshop Papers
Aleven, V., Xhakaj, F., Holstein, K, & McLaren, B. M. (2016). Developing a teacher dashboard for use with intelligent tutoring systems. In Proceedings of the 4th International Workshop on Teaching Analytics at the 11th European Conference On Technology Enhanced Learning, IWTA 2016.
Holstein, K., Xhakaj, F., Aleven, V., & McLaren, B. M. (2016). Luna: A dashboard for teachers using intelligent tutoring systems. In Proceedings of the 4th International Workshop on Teaching Analytics at the 11th European Conference On Technology Enhanced Learning, IWTA 2016.
Journal Publications
Wei, Sh., Xhakaj, F., & Ryder, B.G. (2015) Empirical Study of the Dynamic Behavior of JavaScript Objects. Journal of Software: Practice and Experience, 46, 7, 867–889.
Senior Thesis
Xhakaj, F. (2015). Intelligent tutors and granularity: A new approach to Red Black Trees. Unpublished senior thesis, Department of Computer Science, Lafayette College, Easton, Pennsylvania. USA.