Core Commands

Imported as:

from pedal import *

set_success(justification: str = None)

Set this submission as correct/complete/successful. Typically resolved as overriding all other feedback. Often triggered as part of a conditional. You can optionally set an (internal) justification message for why this was triggered.

set_success()

compliment(message: str, value: float = 0, justification: str = None)

compliment(message_template: str, fields=dict)

Provides a message to the student complimenting them on something they did correctly. You can also specify a numeric value that will be added as partial credit. Finally, you can optionally set an internal justification message for why this was triggered.

compliment("Your `for` loop looks correct!")

If you provide a message_template keyword parameter instead of a message, you can have fields with proper formatting. Fields can either be provided explicitly as a dictionary, or as additional keyword arguments (assuming they do not overlap with any Feedback Function parameters).

compliment(message_template="Hello {name:python_code}!", fields={"name": "Ada"})

give_partial(value: float, justification: str = None)

Increases the student’s overall score by the numeric value. Finally, you can optionally set an internal justification message for why this was triggered.

give_partial(.5)

explain(message: str, label: str = 'explain', justification: str = None)

explain(message_template: str, fields: dict)

A core feedback function, used to display a specific feedback message response to the learner. The label can be given to help track this piece of feedback; we strongly recommend always including a Feedback Label when you can! You can also provide the internal justification text to give some debug information about the justification for this feedback. By default, this feedback’s priority tends to override almost any other kind besides syntax errors.

explain("You need to use a For loop for this problem!")

explain("You need to use a For loop for this problem!", label="do_not_use_for_loop")

gently(message: str, label: str, justification: str = None)

gently(message_template: str, fields: dict)

A core feedback function, used to display a specific feedback message to the learner. Basically the same as the explain(), except the priority is much lower. This will not override runtime or algorithmic errors. The label can be given to help track this piece of feedback; we strongly recommend always including a Feedback Label when you can! You can also provide the internal justification text to give some debug information about the justification for this feedback.

gently("Your solution looks close, but you haven't incorporated any multiplication.")

gently("Your solution looks close, but you haven't incorporated any multiplication.",
       label="need_multiplication")

guidance(message: str, label: str = 'guidance', justification: str = None)

guidance(message_template: str, fields: dict)

Used to display guiding feedback such as a hint or revised instructions via the message to the learner. You are recommended to include a Feedback Label, but not obligated. You can also optionally include a justification.

guidance("Great work so far - now try incorporating a variable!")

guidance("Great work so far - now try incorporating a variable!",
         label="incorporate_variable")

hide_correctness()

Special function to signal to the environment to not report whether or not this submission was correct. Useful in exam situations where you do not want to explicitly state whether the student has a correct answer even if the system can detect it.; not that we take a stance on whether that’s a sound strategy pedagogically.

hide_correctness()

suppress(category: str = None, label: str = None)

Override whether a particular Feedback Category or an individual Feedback Label should be displayed to the user. You can specify one or both, and it will refine the answer accordingly.

from pedal.core.feedback_category import FeedbackCategory

# Suppress Type Errors
suppress(FeedbackCategory.RUNTIME, "TypeError")
# Suppress all TIFA feedback
suppress(FeedbackCategory.ALGORITHMIC)
# Suppress any occurrences of this custom feedback label
suppress(label="wrong_output")

log(message)

A muted feedback type meant for internal system usage, will not be shown to the student. You can use this to log data that will be viewable as an instructor, since printing is often not available when writing grading scripts.

log("This line of code was reached!")

debug(message)

Similar to log(), a muted feedback type meant for internal system usage. However, this one should be used to indicate that something went wrong.

debug("The conditional should be false, but was true instead.")

clear_report()

Removes all existing data from the report, including any submissions, suppressions, feedback, and Tool data. Essentially resets the entire grading script. Be careful about using this one!

clear_report()

contextualize_report(submission: str or Submission, filename: str = 'answer.py', clear: bool = True)

Updates the report with the submission. You can pass in either an actual Submission or a string representing the code of the submission, for the first parameter. The second parameter is unused if you pass in a Submission object, but otherwise will be used as the filename of the student’s code. You can optionally choose to avoid clearing out any existing report information, but by default, clears out any old information in the report. Most environments handle contextualizing the report for you, but you might want to do so yourself. You can use the Source tool to temporarily override the current code for the submission (e.g., to handle follow-up evaluations). Using a full Submission object is more powerful, since you can attach additional meta information and specify additional files.

# Simple two-argument version
contextualize_report("print('hello world')", "answer.py")

# More complex configuration using a Submission
from pedal.core.submission import Submission
files = {"answer.py": "print(open('input.txt').read())", "input.txt": "1\n31\n44\n2"}
contextualize_report(Submission(files, user={"name": "Ada Bart"}))

get_all_feedback() → List[Feedback]

Retrieves a list of all the Feedback currently attached to the report, which you could inspect in order to reuse conditions or even perhaps modify responses. Not very convenient currently, but we will improve the interface if there is demand.

labels = [feedback.label for feedback in get_all_feedback()]
if "wrong_output" in labels and "not_printing" in labels:
    gently("Looks like you are not printing and you have the wrong output!")