How Machine Learning Impacts the Undergraduate Computing Curriculum

November 16, 2018 at 7:00 am 4 comments

I’ve been looking forward to seeing this article in print since Ben Shapiro first talked about this, months and months ago. Ben, Rebecca Fiebrink, and Peter Norvig raise the (reasonable) argument that machine learning is now a central activity in computer science, and should be a core topic in undergraduate computing curriculum. What does that mean for what we teach and how we teach it? It’s something that we ought to be talking about.

The growing importance of machine learning creates challenging questions for computing education…

Changes to the Introductory Sequence…These same two aims can also describe introductory courses for an ML-as-core world. We do not envision that ML methods would replace symbolic programming in such courses, but they would provide alternative means for defining and debugging the behaviors of functions within students’ programs. Students will learn early on about two kinds of notional machine—that of the classical logical computer and that of the statistical model. They will learn methods for authoring, testing, and debugging programs for each kind of notional machine, and learn to combine both models within software systems.

We imagine that future introductory courses will include ML through the use of beginner-friendly program editors, libraries, and assignments that encourage students to define some functions using ML, and then to integrate those functions within programs that are authored using more traditional methods. For instance, students might take a game they created in a prior assignment using classical programming, and then use ML techniques to create a gestural interface (for example, using accelerometers from a smartphone, pose information from a webcam, or audio from a microphone) for moving the player’s character up, down, left, and right within that game. Such assignments would engage students in creating or curating training examples, measuring how well their trained models perform, and debugging models by adjusting training data or choices about learning algorithms and features.

 

Source: How Machine Learning Impacts the Undergraduate Computing Curriculum

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4 Comments Add your own

  • 1. Alfred Thompson  |  November 16, 2018 at 12:02 pm

    How long will it take machine learning to make it down to the high school level I wonder? As few HS teachers we have ready to teach CS at all we have even fewer who know ML enough to teach it.

    Reply
  • 2. Clark Scholten  |  November 18, 2018 at 8:41 am

    Thanks for the information on Machine Learning. I am inspired to sit down and start going through an intro to machine learning book over the fall break. It seems like an interesting topic.

    I would love to see this also brought to high school classrooms, but agree with Alfred that it will be difficult. If there were a curriculum that was available, there may be some teachers that are willing to make that jump. Do you know of a curriculum or one that is being created?

    Reply
    • 3. Ben Shapiro  |  November 18, 2018 at 7:01 pm

      Check out Rebecca’s MOOC on Kadenze. I think it’s excellent. She will also have a piece in TOCE next year that discusses her pedagogical approach and what she’s learned about student learning.

      Reply
  • 4. Ben Shapiro  |  November 18, 2018 at 6:59 pm

    Hi,

    We don’t have a curriculum for HS yet, but one of my PhD students — Abigail Zimmerman-Niefield — is working on this.

    Her work involves kids and coaches building gestural classifiers to help themselves to get real-time feedback on athletic skills. We are starting by partnering with middle and high school athletes and sports coaches, and will eventually involve math teachers as well. Next step after that will be dance teachers.

    Beyond the excitement of working on this topic, we are thrilled to involve PE teachers and coaches, folks who thus far haven’t been especially engaged in CS Ed but have lots of expertise to offer about modeling movement (that’s a hypothesis).

    Happy to answer questions.

    Ben

    Reply

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