For the past 30 years, the University of North Texas has been at the forefront of the fast-growing field of educational, instructional and learning technologies.

Graduate opportunities

Our coursework leads to a Doctor of Philosophy in Learning Technologies, which can be completed in residency or at a distance. We are the first university in Texas to offer a distance-delivered doctoral degree in Learning Technologies or a related area. The required coursework focuses on understanding and expanding the synergy of technology and learning/instructional systems theory. The program also provides:

  • A strong foundation in computing and information/cognitive science, learning theory and education.
  • Mentoring from faculty members and professionals in the field.
  • An option to complete work for a master’s degree in Learning Technologies while working toward your doctoral degree.
  • The opportunity to build a portfolio of academic writing, creative work and service demonstrating student skills to future employers after graduation.

Students are prepared for positions at universities and organizations that create, evaluate and teach about or test innovative applications of learning technology. Many of our doctoral graduates find employment in education; while others find work in business and industry.

Our faculty members are internationally known for advancing knowledge of technology tools and their applications in educational and instructional settings. Their expertise prepares students to be future educators and technology professionals.

Residency and distance-delivered options

The Ph.D. is offered as a residency-based program for those in the North Texas region and as a distance-delivered cohort program for those preferring the flexibility of an online program.

Residency students meet for courses at the state-of-the-art UNT Discovery Park. Students participating in the distance option take online courses throughout the year and attend a mandatory yearly face-to-face meeting during the summer. The annual meeting is held in conjunction with a major conference when possible.

Attending UNT

Admission requirements

Admission to doctoral study in Learning Technologies is competitive within the capacity of the program faculty to mentor doctoral students. Each prospective student will be subjected to evaluation conducted by the Learning Technologies program graduate faculty each term/semester for a limited number of openings. We encourage prospective students to submit all materials well in advance of the deadline due to the processing time.

Students must meet the admission requirements for the Toulouse Graduate School® and the following program requirements:

  • Application Requirements
    • A master’s degree from an accredited institution, with a grade point average of 3.5 (on a 4.0 scale)
    • Under some circumstances, a bachelor’s degree with sufficient additional courses required to secure a master’s degree on the way to a Ph.D. A total grade point average of 3.0 or a grade point average of 3.5 over the last 60 hours (on a 4.0 scale)
  • Graduate Record Examination (GRE) scores, including verbal, quantitative and analytical writing, must be on file at the time the application is reviewed, or submissions of the following materials:
    • A scholarly presentation at a professional conference related to Learning Technologies
    • A scholarly publication in a respected, peer-reviewed setting
    • Completion of six hours of graduate coursework in mathematics or statistics with a grade point average of 3.0 or higher
    • For international students who have completed the IELI program through level 6 and successfully completed the UNT Graduate Preparation Course, the GPC may be submitted in place of GRE scores
  • For international students, a satisfactory score on the Test of English as a Foreign Language (TOEFL) or successful completion of the UNT Intensive English Language Institute (IELI) through level 6
  • Degree program application (contact ci-admissions@unt.edu to request application)
  • A personal resume or curriculum vitae that includes a summary of teaching, administrative, and/or training experience
  • Personal statement (500-1000 words) of career objectives, which may include doctoral research areas of interest; research, professional or community experiences that demonstrate motivation, commitment and potential for doctoral work; accomplishments; communication skills; technology skills; and contribution to the diversity of the field
  • Three letters of recommendation. One must be from a faculty member currently working at an academic institution. This letter must acknowledge the applicant’s potential to successfully complete the doctoral program
  • An interview with program faculty, which is not a requirement, but may be requested by the admission committee

Degree requirements

The Ph.D. degree consists of 60-69 credit hours, which includes:

  • 15 credit hours of core courses
  • 21 credit hours of electives/topic courses
  • 12 credit hours of research courses
  • 9 credit hours of tools courses (waiver possible for one or more)
  • 12 credit hours of dissertation

Students work closely with faculty members during course work and the dissertation, providing support and encouragement, as needed.

Students enrolled in the distance-delivered cohort program receive additional guidance from associate graduate faculty mentors who are professionals in the field.

Financial assistance

Grants from organizations such as the U.S. Department of Education, the Texas Education Agency and the Job Training Partnership Program provide financial support to students. For information on these opportunities, contact the department. For information about other financial assistance programs, visit the Financial Aid website.

Faculty

Yunjo An, Associate Professor; Ph.D., Indiana University. Game-based learning; complex problem solving.

Rose Baker, Assistant Professor; Ph.D., Pennsylvania State University. Workplace learning; performance improvement.

Demetria Ennis-Cole, Professor; Ph.D., Kansas State University. Computer education instruction and administration; systems development; user training.

Aleshia Hayes, Assistant Professor; Ph.D., University of Central Florida. VR, AR, and simulation-based learning.

Karen Johnson, Assistant Professor; Ph.D., University of Minnesota. Workplace learning and training solutions.

Gerald A. Knezek, Regents Professor; Ph.D., University of Hawaii. Technology integration; telecommunications; educational research and measurement.

Youngjin Lee, Associate Professor; Ph.D., University of Illinois. Learning analytics.

Lin Lin, Professor; Ed.D., Columbia University. Instructional technology; human-machine interaction; online teaching and learning; teacher professional development.

Cathleen Norris, Regents Professor; Ph.D., University of North Texas. Mobile technologies; computer-based education; human factors; teacher professional development.

Thomas Parsons, Professor and Director of the NetDragon Digital Research Center, Ph.D., Fuller Graduate School of Psychology. Neuroscience and learning.

J. Michael Spector, Professor and Department Chair; Ph.D., University of Texas at Austin. Complex learning; program evaluation; simulation-based learning.

Tandra Tyler-Wood, Professor and Department Chair, Ph.D., University of North Carolina. Assessing learning and curricula for special needs students.

John Turner, Assistant Professor; Ph.D., University of North Texas. Team cognition and team learning; multi-level analysis methods.

Scott Warren, Professor; Ph.D., Indiana University. Digital learning environments; games and simulations to support literacy and learning; technology-supported research methods.