Course Feedback Surveys
Collect end-of-semester course evaluations via phone for students who didn't complete online surveys, improving response rates and curriculum quality.
Why this matters
Improve enrollment, retention, and outcomes with voice automation for student engagement, attendance, and fundraising. This example shows you how to automate course feedback surveys calls end-to-end with Guava's SDK — no telephony plumbing, no prompt engineering, just Python.
Installation
Install from Guava's private PyPI index. A public package is coming soon — the install command will simplify to pip install guava.
# Step 1: Install Guava
pip install gridspace-guava --extra-index-url https://guava-pypi.gridspace.com
# Public PyPI package coming soon — the install command will simplify to:
# pip install guava
# Step 2: Set your credentials
export GUAVA_API_KEY="..."
export GUAVA_AGENT_NUMBER="..."How it's built
Every Guava agent is a Python class. Walk through the key sections below, then grab the complete file at the end.
Imports
Import the Guava SDK and any helpers you need. guava.CallController is the base class for every voice agent.
# Install: pip install gridspace-guava --extra-index-url https://guava-pypi.gridspace.com
import guava
import osAgent setup
set_persona() defines how the agent presents itself. set_task() gives it its mission in plain English. The checklist drives the conversation — Guava works through it top-to-bottom, collecting Field values and speaking Say items as it goes.
class CourseFeedbackBot(guava.CallController):
def __init__(self, student_name: str, course_name: str, instructor_name: str,
semester: str):
super().__init__()
self.set_persona(
organization_name="Westland University Academic Affairs",
agent_name="Sam",
)
self.set_task(
objective=f"Collect {semester} course evaluation from {student_name} for {course_name}",
checklist=[
f"Thank {student_name} for taking time to provide feedback on {course_name}.",
guava.Field(
key="overall_course_rating",
field_type="rating",
description="Overall course quality rating (1-5)",
),
guava.Field(
key="instructor_rating",
field_type="rating",
description=f"Effectiveness of instructor {instructor_name} (1-5)",
),
guava.Field(
key="materials_rating",
field_type="rating",
description="Quality of course materials and readings (1-5)",
),
guava.Field(
key="workload_appropriate",
field_type="bool",
description="Was the course workload appropriate for the credit hours?",
),
guava.Field(
key="would_recommend",
field_type="bool",
description="Would they recommend this course to other students?",
),
guava.Field(
key="open_feedback",
field_type="text",
description="Any specific suggestions for improving the course?",
required=False,
),
"Thank the student and mention that feedback is reviewed each semester.",
],
on_complete=lambda fields: print(f"Course eval received: {course_name} — {student_name} overall={fields.get('overall_course_rating')}"),
)
guava.dial(
controller=CourseFeedbackBot,
controller_args={"student_name": "Destiny Okonkwo",
"course_name": "Introduction to Machine Learning",
"instructor_name": "Prof. Chen",
"semester": "Fall 2024"},
to=os.environ["STUDENT_PHONE"],
agent_number=os.environ["GUAVA_AGENT_NUMBER"],
api_key=os.environ["GUAVA_API_KEY"],
)Platform performance
<1s
Response time
99.99%
Uptime SLA
13+
Industries served
Full example
The complete file — copy it, save it as example.py, and run it.
# Install: pip install gridspace-guava --extra-index-url https://guava-pypi.gridspace.com
import guava
import os
class CourseFeedbackBot(guava.CallController):
def __init__(self, student_name: str, course_name: str, instructor_name: str,
semester: str):
super().__init__()
self.set_persona(
organization_name="Westland University Academic Affairs",
agent_name="Sam",
)
self.set_task(
objective=f"Collect {semester} course evaluation from {student_name} for {course_name}",
checklist=[
f"Thank {student_name} for taking time to provide feedback on {course_name}.",
guava.Field(
key="overall_course_rating",
field_type="rating",
description="Overall course quality rating (1-5)",
),
guava.Field(
key="instructor_rating",
field_type="rating",
description=f"Effectiveness of instructor {instructor_name} (1-5)",
),
guava.Field(
key="materials_rating",
field_type="rating",
description="Quality of course materials and readings (1-5)",
),
guava.Field(
key="workload_appropriate",
field_type="bool",
description="Was the course workload appropriate for the credit hours?",
),
guava.Field(
key="would_recommend",
field_type="bool",
description="Would they recommend this course to other students?",
),
guava.Field(
key="open_feedback",
field_type="text",
description="Any specific suggestions for improving the course?",
required=False,
),
"Thank the student and mention that feedback is reviewed each semester.",
],
on_complete=lambda fields: print(f"Course eval received: {course_name} — {student_name} overall={fields.get('overall_course_rating')}"),
)
guava.dial(
controller=CourseFeedbackBot,
controller_args={"student_name": "Destiny Okonkwo",
"course_name": "Introduction to Machine Learning",
"instructor_name": "Prof. Chen",
"semester": "Fall 2024"},
to=os.environ["STUDENT_PHONE"],
agent_number=os.environ["GUAVA_AGENT_NUMBER"],
api_key=os.environ["GUAVA_API_KEY"],
)Run it
Start the agent. It will connect to Guava's infrastructure and begin accepting calls on your assigned number.
python example.py