Ph.D. (Technology, Education and Managem

Doctor of Philosophy in Technology Education & Management

 

The Doctor of Philosophy Program in Technology, Education, and Management (TEM) is an interdisciplinary research oriented program. It is designed to produce research scholars, mid-level and senior administrators who are working in the areas of TEM, college instructors, technology trainers, managers and leaders in industries including public, private and Non-Government Organizations (NGOs).

 

MISSION: This program will produce experts who have a thorough understanding of the current technology trends affecting the education sector, the public, private and the Non-Government Organizations (NGOs).

 

VISION: This program will produce experts who have advanced knowledge in Technology, Education, and Management (TEM) and thoroughly understand the significance of applying code of conduct according to the academic and ethical standards of Assumption University.

Qualifications for Admission

  • Master’s degree from an accredited institution

  • Working/teaching experience of at least 2 years

  • Two letters of Recommendation

  • A research Concept Paper (CP)

  • Have passed the AU-English Language Prociency Test AU-EPT with a score of 70% and above or pass one of the following international exams: TOEFL (iBT) with a score at least 79 out of 120 (79/120) or IELTS exam with a score 6.5 out of 9 (6.5/9)

Registration

Students can register for a maximum of 9 credits per semester. 

 

Note: Should the students fail to complete the program after 48 credits enrollment, maintenance of status fees (subject announced by the university) must be paid

Application Procedure

  • Complete the online application form at https://www.grad.au.edu/apply-online-doctoral

  • Submit the required documents as required by the Program (to be notified)

  • Remit fees for interview and English Proficiency Test (in case of taking the English Admission Examination by the University)

Program Duration

3-year program 

Venue & Class Hours

In Class and Hybrid Mode Learning