Hello,

Carnegie Mellon University, 2015-present
Ph.D. in Human-Computer Interaction

Carnegie Mellon University, 2015-2017
M.S. in Human-Computer Interaction

PIER Associate, 2015-present

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

Teaching and Mentoring Experience

Teaching Assistant, Programming Usable Interfaces (PUI)
HCI, CMU, Spring 2018

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

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

REU Mentor
HCI, CMU, Summer 2016

Teaching Assistant, Algorithms and Data Structures
Lafayette College, 2013 - 2014

Programmin Languages

Java, JavaScript, Jess, C++, HTML, CSS, R, Python, C, Intel IA32

Tools

Cognitive Tutor Authoring Tools (CTAT), Django, Ajax, jQuery, Heroku, NodeJs, SQLite, LaTex, Mathematica, WordPress, Sketch, InVision, Adobe: Photoshop, Flash Player, InDesign

Research Methods

Contextual Inquiry, Interpretation Sessions, Affinity Diagramming, Speed Dating, Storyboarding, Prototyping, Directed Storytelling, 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

Together with Dr. Vincent Aleven and Dr. Bruce McLaren, in this project we aim to make use of advanced analytics collected from Intelligent Tutoring Systems (ITSs) to power a teacher dashboard. We aim to make use the wealth of data collected through ITSs and present this information back to teachers and educators in a visual and easy-to-read manner. A teacher’s dashboard for ITSs will help increase the effectiveness of intelligent tutors and help teachers and educators increase student learning. Our project was featured in an article that has appeared in the HCII webiste.

Error Classification through Log Replay for Lynnette Cognitive Tutor

In this project we are aiming to detect and classify student errors based on data collected from student interactions with the Lynnette Cognitive Tutor for algebra. Our work involves improving and enhancing the Lynnette Cognitive Tutor by adding bug rules that model student errors developed in prior work. The bug rules are developed in the Jess programing language and capture common errors students make while working with first degree equations in Lynnette. In addition, we aim to run an off-line analysis by replaying the student logs through the bug rules to identify and classify student errors.

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 explore 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. (accepted, to appear 2018). Towards Improving Introductory Computer Programming with an ITS for Conceptual Learning. AIED 2018, Young Researcher’s Track (YRT)

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. doi.org/10.1007/978-3-319-66610-5_23

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.

Bodily, R., Kay, J., Aleven, V., Davis, D., Jivet, I., Xhakaj, F. & Verbert, K. Open learner models and learning analytics dashboards: A systematic review. To appear in LAK 2018.

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. doi: 10.1007/978-3-319-45153-4_26

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. doi:10.1007/978-3-319-19773-9_53

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. doi:10.1007/978-3-319-19773-9_95

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. doi:10.1145/2729094.2742624

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. doi:10.1002/spe.2334

Unpublished 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.