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With the rise of large online computer science courses, there
is an abundance of high-quality content. At the same time, the sheer
size of these courses makes high-quality feedback to student work more
and more difficult. Talk to any educator, and they will tell you how
instrumental instructor feedback is to a student’s learning process.
Unfortunately, giving personalized feedback isn’t cheap: for a large
online coding course, this could take months of labor. Today, large
online courses either don’t offer feedback at all or take shortcuts that
sacrifice the quality of the feedback given.
Several computational approaches have been proposed to automatically
produce personalized feedback, but each falls short: they either require
too much upfront work by instructors or are limited to very simple
assignments. A scalable algorithm for feedback to student code that
works for university-level content remains to be seen. Until now, that
is. In a recent paper, we proposed a new AI system based on
meta-learning that trains a neural network to ingest student code and
output feedback. Given a new assignment, this AI system can quickly
adapt with little instructor work. On a dataset of student solutions to
Stanford’s CS106A exams, we found the AI system to match human
instructors in feedback quality.
To test the approach in a real-world setting, we deployed the AI system
at Code in Place 2021, a large online computer science course spun out
of Stanford with over 12,000 students, to provide feedback to an
end-of-course diagnostic assessment. The students’ reception to the
feedback was overwhelmingly positive: across 16,000 pieces of feedback
given, students agreed with the AI feedback 97.9% of the time, compared
to 96.7% agreement to feedback provided by human instructors. This is,
to the best of our knowledge, the first successful deployment of machine
learning based feedback to open-ended student work.
In the middle of the pandemic, while everyone is forced to social
distance in the confines of their own homes, thousands of people across
the world were hard at work figuring out why their code was stuck in an
infinite loop. Stanford CS106A, one of the university’s most popular
courses and its largest introductory programming offering with nearly
1,600 students every year, grew even bigger. Dubbed Code in Place,
CS106A instructors Chris Piech, Mehran Sahami and Julie Zelenski wanted
to make the curriculum and teaching philosophy of CS106A publicly
available as an uplifting learning experience for students and adults
alike during a difficult time. In its inaugural showing in April ‘20,
Code in Place pulled together 908 volunteer teachers to run an online
course for 10,428 students from around the world. One year later, with
the pandemic still in full force in many areas of the world, Code in
Place kicked off again, growing to over 12,000 students and 1,120
While crowd-sourcing a teaching team did make a lot of things possible
for Code in Place that usual online courses lack, there are still limits
to what can be done with a class of this scale. In particular, one of
the most challenging hurdles was providing high-quality feedback to
What is feedback?
Everyone knows high quality content is an important
ingredient for learning, but another equally important but more subtle
ingredient is getting high quality feedback. Knowing the breakdown of
what you did well and what the areas for improvement are, is fundamental
to understanding. Think back to when you first got started programming:
for me, small errors that might be obvious to someone more experienced,
cause a lot of frustration. This is where feedback comes in, helping
students overcome this initial hurdle with instructor guidance.
Unfortunately, feedback is something online code education has struggled
with. With popular “massively open online courses” (MOOCs), feedback on
student code boils down to compiler error messages, standardized
tooltips, or multiple-choice quizzes.
You can find an example of each below. On the left, multiple choice
quizzes are simple to grade and can easily assign numeric scores to
student work. However, feedback is limited to showing the right answer,
which does little to help students understand their underlying
misconceptions. The middle picture shows an example of an opaque
compiler error complaining about a syntax issue. As a beginner learning
to code, error messages are very intimidating and difficult to
interpret. Finally, on the right, we see an example of a standardized
tooltip: upon making a mistake, a pre-specified message is shown.
Pre-specified messages tend to be very vague: here, the tooltip just
tells us our solution is wrong and to try something different.
It makes a lot of sense why MOOCs settle for subpar feedback: it’s
really difficult to do otherwise! Even for Stanford CS106A, the teaching
team is constantly fighting the clock in office hours in an attempt to
help everyone. Outside of Stanford, where classes may be more
understaffed, instructors are already unable to provide this level of
individualized support. With large online courses, the sheer size makes
any hope of providing feedback unimaginable. Last year, Code in Place
gave a diagnostic assessment during the course for students to summarize
what they have learned. However, there was no way to give feedback
scalably to all these student solutions. The only option was to release
the correct solutions online for students to compare to their own work,
displacing the burden of feedback onto the students.
Code in Place and its MOOC cousins are examples of a trend of education
moving online, which might only grow given the lasting effects of the
pandemic. This shift surfaces a very important challenge: can we provide
feedback at scale?
The feedback challenge.
In 2014, Code.org, one
of the largest online platforms for code education, launched an
initiative to crowdsource thousands of instructors to provide feedback
to student solutions [1,2]. The hope of the initiative was to tag enough
student solutions with feedback so that for a new student, Code.org
could automatically provide feedback by matching the student’s solution
to a bank of solutions already annotated with feedback by an instructor.
Unfortunately, Code.org quickly found that even after thousands of
aggregate hours spent providing feedback, instructors were only
scratching the surface. New students were constantly coming up with new
mistakes and new strategies. The initiative was cancelled after two
years and has not been reproduced since.
We might ask: why did this happen? What is it about feedback that makes
it so difficult to scale? In our research, we came up with two parallel
First, providing feedback to student code is hard work. As an
instructor, every student solution requires me to reason about the
student’s thought process to uncover what misconceptions they might have
had. If you have ever had to debug someone else’s code, providing
feedback is at least as hard as that. In a previous research paper, we found that producing
feedback for only 800 block-based programs took a teaching team a
collective 24.9 hours. If we were to do that for all of Code in Place,
it would take 8 months of work.
Second, students approach the same programming problem in an exponential
number of ways. Almost every new student solution will be unique, and a
single misconception can manifest itself in seemingly infinite ways. As
a concrete example, even after seeing a million solutions to a Code.org
problem, there is still a 15% chance that a new student generates a
solution never seen before. Perhaps not coincidentally, it turns out the
distribution of student code closely follows the famous Zipf
distribution, which reveals an extremely “long tail” of rare solutions
that only one student will ever submit. Moreover, this close
relationship to Zipf doesn’t just apply to Code.org; it is a much more
general phenomenon. We see similar patterns for student work for
university level programming assignments in Python and Java, as well as
free response solutions to essay-like prompts.
So, if asking instructors to manually provide feedback at scale is
nearly impossible, what else can we do?
“If humans can’t do it, maybe machines
can” (famous last words). After all, machines process information a lot
faster than humans do. There have been several approaches applying
computational techniques to provide feedback, the simplest of which is
unit tests. An instructor can write a collection of unit tests for the
core concepts and use them to evaluate student solutions. However, unit
tests expect student code to compile and, often, student code does not
due to errors. If we wish to give feedback on partially complete
solutions, we need to be able to handle non-compiling code. Given the
successes of AI and deep learning in computer vision and natural
language, there have been attempts of designing AI systems to
automatically provide feedback, even when student code does not compile.
Given a dataset of student code, we can ask an
instructor to provide feedback for each of the solutions, creating a
labeled dataset. This can be used to train a deep learning model to
predict feedback for a new student solution. While this is great in
theory, in practice, compiling a sufficiently large and diverse dataset
is difficult. In machine learning, we are accustomed to datasets with
millions of labeled examples since annotating an image is both cheap and
requires no domain knowledge. On the other hand, annotating student
code with feedback is both time-consuming and needs expertise, limiting
datasets to be a few thousand examples in size. Given the Zipf-like
nature of student code, it is very unlikely that a dataset of this size
can capture all the different ways students approach a problem. This is
reflected in practice as supervised attempts perform poorly on new
While annotating student code is difficult work,
instructors are really good at thinking about how students would tackle
a coding problem and what mistakes they might make along the way.
Generative grading [2,3] asks instructors to distill this intuition
about student cognition into an algorithm called a probabilistic
grammar. Instructors specify what misconceptions a student might make
and how that translates to code. For example, if a student forgets a
stopping criterion resulting in an infinite loop, their program likely
contains a “while” statement with no “break” condition. Given such an
algorithm, we can run it forward to generate a full student solution
with all misconceptions already labeled. Doing this repeatedly, we
curate a large dataset to train a supervised model. This approach was
very successful on block-based code, where performance rivaled human
instructors. However, the success of it hinges on a good algorithm.
While tractable for block-based programs, it became exceedingly
difficult to build a good algorithm for university level assignments
where student code is much more complex.
The supervised approach requires the instructor to curate a dataset of
student solutions with feedback where as the generative grading approach
requires the instructor to build an algorithm to generate annotated
data. In contrast, the meta-learning approach requires the instructor to
annotate feedback for K examples across N programming problems. K is
typically very small (~10) and N not much larger (~100).
Meta-learning how to give feedback.
So far, neither approach is quite
right. In different ways, supervised learning and generative grading
both expect too much from the instructor. As they stand, for every new
coding exercise, the instructor would have to put in days, if not weeks
to months of effort. In an ideal world, we would shift more of the
burden of feedback onto the AI system. While we would still like
instructors to play a role, the AI system should bear the onus of
quickly adapting to every new exercise. To accomplish this, we built an
AI system to “learn how to learn” to give feedback.
Meta-learning is an old idea from the 1990s [9, 10] that has seen a
resurgence in the last five years. Recall that in supervised learning a
model is trained to solve a single task; in meta-learning, we solve many
tasks at once. The catch is that we are limited to a handful of labeled
examples for every task. Whereas supervised learning gets lots of labels
for one task, we spread the annotation effort evenly across many tasks,
leaving us with a few labels per task. In research literature, this is
called the few-shot classification problem. The upside to meta-learning
is that after training, if your model is presented with a new task that
it has not seen before, it can quickly adapt to solve it with only a
“few shots” (i.e., a few annotations from the new task).
So, what does meta-learning for feedback look like? To answer that, we
first need to describe what composes a “task” in the world of
educational feedback. Last year, we compiled a dataset of student
solutions from eight CS106A exams collected over the last three academic
years. Each exam consists of four to six programming exercises in which
the student must write code (but is unable to run or compile it for
testing). Every student solution is annotated by an instructor using a
feedback rubric containing a list of misconceptions tailored to a single
problem. As an example, consider a coding exercise that asks the student
to write a Python program that requires string insertion. A potential
feedback rubric is shown in the left image: possible misconceptions are
inserting at the wrong location or inserting the wrong string. So, we
can treat every misconception as its own task. The string insertion
example would comprise of four tasks.
One of the key ideas of this approach is to frame
the feedback challenge as a few-shot classification problem. Remember
that the reasons why previous methods for automated feedback struggled
were the (1) high cost of annotation and (2) diversity of student
solutions. Casting feedback as a few-shot problem cleverly circumvents
both challenges. First, meta-learning can leverage previous data on old
exams to learn to provide feedback to a new exercise with very little
upfront cost. We only need to label a few examples for the new exercise
to adapt the meta-learner and importantly, do not need to train a new
model from scratch. Second, there are two ways to handle diversity: you
can go for “depth” by training on a lot of student solutions for a
single problem to see different strategies, or you can go for “breadth”
and get sense of diverse strategies through student solutions on a lot
of different problems. Meta-learning focuses its efforts on capturing
“breadth”, accumulating more generalizable knowledge that can be shared
We will leave the details of the meta-learner to the technical report.
In short, we propose a new deep neural network called a ProtoTransformer
Network that combines the strengths of BERT from natural language
processing and Prototypical Networks from few-shot learning literature.
This architecture, in tandem with technical innovations – creating
synthetic tasks for code, self-supervised pretraining on unlabeled code,
careful encoding of variable and function names, and adding question and
rubric descriptions as side information – together produce a highly
performant AI system for feedback. To help ground this in context, we
include three examples on the bottom of the last page of the AI system
predicting feedback to student code. The predictions were taken from
actual model output on student submissions.
Aside from looking at qualitative examples, we can
measure its performance quantitatively by evaluating the correctness of
the feedback an AI system gave on exercises not used in training. A
piece of feedback is considered correct if a human instructor annotated
the student solution with it.
We consider two experimental settings for evaluation:
Held-out Questions: we randomly pick 10% of questions across all exams
to evaluate the meta-learner. This simulates instructors providing
feedback for part of every exam, leaving a few questions for the AI to
give feedback for.
Held-out Exams: we hold out an entire exam for evaluation. This is a
much harder setting as we know nothing about the new exam but also most
faithfully represents an autonomous feedback system.
We measure the performance of human instructors by asking several
teaching assistants to grade the same student solution and recording
agreement. We also compare the meta-learner to a supervised baseline. As
shown in the graph on the previous page, the meta-learner outperforms
the supervised baseline by up to 24 percentage points, showcasing the
utility of meta-learning. More surprisingly, we find that the
meta-learner surpasses human performance by 6% in held-out questions.
However, there is still room for improvement as we fall short 8% to
human performance on held-out exams – a harder challenge. Despite this,
we find these results encouraging: previous methods for feedback could
not handle the complexity of university assignments, let alone approach,
or match the performance of instructors.
Automated feedback for Code in Place.
Taking a step back, we began
with the challenge of feedback, an important ingredient to a student’s
learning process that is frustratingly difficult to scale, especially
for large online courses. Many attempts have been made towards this,
some based on crowdsourcing human effort and others based on
computational approaches with and without AI, but all of which have
faced roadblocks. In late May ‘21, we built and tested an approach based
on meta-learning, showing surprisingly strong results on university
level content. But admittedly, the gap between ML research and
deployment can be large, and it remained to be shown that our approach
can give high quality feedback at scale in a live application. Come
June, Code in Place ‘21 was gearing up for its diagnostic assessment.
In an amazing turnout, Code in Place ‘21 had 12,000 students. But
grading 12,000 students each solving 5 problems would be beyond
intractable. To put it into numbers, it would take 8 months of human
labor, or more than 400 teaching assistants working standard
nine-to-five shifts to manually grade all 60,000 solutions.
The Code in Place ‘21 diagnostic contained five new questions that were
not in the CS106A dataset used to train the AI system. However, the
questions were similar in difficulty and scope, and correct solutions
were roughly the same length as those in CS106A. Because the AI system
was trained with meta-learning, it could quickly adapt to these new
questions. Volunteers from the teaching team helped annotate a small
portion of the student solutions that the AI meta-learning algorithm
To showcase feedback to students, we were joined by Alan Chang and together we built an application for students
to see their solutions and AI feedback (see image above). We were
transparent in informing students that an AI was providing feedback. For
each predicted misconception, we associated it with a message (shown in
the blue box) to the student. We carefully crafted the language of these
messages to be helpful and supportive of the student’s learning. We also
provided finer grained feedback by highlighting portions of the code
that the AI system weighted more strongly in making its prediction. In
the image above, the student forgot to cast the height to an integer. In
fact, the highlighted line should be height = int(input(…)), which the
AI system picked up on.
Human versus AI Feedback
For each question, we asked the student to
rate the correctness of the feedback provided by clicking either a
“thumbs up” or a “thumbs down” before they can proceed to the next
question (see lower left side of the image above). Additionally, after a
student reviewed all their feedback, we asked them to rate the AI system
holistically on a five-point scale. As part of the deployment, some of
the student solutions were given feedback by humans but students did not
know which ones. So, we can compare students’ holistic and per-question
rating when given AI feedback versus instructor feedback.
Here’s what we found:
- 1,096 students responded to a survey after receiving 15,134 pieces
of feedback. The reception was overwhelmingly positive: Across all
15k pieces of feedback, students agreed with AI suggestions 97.9% ±
0.001 of the time.
- We compared student agreement with AI feedback against agreement
with instructor feedback, where we surprisingly found the AI system
surpass human instructors: 97.9% > 96.7% (p-value 0.02). The
improvement was driven by higher student ratings on constructive
feedback – times when the algorithm suggested an improvement.
- On the five-point scale, the average holistic rating of usefulness
by students was 4.6 ± 0.018 out of 5.
- Given the wide diversity of students participating in Code in Place,
we segmented the quality of AI feedback by gender and country, where
we found no statistically significant difference across
To the best of our knowledge, this was both the first successful
deployment of AI-driven feedback to open-ended student work and the
first successful deployment of prototype networks in a live application.
With promising results in both a research and a real-world setting, we
are optimistic about the future of artificial intelligence in code
education and beyond.
How could AI feedback impact teaching?
A successful deployment of an
automated feedback system raises several important questions about the
role of AI in education and more broadly, society.
To start, we emphasize that what makes Code in Place so successful is
its amazing teaching team made up of over 1,000 section leaders. While
feedback is an important part of the learning experience, it is one
component of a larger ecosystem. We should not incorrectly conclude from
our results that AI can automate teaching or replace instructors – nor
should the system be used for high-stakes grading. Instead, we should
view AI feedback as another tool in the toolkit for instructors to
better shape an amazing learning experience for students.
Further, we should evaluate our AI systems with a double bottom line of
both performance and fairness. Our initial experiments suggest that the
AI is not biased but our initial results are being supplemented by a
more thorough audit. To minimize the chance of providing incorrect
feedback to student work, future research should encourage AI systems to
learn to say: “I don’t know”.
Third, we find it important that progress in education research be
public and available for others to critique and build upon.
Finally, this research opens so many directions moving forward. We hope
to use this work to enable teachers to better reach their potential.
Moreover, an AI feedback makes it scalable to study not just students’
final solutions, but the process of how students solve their
assignments. Finally, there is a novel opportunity for computational
approaches towards unraveling the science of how students learn.
Many thanks to Chelsea Finn, Chris Piech, and Noah
Goodman for their guidance. Special thanks to Chris for his support the
last three years through the successes and failures towards AI feedback
prediction. Also, thanks to Alan Cheng, Milan Mosse, Ali Malik, Yunsung
Kim, Juliette Woodrow, Vrinda Vasavada, Jinpeng Song, and John Mitchell
for great collaborations. Thank you to Mehran Sahami, Julie Zelenki,
Brahm Capoor and the Code in Place team who supported this project.
Thank you to the section leaders who provided all the human feedback
that the AI was able to learn from. Thank you to the Stanford Institute
for Human-Centered Artificial Intelligence (in particular the Hoffman-Yee Research Grant) and the Stanford
Interdisciplinary Graduate Fellowship for their support.
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