SBG and Leveling Up - Part 2: Machine Learning

In my 100-point scale series last June, I wrote about how our system does a pretty cruddy job of classifying students based on raw point percentages. In a later post in that series, I proposed that machine learning might serve as a way to make sense of our intuition around student achievement levels and help provide insight into refining a rubric to better reflect a student's ability.

In I last post, I wrote about my desire to become more methodical about my process of deciding how a student moves from one standard level to the next. I typically know what I'm looking for when I see it. Observing students and their skill levels relative to a given set of tasks is often required to identify the level of a student students. Defining the characteristics of different levels is crucial to communicating those levels to students and parents, and for being consistent among different groups. This is precisely what we intend to do when we define a rubric or grading scale.

I need help relating my observations of different factors to a numerical scale. I want students to know clearly what they might expect to get in a given session. I want them to understand my expectations of what is necessary to go from a level 6 to a level 8. I don't believe I have the ability to design a simple grid rubric that describes all of this to them though. I could try, sure, but why not use some computational thinking to do the pattern finding for me?

In my last post, I detailed some elements that I typically consider in assigning a level to a student: previously recorded level, question difficulty, number of conceptual errors, and numbers of algebraic, and arithmetic errors. I had the goal of creating a system that lets me go through the following process:

  • I am presented with a series of scenarios with different initial scores, arithmetic errors, conceptual errors, and so on.
  • I decide what new numerical level I think is appropriate given this information. I enter that into the system.
  • The system uses these examples to make predictions for what score it thinks I will give a different set of parameters. I can choose to agree, or assign a different level.
  • With sufficient training, the computer should be able to agree with my assessment a majority of the time.

After a lot of trial and error, more learning about React, and figuring out how to use a different machine learning library than I used previously, I was able to piece together a working prototype.

You can play with my implementation yourself by visiting the CodePen that I used to write this. The first ten suggested scores are generated by increasing the input score by one, but the next ten use the neural network to generate the suggested scores.

In my next post in this series, I'll discuss the methodology I followed for training this neural network and how I've been sharing the results with my students.

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