Knewton : Gravitating towards data-driven education

-
There are multiple companies that have sprung up in the past decade which have used big data to create novel products that might completely change they way we think about education itself. Knewton (probably a famous name spelled wrong) is a company in the data-driven education space that derives insights from extended interaction of students with their course material and adapts the material to the learner’s pace, strengths and  weaknesses. The platform provides features like tracking day-to-day activity of a student to determine their “most efficient” study time and what materials suit them and then dynamically generating varied learning plans tailored to a student’s specific needs. What is more exciting about this company is that it was started in 2008 and has now grown to involve textbook publishers, universities and software companies.
Knewton’s CEO Jose Ferreira, provides an approximate classification of data gathering categories that the platform uses.


  • Identity data - mainly used to identify user and provide access based on policies
  • User interaction data - involves metrics like click rate, page views and bounce rates
  • Inferred content data - to determine quality of study material
  • System-wide data - attendance records, grades at the end of course, history
  • Inferred student data - performance analysis of student based on holistic factors (distraction, forgetfulness, study material quality/type )
Student life might just get better years later. 

Cheers to their idea! 

Comments

Post a Comment

Popular posts from this blog

Why computers aren't amazingly smart

-
Artificial intelligence is the field of computer science with the goal of making computer more useful than they already are by making them receptive to their environment, giving them a novel way to store, process and expand their knowledge base and letting them make informed decisions based on the accumulated knowledge. As you can already see from the above definition that the field has a variety of sub-fields or places where one can contribute. Let's begin by talking about what motivates artificial intelligence. How can the task of making decisions in machines ( which is mostly inspired by how humans think and act ) be understood as a process ? The best way to draw a parallel is to keep in mind that what the machine is essentially trying to do is act like humans albeit without a perfect resemblance. The state of art thinking machines are not even as good as a teenager. In fact, most would not be able to do simple tasks like differentiating a girl from a boy as fast and as accur...
Read more

Making Machines : Introduction

-
Machines are now ubiquitous but once they were just thoughts in a crazy man's mind. Well actually, many men but for our understanding there were three people who set the ball rolling. 1. Alan Turing 2. Alonzo Church 3. Kurt Godel All these men were, in their own ways, trying to figure out a mathematical model for a machine. For all those readers who lost me here should think of a mathematical model as the way of describing anything around you with pen and paper. It's almost like taking notes but mathematics is the art of taking notes about your notes and doing all that till you make sense of what is possible by twisting what you know in ways you couldn't have directly imagined. Math is just a language and it's not necessary for all of us to think in that language. My blog here is another effort among many to explain what "computation" means and how is it different and similar to the word "computer" we use every day now. Think about what yo...
Read more

Data Mining and R/Rattle : First Experiment

-
Data mining is the activity of harnessing useful insights from vast amount of data based on a certain model. Instead of one particular model or way analysis, data mining is context-based and may potentially employ multiple models and can work on a variety of sources of data including text, audio, video, images etc. Data mining is an ongoing process and there are scenarios where the development of the data model stagnates are relatively few. This is because the mining insights act as feedback for a better model and hence, generally the process is much like agile software development. The role of a data miner in an organization begins with an understanding of the domain and data itself. This process is crucial to defining and refining the model that is the heart of the mining process. A model is constructed and critiqued by domain experts and data experts. This completes one cycle of data mining and the cycle continues in the same way every time using the insights generated from th...
Read more