The Department of Learning Technologies at the University of North Texas in Denton is recruiting for an open rank Assistant/Associate/Professor position, full-time, tenure track faculty position. As the textbook-driven, one-size-fits-all model fades, there is a growing recognition that there is an opportunity to provide truly a personalized learning experience to each and every learner in K-12, post-secondary, adult and career and technical areas. What is being learned is dependent on who the learner is, the goals and objectives involved in instruction, what the learner already knows, and how the learner learns; these factors imply specific instructional strategies that consider both the learner and the learner’s situation. Personalized learning, learning that truly adapts to each and every learner using such technologies as learning analytics, requires that decisions about the what and the how are made by learners and instructors in concert with an instructional delivery system that may be face-to-face, online, or a blend of approaches involving different instructional pedagogies and technologies. Providing personalized learning is, as a consequence of powerful new pedagogies and technologies, a growing enterprise at all educational and training levels. The faculty in the Department of Learning Technologies have historically and consistently brought an interdisciplinary/multidisciplinary/transdisciplinary approach to their teaching and scholarship.
UNT has a highly diverse campus with a wide range of languages spoken in addition to English. We welcome candidates who have experience with HSI/MSIs and/or who speak additional languages, such as Spanish, Vietnamese, American Sign Language, Chinese (Cantonese, Mandarin and other variations), Arabic, Tagalog, Farsi, French, or/and Yoruba.
The position will teach undergraduate, masters and doctoral level courses, core and elective, and will be responsible for creating and updating new course offerings, with focus on both face-to-face and online courses.
There is a growing recognition that there is an opportunity to provide a truly personalized learning experience to each and every learner in all educational areas, and so we are looking for someone with a research focus on adaptive and personalized instruction, artificial intelligence in education, learning analytics, and associated instructional design theories.
The new faculty is also expected to play a critical role in collaborating with current faculty in supporting the departments’ expanding doctoral programs, as we anticipate that there will be an increasing demand from incoming students for research and training in the emerging area of personalized learning that uses emerging pedagogical approaches and technologies for designing and building learning technology systems. Expanding and sustaining a first-class doctoral program is essential to maintaining the goal of continuing to be a Carnegie Tier I research University.
Candidates must have earned a doctorate in Instructional Technology, Learning Technologies or related field, and have expertise/experience in the following areas: Online teaching, application of learning and instructional design theories into technology-enhanced learning systems or environments, and the design, development, implementation, and evaluation of emerging technologies (e.g., AI, learning analytics, adaptive learning systems, games) in multiple learning contexts (e.g., K-12, higher education, corporate training, informal learning, etc.). The successful candidate will have demonstrated a strong grant seeking and high-quality publication record. Applicants seeking appointment with tenure at the associate professor or professor rank must meet UNT’s criteria for tenure at the appropriate level.
The University of North Texas System and its component institutions are committed to equal opportunity and comply with all applicable federal and state laws regarding nondiscrimination and affirmative action. The University of North Texas System and its component institutions do not discriminate on the basis of race, color, sex, sexual orientation, gender identity, gender expression, religion, national origin, age, disability, genetic information, or veteran status in its application and admission processes, educational programs and activities, and employment practices.