Hello,
I am a sixth year Ph.D. 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 practices in the classroom. Prior to CMU, I have completed my undergraduate studies at Lafayette College, where I majored in Computer Science and minored in Mathematics.
I will be joining CMU in Fall 2020 as Assistant Teaching Professor in a joint appointment between the Computer Science Department (CSD) and the Human-Computer Interaction Institute (HCII).
Newell-Simon Hall 4617
RESUME
EDUCATION
Ph.D. Human-Computer Interaction, 2015 - present (expected Summer 2020)
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 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, 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 & MENTORING EXPERIENCE
TEACHING EXPERIENCE
Principles of Computing (15-110), Summer 2 2020
School of Computer Science, Carnegie Mellon University
Instructor (with Norman Bier) of the intro CS course 15-110, a course in fundamental computing principles (~80 students). Due to the 2020 pandemic, this course was conducted fully online. Lead daily lectures over Zoom and designed online active learning activities. Worked on course and curriculum redesign to adapt the class to online learning.
CS Pedagogy (15-890), Spring 2020
School of Computer Science, Carnegie Mellon University
Instructor of 15-890 (with Charlie Garrod and Michael Hilton), 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. Lead discussions on various topics about pedagogy and CS Education research.
Principles of Computing (15-110), Summer 2 2019
School of Computer Science, Carnegie Mellon University
Instructor (with Norman Bier) of the intro CS course 15-110, a course in fundamental computing principles. Lead daily lectures (~50 students) and designed active learning activities. Designed exam questions, held office hours, mentored students.
User-Centered Research and Evaluation (UCRE), Fall 2018
Human-Computer Interaction Institute, Carnegie Mellon University
Supported course and curriculum redesign, decided topics/concepts for the course, created learning goals for projects/assignments, structured projects/assignments over the semester. Created assignments, projects and questions for the final exam. Designed grading rubrics for assignments/the exam and graded individual and group work. Taught recitation (20 students), supervised individual and group work, held office hours.
Programming Usable Interfaces (PUI), Spring 2018
Human-Computer Interaction Institute, Carnegie Mellon University
Taught recitation (20 students). Designed labs and homework assignments, created quiz and exam questions, graded student work, held office hours.
Algorithms and Data Structures (CS150), 2013 - 2014
Department of Computer Science, Lafayette College
Led and oversaw lab sessions, built some assignments and lab worksheets.
TEACHING GUEST LECTURES
Grad research seminar on Second Language Acquisition
Department of Modern Languages, Carnegie Mellon University
Lecture on my work on ClassInSight and using data to help support teacher in their teaching
Programming Usable Interfaces (PUI)
Human-Computer Interaction Institute, Carnegie Mellon University
Lecture on how to prototype with the InVision technology.
Programming Usable Interfaces (PUI)
Human-Computer Interaction Institute, Carnegie Mellon University
Lecture on user-centered design methods and examples of their use in my own research.
STUDENT MENTORING & ADVISING
Throughout my Ph.D., I have had the honor to work with many outstanding students, undergraduates and masters, from CMU and other schools. Their majors were interdisciplinary including CS, HCI, design, business, statistics, etc. The majority of the students were from underrepresented groups in STEM.
Undergraduate Independent Study and Research Assistants
Mentoring one undergraduate student in their Independent Study, three undergraduate students and one master student as Research Assistants on the ClassInSight project.
Research Experience for Undergraduates (REU)
Mentored two undergraduate students in their REU projects (ClassInSight and ProI for Professionals).
Undergraduate Independent Study and Research Assistants
Mentored a master student in their Independent Study and six undergraduate and master students as Research Assistants on the ClassInSight project.
Undergraduate Independent Study and Research Assistants
Mentored sixteen undergraduates and masters Research Assistants students on the ClassInSight project.
Research Experience for Undergraduates (REU)
Mentored five students as they conducted research and software development on ClassInSight.
Spring 2019
Undergraduate Independent Study
Mentored two undergraduate students each semester in their Independent Study projects on ClassInSight.
Research Experience for Undergraduates (REU)
Mentored five students as they conducted research and software development on an Intelligent Tutoring Systems.
2015 - 2018
LearnLab Summer School at CMU
Supervised small group projects in developing Intelligent Tutoring Systems for various domains.
PROFESSIONAL DEVELOPMENT
Future Faculty Program, Eberly Center Teaching Excellence & Education Innovation, CMU
Attended various professional development workshops and seminars which covered a wide range of topics on teaching and learning. Received feedback twice on my teaching through classroom observations and microteaching workshops. Completed a statement of teaching philosophy project.
Program for Interdisciplinary Education Research (PIER), Carnegie Mellon University
Attended research seminars from various educational researchers. Networked and received mentoring from educational experts. Took a range of courses on education including Research Methods for the Learning Sciences (05-748), Educational Goals, Instruction and Assessment (85-738), Scientific Research in Education (85-736), Personalized Online Learning (05-832).
Computer Science Pedagogy (15-539/15-890), Carnegie Mellon University
Took this course which was aimed at helping students improve their ability to teach computer science.
PROJECTS
Thesis Proposal: Creating Tools To Support Teachers, Their Teaching And To Help Them Improve Their Practices In The Classroom
Draft Proposal DocumentProposal Talk
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.