Teaching Excellence

Dr. Mohammad Fayez Al Bataineh

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4.52/5.0

Student Evaluation Average

15+

Years Teaching Experience

400+

Educational Videos

10.8K

YouTube Subscribers

Teaching Philosophy

My teaching philosophy centers on creating an engaging, interactive learning environment where students are not passive recipients of information but active participants in their educational journey. I believe in bridging theoretical concepts with practical applications, making complex engineering principles accessible and relevant to real-world challenges.

Through innovative use of technology—including iPad-integrated lectures, recorded sessions for flexible learning, and comprehensive digital course management—I strive to accommodate diverse learning styles and create opportunities for students to learn at their own pace. My goal is not just to teach formulas and theories, but to inspire critical thinking, problem-solving skills, and a passion for lifelong learning in electrical engineering and communications.

I am committed to maintaining the highest standards of teaching excellence, as evidenced by my consistent student evaluations (4.52/5.0) that exceed departmental averages, and my continuous pursuit of professional development through programs like the Faculty Teaching Academy Program (FTAP) and Quality Matters certification.

Current Courses at UAEU

Undergraduate Courses

ELEC 360
Signals and Systems

Analysis of continuous-time and discrete-time signals and systems using time-domain and frequency-domain techniques.

Undergraduate
ECOM 320
Probability and Random Processes

Fundamental concepts of probability theory, random variables, and stochastic processes essential for communication systems analysis.

Undergraduate
ELEC 380
Analytical Methods for EE

Mathematical tools and analytical techniques essential for solving electrical engineering problems.

Undergraduate
ECOM 561
Information Theory and Coding

Fundamentals of information theory, source coding, channel coding, and error control coding techniques.

Undergraduate
ELEC 551
Digital Image Processing

Image enhancement, restoration, compression, and analysis techniques using digital signal processing methods.

Undergraduate
ELEC 585/590
Graduation Projects

Capstone design projects integrating knowledge from multiple courses to solve real-world engineering problems.

Senior Project

Graduate Courses

ELEC 644
Artificial Neural Networks

Advanced study of neural network architectures, training algorithms, and applications in engineering systems.

Graduate

Courses Previously Taught at Yarmouk University

CME 312
Signals and Systems
Undergraduate
CME 314
Probability and Random Processes
Undergraduate
CME 452
Digital Communications
Undergraduate
CME 456
Communication Systems
Undergraduate
CME 612
Digital Signal Processing
Graduate
CME 616
Information Theory and Coding
Graduate
BME 152
Introduction to Engineering
Undergraduate

Teaching Innovations & Methodologies

📱

iPad-Integrated Lectures

Interactive lectures using iPad technology for real-time problem-solving, annotations, and dynamic visualizations that enhance student engagement.

🎥

Recorded Sessions

All lectures recorded and made available for students, enabling flexible learning, review, and accommodating different learning paces.

💻

Digital Course Management

Comprehensive online platforms using MS Teams for seamless communication, assignment submission, and resource sharing.

🤖

AI-Enhanced Learning

Integration of NotebookLM and AI tools to provide additional learning support and personalized study materials.

📊

Constructivist Learning

Implementation of constructivist learning theory from Faculty Teaching Academy Program (FTAP) for active student participation.

Quality Matters Framework

Applying Quality Matters standards for course design, ensuring accessibility, alignment, and evidence-based practices.

📺 Educational YouTube Channel

Access hundreds of educational videos covering electrical engineering and communications topics

400+

Educational Videos

10.8K

Subscribers

1000s

Hours of Content

Visit YouTube Channel

Student Resources

Teaching Recognition & Professional Development

Awards & Ratings

  • Student Evaluation Average: 4.52/5.0 (Exceeds departmental average of 4.43)
  • Faculty Evaluation: Excellent Rating (2020-2022 cycle at UAEU)
  • Teaching Load: 22-27 credit hours annually

Professional Development

  • Faculty Teaching Academy Program (FTAP): Completed with focus on Constructivist Learning Theory
  • Quality Matters Certification: "Applying the QM Rubric" (Scheduled September 2025)
  • Digital Liaison: Educational Technology Integration (2025)
  • AI in Higher Education Workshop: University-wide professional development (March 2025)