Teaching

One of the critical factors that distinguish an academic career is the opportunity to train the next generation of professionals and scientists in my field. I find it very rewarding to interact with students and to encourage their passion and curiosity in computing and information science (IS). One of the core challenges facing teachers in the IS fields is recognizing and mitigating the diverse scholarly intellectual background of our students. Over my two courses teaching experience, I have learned techniques to better engage with, and impart significant technical knowledge to a very diverse group of students.

As an instructor, I have taught two graduate-level courses in five continues academic semesters: INFSCI 2300: Human Information Processing (summer 2017 with 17 students & repeated in fall 2017 with 23 students) and INFSCI 2710: Database Management (spring 2018 with 45 students, summer 2018 with 17 students and fall 2018 with 57 students). The two courses helped me to master the necessary skills for utilizing different kinds of teaching methods – including formal lectures, practical and tutorial sessions, which is essential to teach multidisciplinary students in computing and information sciences effectively. My teaching evaluations revealed positive feedback (4.5 out of 5) from the participating students.

The computing and information science disciplines have huge potential to impact the educational mission of universities and colleges, and they represent new opportunities as well as challenges. All computer science and information science departments are experiencing an enormous increase in undergraduate and graduate enrollment. It is an exceptional chance for the new computing era teacher/researcher, who to rethink curricula to reflect emerging concepts and new research and develop activities for the introduction of new computing and information skills into other disciplines. In our data-saturated age, knowing facts is not as important as being able to use knowledge to define and solve problems. For all the above practices to be successful, it is essential to consider diversity in, and personalization for information science education.

Students from different disciplines should be able to leverage the power of computational research in all contexts, from studio arts to digital humanities, from computational biology to digital ethics. Moreover, it is critical to address the diversity gaps in information science, by exploring how to attract and retain underrepresented student groups. I feel strongly that a personalized teaching method should be able to improve the learning experience by bringing together an array of resources customized to the interests and aspirations of each student and enriched by advisors, real-time data, and learning innovations to refine and guide each student's career path.

  • As Instructor
    • INFSCI 2710: Database Management
      • Spring 2018, Graduate Course (47 Students), University of Pittsburgh [Syllabus]
      • Summer 2018, Graduate Course (17 Students), University of Pittsburgh [Syllabus]
      • Fall 2018, Graduate Course (57 Students), University of Pittsburgh [Syllabus]
    • INFSCI 2300: Human Information Processing
      • Summer 2017, Graduate Course (17 Students), University of Pittsburgh [Syllabus]
      • Fall 2017, Graduate Course (23 Students), University of Pittsburgh [Syllabus]
  • As Teaching Assistant
    • INFSCI 2560: Web Technologies & Standards (Fall 2016, University of Pittsburgh)
    • INFSCI 2560: Web Technologies & Standards (Spring 2017, University of Pittsburgh)
    • INFSCI 2730: E-Business (Spring 2017, University of Pittsburgh)
  • As Guest Lecturer
    • INFSCI 2560: Web Technologies & Standards (Fall 2013, University of Pittsburgh)
    • INFSCI 2560: Web Technologies & Standards (Fall 2016, University of Pittsburgh)
    • INFSCI 2560: Web Technologies & Standards (Spring 2017, University of Pittsburgh)