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Edel Jesse, PhD

Executive Director, Student Development and Director, Communications and Community Relations

Staff

Student Development: Communications and Community Relations

Contact

Email: Edel Jesse, PhD
Phone: 937-229-3497
Gosiger Hall, Room 225A
Website: Visit Site

DEGREES

  • Ph.D. in Educational Leadership, University of Dayton
  • Master of Business Administration, Wright State University
  • Bachelor of Science in Management, Park University

SELECTED PUBLICATIONS

  • Jesse, E. (2019). Student attitudes toward use of massive open online courses. University of Dayton https://etd.ohiolink.edu/ 
  • Fogle, E. M., Franco, S. D., Jesse, E. M., Kondritz, B., Maxam, L., Much-McGrew, H., McMillen, C., Ridenour, C., & Trunk, D. J. (2017). Served through service: Undergraduate students’ experiences in community engaged learning at a Catholic and Marianist university. Journal of Catholic Education, 20(2), 126-153. http://dx.doi.org/10.15365/joce.2002062017
  • Elrod, L., Fogle-Young, E., Franco, S., Jesse, E., Kondritz, B., McGrew, H., McMillen, C., & Trunk, D. (2014). Students who serve: A study of undergraduate students' experiences in community services. eCommons.
  • Jesse, E. M. (2015, August). Reflective teaching. Review of critical perspectives on service-learning in higher education by Susan J. Deeley. Journal of Teaching Theology & Religion, 18(4). 
  • Jesse, E. M., & McMillen, C. (2015). Integrating institutional mission into faculty work. eCommons.

CERTIFICATes

  • Psychometric Assessment: OPQ32 and ability assessments
  • Lominger Interview and Leadership Architect
  • DDI Targeted Selection
  • Emerging Leaders, master's certificate, University of Dayton
  • Leadership Dayton, 2020 Cohort

BOARD AFFILIATION AND PROFESSIONAL ACTIVITIES

  • United Rehabilitation Services of Greater Dayton, Board of Directors Member
  • University of Dayton, Secretary to the Board of Trustees Student Life Committee
  • Association of Training and Development

RESEARCH INTERESTS

  • Machine learning and statistical algorithms
  • Biased data in AI models
  • Prescriptive and predictive modeling