Metadata-Version: 1.1
Name: edxclassify
Version: 0.10.a1
Summary: A machine learning workflow, with classifiers to detect                affect in MOOC discussion forums.
Home-page: https://github.com/akshayka/edxclassify
Author: Akshay Agrawal, Shane Leonard
Author-email: akshayka@cs.stanford.edu, shanel@stanford.edu
License: GNU General Public License v2
Description: Classifying MOOC Discussion Forums
        ==================================
        
        The edxclassify package contains a classification suite built to
        detect learner affect and behavior in the discussion forums of Massive
        Open Online Courses (MOOCs). It is a result of a year's worth of research
        at Stanford and was designed to enable the discovery of insights into
        forums attached to Stanford's `online course
        offerings <https://lagunita.stanford.edu/>`_.
        
        Research and Motivation
        ------------------------
        Our motivation to discover insights into the dynamics of these courses is
        three-fold. In particular, we wish to:
        
        1. better understand the educational and learning process,
        2. improve the educational environment for MOOC learners, and
        3. empower instructors by equipping them with a suite of tools designed to
           ease the burden of teaching large-scale classes.
        
        For example, we could use a classifier that detects confusion in forum posts
        to help us target automated learning interventions in courses. We have built
        such a prototype; to learn more, please refer to our
        `paper <http://debugmind.com/youedu.pdf>`_, published in the eighth conference
        on Educational Data Mining.
        
        Included Classifiers
        ---------------------
        The abstractions in edxclassify are general enough to be applicable
        to most classification tasks. The repository does come packaged
        with a set of classifiers that were trained to detect affect in Stanford's
        MOOC discussion forums. Since Stanford's courses are powered by edX, these
        classifiers should be compatible with any edX MOOC; they should also be
        compatible with other flavors of MOOCs, as their feature space is not
        tightly coupled with the particulars of edX.
        
        These classifiers were trained
        on subsets of the `Stanford MOOC-Posts
        Dataset <http://datastage.stanford.edu/StanfordMoocPosts/>`_,
        a collection of 30,000 human-tagged forum posts, originating from a
        variety of courses. Classifiers to detect all six of the core variables
        in the MOOC-Posts Dataset -- confusion, urgency, sentiment, question,
        answer, and opinion -- are included in this repository. The class
        ``edxclassify.live_clf.LiveCLF`` provides an interface to them; see the module
        ``edxclassify.live_clf`` for further documentation.
        
        
        Running Experiments
        -------------------
        ``edxclassify.harness`` is a driver that facilitates the training and testing of
        and experimentation with classifiers. After installation, it can be invoked
        with the command ``clfharness``.
        
        Data
        ----
        The MOOC-Posts Dataset is available to researchers,
        `upon request <http://datastage.stanford.edu/StanfordMoocPosts/>`_.
        
        Much of the included code in edxclassify was designed for data formatted
        as per ``edxclassify.feature_spec``; in particular, the harness takes
        data files each containing a pickled list of examples, each example a list
        with features in the positions specified in ``edxclassify.feature_spec``.
        If you would like access to these data files, first request access to the
        MOOC-Posts Dataset. When your request is approved, send an email to
        akshayka ~at~ cs.stanford.edu with subject line
        "edxclassify: request for data files".
        
        Installation
        -------------
        Installation can proceed in two ways: from source or from pip. Note that
        when installing from pip, only a subset of the pre-trained classifiers found
        in this repository will be included, due to size constraints imposed by pypi.
        In particular, the pypi version only includes classifiers for confusion
        trained on technical and non-technical courses, whereas the source version
        includes classifiers for all six MOOC-Posts variables.
        
        Regardless of whether you install from source or from pip, begin by installing
        `scikit-learn <http://scikit-learn.org/dev/install.html>`_ and its
        dependencies; make sure to install version 0.15.2 to ensure compatibility with
        skll.
        
        If installing from source, clone this repository and simply run
        ``python setup.py install``. Otherwise, run ``pip install edxclassify``.
        
Keywords: machine-learning classification education mooc moocs forum              discussion
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: GNU General Public License v2 (GPLv2)
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
