Monday, March 24, 2014

The Non-Linearity of Human Learning

The main purpose of this blog is to document some of the more technical aspects of what we do here at DreamBox, the types of problems we face, how we approach them, and how we solve them.

One of the major goals of our work is to study, understand, and model the data and processes related to human learning.

When considering human learning from a software engineering point of view, nothing should be assumed to be linear. The way students acquire, retain, and progress through the information we give them is nonlinear, organic, and multidimensional in nature. At DreamBox, our software engineers quickly become data scientists and spend much of their time thinking and designing technical solutions to model the non-linearity of the human learning process and studying its organic behaviors.

From modeling student skill acquisition and how that information is retained and potentially lost over time in unpredictable ways, to visualizing these multidimensional data models to show student progress across a complex array of skills—in a way that is interpretable by educators—the challenge of making sense of the natural non-linearity of the human mind is always before us.

Here is a partial list of some of the challenges that we have been working on, and will continue to tackle during our continuous process of refinement:

  • Defining which aspects of student interactions with DreamBox are important to use when “learning from the learners the way they learn.”
  • Modeling organic data—representing student mastery of academic concepts—as a complex and ever-growing graph formed by nodes and edges that morph over time in unpredictable ways due to a multitude of reasons that we do not have full control over.
  • Modeling the elementary and middle school math curriculum in a form that is compatible both with student learning preferences and academic standards.
  • Creating several engagement layers that are fun and appropriate for children while supporting their math skill acquisition journey.
  • Collecting, storing, organizing, and processing enormous amounts of data recorded during student interactions with DreamBox.
  • Analyzing data, defining hypotheses, and testing the hypotheses through experiments to refine our understanding of current student learning models.
  • Building distributed systems that are able to scale automatically and quickly during tremendous bursts and drops of traffic on our servers that we experience throughout the day, week, and school year.
  • Continuously adapt to what the students are doing both at a real-time micro level and at an over-time macro level.
  • Building systems that allow us to visualize, study, and refine our understanding of the data we collect and the algorithms that process that data.
  • Finding methods of showing student progression in ways that are familiar to educators, by translating the enormous amount of data into efficient and functional reports with numbers and graphs.
  • Building technologies that allow educators to create lessons and content, effectively making them DreamBox lesson engineers.
In future articles we’ll explore some of the technical aspects related to these points.

Written by Lorenzo Pasqualis
Director of Engineering at DreamBox Learning

1 comment:

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