My teaching approach part 2

There are three factors that determine students’ success: Knowledge, Attitudes, and Motivation. If the teachers can address these three factors, they can help students to learn and develop the appropriate skills to be successful in their career. To motivate students to learn, I often ask them to list their “dream companies” that they want to work for and research these companies’ skills requirements then identify the skill gaps (i.e., Skills that they do not have) and the list of courses that they must take to fill the gaps. By knowing the reason why they need to take these courses based on their career, all students are self-motivated to learn and work hard to develop their skills.

For example:

Dream company # 1: Flatiron Inc.

Skill Required:

  1. Experience in Python, Java, C++,and  JavaScript.
  2. Experience working with some of the following: distributed and parallel systems, machine learning, information retrieval,
  3. Proficiency in Data Structures and algorithms
  4. Experience in building scalable solutions to machine learning problems

Skill Gap:

  1. Distributed and Parallel systems.
  2. Hadoop and Spark for machine learning

Courses to fill:

  1. (15 642) Distributed and Parallel System in Spring Semester
  2. (10 658) Machine learning with large datasets in the Fall semester
  3. (10-642) Machine learning System: Hadoop & Spark in the Fall semester

Dream Company # 2: Deep-mind Inc.

Skill Required:

  1. Experience in Python and Java
  2. Knowledge of machine learning and statistics
  3. Knowledge of algorithm design
  4. Knowledge in computer vision
  5. Knowledge in image analysis
  6. Working knowledge of TensorFlow or similar frameworks
  7. Experience with multi-threaded design and parallel/distributed computing

Skill Gap:

  1. Parallel and distributed computing
  2. Computer vision
  3. Statistics

Courses to fill:

  1. (15 642) Distributed and Parallel System in Spring Semester
  2. (10 658) Machine learning with large datasets in Spring Semester
  3. (10-674) Computer Vision in the Fall semester
  4. (11-661) Statistics for Data Science in the Fall Semester.

The best way to motivate students to learn is to integrate what they learn in class with information about their future careers. When students understand the reason WHY they need to take the class, WHAT skills they need to get their dream job, and WHEN they take these courses before graduation, most of them will become self-motivated.  When the students have the ability to do what needs to be done, what they must learn to benefit themselves, they have the reason and motivation to complete these courses, even the most challenging courses, without needing someone to encourage them.

To help strengthen their motivation, I often invite guest speakers from the industry or former students to return and share their work experiences with them. It also helps connect between the course materials and the careers that the students want to work on.


  • Blogs of Prof. John Vu, Carnegie Mellon University

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