Teaching Philosophy

Education is not the learning of facts, but the training of the mind to think.

-Albert Einstein, Nobel Laureate in Physics, 1922

My students learn, and they learn a lot. It is in part, my teaching (or not-teaching) philosophy, and in part, my convictions as a faculty member. I believe teaching should not be called teaching… it should be called facilitating knowledge and skill attainment, training the mind to think. A bit lengthy, I suppose, but “teaching”, in the traditional sense, is not effective and is quickly being replaced by other forms and modalities of learning. As educators, we are guides through the learning experience, not the source, and as guides, we provide coaching, mentoring, and of course assessment of learning, to our students. And if universities are not careful, they will be quickly sidelined by industry and e-learning platforms [1], [2]. It is my humble philosophy that Universities have two primary responsibilities, above all others, to the world: 1) to generate new knowledge for the benefit of the humanity and the planet and 2) to educate those that desire it. Thus, my role, as a current and future faculty member, is to advance both to the highest degree possible.

I say teaching should not be called teaching, but in fact, I love helping students learn (which in my view, is not the same thing). As an educator, I believe it is my role to advance not only knowledge generation, but also knowledge and skill attainment. To that end, adopting new modalities and pedagogical strategies are paramount for Universities to remain competitive and healthy in the future. This academic year, I have resolved to deliver all of my course learning via two modern modalities: 1) fully online and 2) hybrid. As fully online learning has not been shown to be better (or worse) for learning efficacy [3], hybrid, especially, will be the future of learning. Part face to face activity and part online activity, the hybrid learning model has been shown to be an very efficient approach of distance learning considering student’s learning experiences, student-student interaction and student-instructor interaction and will most likely “emerge as the predominant education model in the future” [4]. However, if the activities that form part of the course are not well planned and developed with the end in mind (i.e. student outcomes and learning goals), then no course will be effective, regardless of the modality.

Fink [5] defines significant student learning in six parts: foundational knowledge, application, integration, human dimension, caring, and learning how to learn. In order to create significant learning in students, I choose to redesign Introduction to Machine Learning, which I will teach this coming Fall. From development course objectives and course learning outcomes, to developing activities throughout the semester, a fully holistic approach is best. For example, I design activities in the course that target at least two to three of the six parts to foster significant learning upon completion of the course. For example, I make extensive use of team-based learning and problem based-learning throughout the term. In Introduction to Machine Learning, I have students gather into groups at the middle of the term and select a term project topic. I then coach and mentor the team through the project lifecycle, ultimately resulting in a final demonstration of their project and a final presentation. Similarly, I have three to four problem sets that students need to complete throughout the semester. Individual responses are required; however, I encourage students to work in groups and provide an online forum via the course learning management system (LMS) to discuss each module’s problem set.

As a result of implementing these practices, a majority of my students have rated me as Excellent, or Very Good for the following questions that are part of FIU’s Student Perceptions of Teaching (SPOT) survey [6], as recently as this past spring.

  • Description of course objectives and assignments
  • Expression of expectations for performance in this class
  • Description of grading policies in the course syllabus
  • Consistency in following the course syllabus
  • Preparation for class
  • Use and management of class time
  • Knowledge of course content
  • Communication of ideas and information
  • Stimulation of interest in course
  • Facilitation of learning
  • Availability to assist students in or out of class
  • Respect and concern for students
  • Fairness of instructor

There is however, one area that I can improve for my students, which is one of the steps of creating significant learning experience, and that is to provide timely feedback to students about their performance. Although I grade most assignments and exams with one week of the deadline, I find it challenging to engage in meaningful one-on-one, face-to-face conversations with students to help them in their learning journey due to schedule conflicts (both mine and theirs), as well as, other duties (i.e. research and service). This situation becomes more difficult with fully online or hybrid courses. Due to the nature of the modality, students are not on campus as frequently and these courses tend to attract students that have work or family obligations (taking into consideration that FIU is a commuter university). So, in order to improve, I will be dedicating one week this coming term to meet with students individually, just after the midpoint of the semester and after they take their course exam, to discuss their progress in the course and address any issues or roadblocks they may be facing with sufficient time to course correct before the end of the term.

I am constantly in a continuous improvement cycle for my courses, constantly gauging student learning through classroom assessments, project and problem-based learning assessments, and SPOT surveys. Using Fink’s 12 step program, I look to develop fundamental skills in students, from technical, course related knowledge and skills, to broader outcomes such as critical thinking, teamwork and collaboration, leadership, and digital technology excellence. I also observe student feedback and recommendations, and I integrate pedagogically sound teaching strategies into my courses, such as project-based learning and problem-based learning. Ultimately, as an educator, I’m constantly learning and growing as faculty member, and will continue to provide significant learning experiences for all of the courses that I facilitate for students.


[1]        “Amazon is becoming its own university — Quartz.” [Online]. Available: https://qz.com/1191619/amazon-is-becoming-its-own-university/. [Accessed: 21-May-2018].

[2]        “Udacity Achieves 100% Year-Over-Year Revenue Growth in 2017,” 27-Feb-2018. [Online]. Available: https://www.businesswire.com/news/home/20180227005737/en/Udacity-Achieves-100-Year-Over-Year-Revenue-Growth-2017. [Accessed: 21-May-2018].

[3]        B. Means, Y. Toyama, R. Murphy, M. Bakia, K. Jones, and E. and Policy Development USA. Dept of Education. Office of Planning, “Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies,” Httplst-Iiepiiep-Unescoorgcgi-Binwwwi32exeinepidoc1int2000027003100, vol. 115, Jan. 2010.

[4]        M. Tayebinik and M. Puteh, “Blended Learning or E-learning?,” ArXiv13064085 Cs, Jun. 2013.

[5]        L. D. Fink, Creating Significant Learning Experiences: An Integrated Approach to Designing College Courses. Wiley, 2003.

[6]        “SPOTs Policy.” [Online]. Available: https://opir.fiu.edu/spotspolicy.htm. [Accessed: 21-May-2018].

Courses Taught

CAP 5610 – Introduction to Machine Learning
CAP 5771 – Principles of Data Mining
HFT 4445 – Hospitality Design Thinking and Innovation
HFT 4292C – Entrepreneurship in H&T
EEL 3003 – Electrical Engineering I
EEL 3111 – Circuits I
EEL 4930 – Development of Dynamic Websites
CEN 1990 – Introduction to Computing through Mobile Application Development
CET 2123C – Microprocessors
COP 1220 – Introduction to C++
CGS2423 – C for Engineers
CET1112C – Digital Circuits
CET 2113C – Advanced Digital Circuits
EET 2351 –Digital and Data Communications
ETD 1340 – Computer Aided Drawing/Design
EGS 1001C – Introduction to Engineering
EET 1082 – Introduction to Electronics
EET 2305c –Analog Communications
EGS 2311 – Engineering Mechanics: Statics
ETI 3704 – Computer Security
EET3716C – Advanced Systems Analysis
CET4190C – Applied DSP

Teaching areas/courses prepared to teach

Deep Learning
AI, Entrepreneurship, & Society
Computer Vision
Artificial Intelligence
Machine Learning
Reinforcement Learning
Data Science
Data Visualization
Distributed, High-performance, and Cloud Computing
AI Robotics
Software Engineering
Data Mining
Natural Language Processing