A framework for creating channels on Kolibri Studio.

• Install pip if you don't have it already.

• Run pip install ricecooker

• You can now reference ricecooker using import ricecooker in your .py files

A sample program has been created here

• Initializing the Channel In order for the rice cooker to run properly, you must include a create_channel method in your target py file that returns a Channel model. This function will be responsible for building a tree based on ricecooker.classes.

  Start by importing Channel from ricecooker.classes.nodes and create a Channel model. The Channel model has the following fields: - channel_id (str): channel's unique id - domain (str): who is providing the content (e.g. learningequality.org) - title (str): name of channel - description (str): description of the channel (optional) - thumbnail (str): local path or url to image file (optional)

  For example:

   from ricecooker.classes.nodes import Channel
  
   def construct_channel(args):
  
       channel = Channel(
           domain="learningequality.org",
           channel_id="rice-channel",
           title="Rice Channel",
       )
       _build_tree(channel, <source tree>) 	# see sample_program.py for example build_tree function
  
       return channel
  

• Building the Tree Once your channel is created, you can start adding content. To do this, you will need to convert your data to the rice cooker's models. Here are the model types that are available to you:

  • Topic (folders to add hierarchy to the channel's content)
  • Video (mp4)
  • Audio (mp3 or wav)
  • Document (pdf)
  • Exercise (assessment-based content with questions)

  The ricecooker.classes.nodes module has the function guess_content_kind, which takes in a file or list of files as well as a list of questions (if available) and determines what model best suits those files (if no match could be found, an UnknownContentKindError will be raised). For example:

  	>> guess_content_kind([])
  	'topic'
  	>> guess_content_kind(["http://path/to/some/file.mp4"])
  	'video'
  	>> guess_content_kind([], ["Question?"])
  	'exercise'
  

  Once you have created the model, add it to a parent node with <parent-node>.add_child(<child-node>)

• Adding Exercises Exercises are special model kinds that have questions used for assessment. In order to set the criteria for completing exercises, you must set exercise_data to equal a dict containing a mastery_model field based on the mastery models provided under le_utils.constants.exercises. If no data is provided, the rice cooker will default to mastery at 3 of 5 correct. For example:

   node = Exercise(
   	exercise_data={'mastery_model': exercises.M_OF_N, 'randomize': True, 'm': 3, 'n': 5},
   	...
   )
  

  To add a question to your exercise, you must first create a question model from ricecooker.classes.questions. Your program is responsible for determining which question type to create. Here are the available question types:

  • PerseusQuestion: special question type for pre-formatted perseus questions
  • MultipleSelectQuestion: questions that have multiple correct answers (e.g. check all that apply)
  • SingleSelectQuestion: questions that only have one right answer (e.g. radio button questions)
  • InputQuestion: questions that have text-based answers (e.g. fill in the blank)
  • FreeResponseQuestion: questions that require subjective answers (ungraded)

  To set the correct answer(s) for input questions, you must provide an array of all of the accepted answers (answers). For multiple selection and single selection questions, you must provide a list of all of the possible choices as well as an array of the correct answers (all_answers and correct_answer(s) respectively).

  To add images to a question's question, answers, or hints, format the image path with '![](<path/to/some/file.png>)'

  Once you have created the appropriate question model, add it to an exercise model with <exercise-node>.add_question(<question>)

• Running the Rice Cooker Run python -m ricecooker uploadchannel [-v] "<path-to-py-file>" [--debug]

  • -v (verbose) will print what the rice cooker is doing
  • --debug will send data to localhost if you have Kolibri Studio running locally