Hello,

Education

Ph.D. in Human-Computer Interaction, 2015 - present
Human-Computer Interaction Institute
School of Computer Science
Carnegie Mellon University
Advisors: Dr. Amy Ogan and Dr. Vincent Aleven

M.S. in 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. in Computer Science, 2011 - 2015
Minor in Mathematics
Summa Cum Laude, Honors in Computer Science
Lafayette College

Teaching and Mentoring

Instructor, Principles of Computing (15-110)
CMU, Summer 2019

Head TA, User-Centered Research and Evaluation
HCI, CMU, Fall 2018

TA, Programming Usable Interfaces (PUI)
HCI, CMU, Spring 2018

Mentor, Undergraduate Independent Study
HCI, CMU, Fall 2018

Guest Lecture, Programming Usable Interfaces (PUI)
HCI, CMU, Spring 2018

Mentor, LearnLab Summer School at CMU, ITS Track
LearnLab, ITS Track, CMU, Summers 2015 - 2018

REU Mentor
HCI, CMU, Summer 2016

TA, Algorithms and Data Structures
Lafayette College, 2013 - 2014

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

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. Little research has investigated how dashboards influence teacher practices in the classroom and whether they can help improve student learning. 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

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.