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Personalizing education using deep learning

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Personalizing education using deep learning

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Personalizing education using deep learning

  1. 1. AI for Education Personalizing education using deep learning
  2. 2. Personalized education is proven to produce learning gains for the average student to the order of two standard deviations. The challenge is to apply recent breakthroughs in deep learning to make personalized education accessible to everyone — providing these benefits on a global scale. Challenge Individual Tutoring Lecture Mastery Learning Student Achievement Score Percentile
  3. 3. Harnessing recent breakthroughs in deep learning, we present an algorithm that can personalize education using only historic data. This algorithm learns dependencies in content, models how student’s learn — and subsequently present the right content at the right time. The result is more engaged students and higher proficiencies. Solution
  4. 4. In this example, we use Recurrent Neural Networks (RNNs) to recommend content for each user. The RNN family of models have important advantages over previous methods in that they do not require the explicit encoding of human domain knowledge, and can capture more complex representations of student knowledge states. RNN
  5. 5. The RNN represents the latent knowledge state, along with its temporal dynamics. Then, as a student progresses through a course, it utilizes information from previous time steps, to make predictions regarding future performance. RNN
  6. 6. The model takes the content, answers, response times and an array of contextual information as an input and process it through a number of different network layers containing millions of neuron-like connections to inform the predictions. Prediction
  7. 7. The prediction is subsequently used to analyze which piece of content that won’t be too easy nor too difficult, is most likely to keep the student engaged over time, and will result in the highest increase in proficiency over multiple time steps. Recommendation
  8. 8. During the fall of 2017, we will apply deep neural networks on one of the world’s largest educational products, and we will be delighted to share this case study with you at SXSWEdu. With knowledge, passion, and creativity we will showcase how the use of deep neural networks can increase student engagement and proficiency, as well as reduce churn. In Practice Case Study

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