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MOOC-Learner-Quantified

Quantifies the MOOC learner behavior with MOOC-Learner-Quantified (MLQ)

Requirements

(see MOOC-Learner-Docker/quantified_base_img )

Technologies

Installation

See MOOC-Learner-Docker

Tutorial

Entry point is autorun.py. Configuration is done with config/*yml, see e.g. config/sample_config.yml.

Two steps of adding a new feature extraction script to MLQ

  • Add an entry to the
  • Add a MySQL script to

Feature Tables

Each feature table is describing one or multiple objects, where objects include but are not limited to user, video, problem, forum threads. There are two types of feature tables, longitudinal and non-longitudinal ones. If we split a feature by the number of week it belongs to in a course, we get longitudinal features. Only user longitudinal feature table is useful for dropout prediction. But visualization can work on all feature tables and non-longitudinal features may provide more meaningful plots than longitudinal ones.

Existing features

Scripts for extracting features are in feature_populate/scripts. Features are described in docs/README.md

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Quantifies the MOOC learner behavior with MOOC-Learner-Quantified (MLQ)

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