ML Education

Using Logistic Regression as a Proficiency Model

• Mean problems done to reach proficiency: ideally we like to minimize this so that students can spend less time rote grinding on problems they know well, and move on to other concepts.

• P(next problem correct | just gained proficiency): Unfortunately, this is hard to correctly measure in our offline data set due to the streak-of-10 bias: students may loosen up after they gain proficiency and spend less time on subsequent problems.

• Proficiency Rate: The percent of proficiencies attained per user-exercise pair. Again, this is hard to measure because of the streak bias.

• Confusion matrix for predicted next problem correct: This is for comparing binary classifiers on their accuracy in predicting the outcome of any answer in a user’s response history. We build up the confusion matrix, and from that extract two valuable measures of the performance of a binary classifier.

• Metric gathering

  • Response time
  • Short and Long term memory
    • Retention
  • Background color transitions
  • Sounds generated
  • The speed of deliver
  • Proficiencies earned per user
  • New exercises attempted per user
  • Problems done per proficiency
  • Accuracy: P(next problem correct | just gained proficiency)

• NLP

  • Chatbot response to encouragement and praise
  • Analysis of positive reinforcement
  • Discouragement analysis

Let’s say we have the values of input features, and we stuff them into a vector x. Let’s say we also happen to know how much each feature makes it more likely that the user is proficient, and stuff those weights into a vector w. We can then take the weighted sum of the input features, plus a pre-determined constant to correct for any constant bias, and call that z:

• A/B Testing
• Logistic Regression

A/Bingo split testing now on App Engine, built for Khan Academy https://bjk5.com/post/10171483254/abingo-split-testing-now-on-app-engine-built-for

Khan Academy Exercises https://github.com/Khan/khan-exercises

Sensitivity and specificity https://en.wikipedia.org/wiki/Sensitivity_and_specificity

Least Squares Fitting--Exponential http://mathworld.wolfram.com/LeastSquaresFittingExponential.html To fit a functional form

hiteshsahu/Android-TTS-STT

One line solution for Android Text to speech(TTS) & Speech to Text(STT) translation problem https://github.com/hiteshsahu/Android-TTS-STT.git

animated-gradient-background-tutorial Gradient Background

Animated Gradient Background in Android :- http://www.androidtutorialshub.com/animated-gradient-background-in-android/

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Research

Curriculum for Reinforcement Learning

• https://lilianweng.github.io/lil-log/2020/01/29/curriculum-for-reinforcement-learning.html

  @article{weng2020curriculum,
    title   = "Curriculum for Reinforcement Learning",
    author  = "Weng, Lilian",
    journal = "lilianweng.github.io/lil-log",
    year    = "2020",
    url     = "https://lilianweng.github.io/lil-log/2020/01/29/curriculum-for-reinforcement-learning.html"
  }
  

Learning Curriculum Policies for Reinforcement Learning

• https://arxiv.org/abs/1812.00285