Open edX中的AI应用

  • Harvard University and Microsoft.
  • ALOSI (Adaptive Learning Open Source Initiative) is based on creating an open source adaptive engine powering individualized learning and assessment pathways.
  • Collaborative-based Personalization 
  • Zachary A. Pardos, Assistant Professor UC Berkeley
  • Data-Driven Syllabus Adaptation
  • Data & AI as a translation between expert/novice learner models
  • RNNs langue models adapted to course navigation sequences. Intended to pick up on patterns among successful learners.
  • Course Recommendation Systems with Open edX Data
  • Chinmay Nivargi, Stanford University 
  • Content-based Recommenders
  • How similar is Course A to Course B?
    Represent each course’s bag-of-words as a vector
    TF-IDF (Term Frequency – Inverse Document Frequency) was used as the weighting for every word
  • Courses previously completed by user, Courses rated by user (if available), Any other course – user relationship data (e.g. topics, region, etc.)

AI Grading

https://edx-ora-2.readthedocs.io/en/latest/architecture/ai_grading.html

Training a classifier using a supervised machine learning algorithm.
Grading student essays using a trained classifier.
Both training and grading require more time than is acceptable within the request-response cycle of a web application. Therefore, both training and grading must occur asynchronously.

The architecture should not constrain the ML algorithm (or algorithms) used by a particular implementation. It should be possible to replace the ML algorithm with any supervised learning algorithm that produces a text classifier.

We also avoid constraining the particular task queue implementation. In principle, any task queue that provides basic reliability guarantees and a retry mechanism will work (see Entities).

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