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Defense

Wednesday, April 12, 2023
Defense of A Doctoral Dissertation by Raed Awada in Educational Administration

The Ph.D. researcher in the program of Educational Administration, Raed Awada, defended his dissertation which is entitled "A Proposed Model for Building a Creative Organizational Culture and Applying an Electronic Educational Space According to the Needs of the Twenty-First Century in Basic Schools of Jerusalem Governorate". The dissertation was supervised by Prof. Dr. Tayseer Abdullah, and Dr. Kamel Hashem.

The study sought to provide a highly efficient model for building a creative organizational culture and applying an electronic educational space according to the needs of the twenty-first century among the principals of basic schools in Jerusalem Governorate, in addition to reveal whether there are differences in building a creative organizational culture and applying an electronic educational space according to several variables.

Tuesday, April 11, 2023
Defense of A Doctoral Dissertation by Fatima Ghannam in the Program of Educational Administration

The Ph.D. researcher in the Educational Administration program, Fatima Ishtayeh Ghannam, defended her thesis which is entitled: "The Relationship between Organizational Loyalty and the Change Leadership among Palestinian School Principals: the Skill of Decision-Making as a Mediating Variable - A Proposed Model."

The study aimed at revealing the relationship between organizational loyalty and the leadership of change among all Palestinian school principals in Ramallah and Al-Bireh governorate, employing the descriptive-correlational approach.

Thursday, March 2, 2023
Defence of an M.A Thesis in Data Science and Business Analysis about the Role of Deep Machine Learning in the Detection and Classification of Heart Diseases

An MA student in the field of data science and business analysis at the Faculty of Postgraduate Studies at the Arab American University, Abdulhakeem Ahmed Al-Ardah, passed the defence of his thesis for obtaining the M.A degree. The thesis is entitled “Heart Disease Prediction & Disease Type Classification Using Machine Learning”

The thesis relied on merging heart disease databases, and an accurate and effective machine learning system to obtain high classification accuracy. The proposed research method included an initial stage to predict the existence of heart disease, followed by a smart classification to determine the type of heart disease using machine learning. The classification of the disease depends on the results in the initial stage.

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