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Zachary Hancock authored
* docs: readme badges and refactor * feat: add backstage catalog-info
Zachary Hancock authored* docs: readme badges and refactor * feat: add backstage catalog-info
LTI Consumer Implementations
`LTI 1.1`_
`LTI 1.3`_
Custom LTI Parameters
This XBlock sends a number of parameters to the provider including some optional parameters. To keep the XBlock
somewhat minimal, some parameters were omitted like lis_person_name_full
among others.
At the same time the XBlock allows passing extra parameters to the LTI provider via parameter processor functions.
Defining an LTI Parameter Processor
The parameter processor is a function that expects an XBlock instance, and returns a dict
of
additional parameters for the LTI.
If a processor throws an exception, the exception is logged and suppressed.
If a processor returns None
or any falsy value, no parameters will be added.
def team_info(xblock):
course = get_team(xblock.user, lti_params.course.id)
if not course:
return
return {
'custom_course_id': unicode(course.id),
'custom_course_name': course.name,
}
A processor can define a list of default parameters lti_xblock_default_params
,
which is useful in case the processor had an exception.
It is recommended to define default parameters anyway, because it can simplify the implementation of the processor function. Below is an example:
def dummy_processor(xblock):
course = get_team(xblock.user, lti_params.course.id) # If something went wrong default params will be used
if not course:
return # Will use the default params
return {
'custom_course_id': unicode(course.id),
'custom_course_name': course.name,
}
dummy_processor.lti_xblock_default_params = {
'custom_course_id': '',
'custom_course_name': '',
}
If you're looking for a more realistic example, you can check the Tahoe LTI repository at the Appsembler GitHub organization.
Configuring the Parameter Processors Settings
Using the standard XBlock settings interface the developer can provide a list of processor functions:
Those parameters are not sent by default. The course author can enable that on per XBlock instance
(aka module) by setting the Send extra parameters to true
in Studio.
To configure parameter processors add the following snippet to your Ansible variable files:
EDXAPP_XBLOCK_SETTINGS:
lti_consumer:
parameter_processors:
- 'customer_package.lti_processors:team_and_cohort'
- 'example_package.lti_processors:extra_lti_params'
Dynamic LTI Custom Parameters
This XBlock gives us the capability to attach static and dynamic custom parameters in the custom parameters field, in the case we need to declare a dynamic custom parameter we must set the value of the parameter as a templated parameter wrapped with the tags '${' and '}' just like the following example:
["static_param=static_value", "dynamic_custom_param=${templated_param_value}"]
Defining a dynamic LTI Custom Parameter Processor
The custom parameter processor is a function that expects an XBlock instance, and returns a string
which should be the resolved value.
Exceptions must be handled by the processor itself.
def get_course_name(xblock):
try:
course = CourseOverview.objects.get(id=xblock.course.id)
except CourseOverview.DoesNotExist:
log.error('Course does not exist.')
return ''
return course.display_name
Note. The processor function must return a string
object.
Configuring the LTI Dynamic Custom Parameters Settings
The setting LTI_CUSTOM_PARAM_TEMPLATES must be set in order to map the template value for the dynamic custom parameter as the following example:
LTI_CUSTOM_PARAM_TEMPLATES = {
'templated_param_value': 'customer_package.module:func',
}
- 'templated_param_value': custom parameter template name.
- 'customer_package.module:func': custom parameter processor path and function name.