I am an Assistant Teaching Professor in the Computer Science Department (CSD) and in the Human-Computer Interaction Institute (HCII) at the School of Computer Science (SCS) at Carnegie Mellon University (CMU).
I have received my Ph.D. from the HCII at CMU where I focused on investigating how to support teachers in their teaching and help them improve their practices through data and technology. Prior to CMU, I have completed my undergraduate studies at Lafayette College, where I majored in Computer Science and minored in Mathematics.
To meet with me, please sign up for a slot via YouCanBookMe: franceska-xhakaj.youcanbook.me
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EDUCATION
Ph.D. Human-Computer Interaction, 2015 - 2021
School of Computer Science
Carnegie Mellon University
Advisor: Dr. Amy Ogan
M.S. Human-Computer Interaction, 2015 - 2017
School of Computer Science
Carnegie Mellon University
PIER Fellowship 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
UPE Special Recognition Award
Upsilon Pi Epsilon International Honor Society for the Computing and Information Disciplines, 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
Tools
CTAT, Django, jQuery, NodeJs, SQLite, LaTex, Mathematica, WordPress, Sketch, InVision, Adobe: Photoshop, Flash Player, InDesign
Methods
User Centered Research, User Centered Design, Contextual Inquiry, Affinity Diagramming, Speed Dating, Storyboarding, Prototyping, Think Alouds, Classroom Studies
Languages
English, Albanian, Italian, French, Greek, Korean
Introduction to Data Structures (15-121), [CSD, CMU]
Undergraduate Capstone Project in HCI (05571), [HCII, CMU]
The Role of Tech in Learning in the 21st Century (05438/05838), [HCII, CMU]
Introductory Programming for MHCI, [HCII, CMU]
ELAIDA: Experiential Learning using AI and Data (99-520), [CMU]
User-Centered Research and Evaluation (UCRE), [HCII, CMU]
Programming Usable Interfaces (PUI), [HCII, CMU]
Algorithms and Data Structures (CS150), [CS Dept., Lafayette College]
Lisa Huang, [Su24]
Samantha Joseph, [Su24]
Daniela Munoz, [S24]
Angie (Chuyi) Wang, [Su23, F23]
Namita Rao, [Su23]
Thesis Proposal: Creating Tools To Support Teachers, Their Teaching And To Help Them Improve Their Practices In The Classroom
Draft Proposal DocumentEduSense: 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.
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.
Ph.D. Thesis
Xhakaj, F. (2021). Investigating How To Support Teachers In Their Teaching And Help Them Improve Their Practices Through Data And Technology. Human-Computer Interaction Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania. USA.
Undergraduate 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.