Providing feedback, both assessing final work and giving hints to stuck students, is difficult for open-ended assignments in massive online classes which can range from thousands to millions of students. We introduce a neural network method to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and propose an algorithm for feedback at scale using these linear maps as features. We apply our algorithm to assessments from the Code.org Hour of Code and Stanford University's CS1 course, where we propagate human comments on student assignments to orders of magnitude more submissions.
@cite_7: While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical questions that figure prominently in STEM (science, technology, engineering, and mathematics) courses. Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors. MLP takes inspiration from the success of natural language processing for text data and comprises three main steps. First, we convert each solution to an open response mathematical question into a series of numerical features. Second, we cluster the features from several solutions to uncover the structures of correct, partially correct, and incorrect solutions. We develop two different clustering approaches, one that leverages generic clustering algorithms and one based on Bayesian nonparametrics. Third, we automatically grade the remaining (potentially large number of) solutions based on their assigned cluster and one instructor-provided grade per cluster. As a bonus, we can track the cluster assignment of each step of a multistep solution and determine when it departs from a cluster of correct solutions, which enables us to indicate the likely locations of errors to learners. We test and validate MLP on real-world MOOC data to demonstrate how it can substantially reduce the human effort required in large-scale educational platforms.
@cite_1: Massive open online courses (MOOCs), one of the latest internet revolutions have engendered hope that constant iterative improvement and economies of scale may cure the cost disease" of higher education. While scalable in many ways, providing feedback for homework submissions (particularly open-ended ones) remains a challenge in the online classroom. In courses where the student-teacher ratio can be ten thousand to one or worse, it is impossible for instructors to personally give feedback to students or to understand the multitude of student approaches and pitfalls. Organizing and making sense of massive collections of homework solutions is thus a critical web problem. Despite the challenges, the dense solution space sampling in highly structured homeworks for some MOOCs suggests an elegant solution to providing quality feedback to students on a massive scale. We outline a method for decomposing online homework submissions into a vocabulary of "code phrases", and based on this vocabulary, we architect a queryable index that allows for fast searches into the massive dataset of student homework submissions. To demonstrate the utility of our homework search engine we index over a million code submissions from users worldwide in Stanford's Machine Learning MOOC and (a) semi-automatically learn shared structure amongst homework submissions and (b) generate specific feedback for student mistakes. Codewebs is a tool that leverages the redundancy of densely sampled, highly structured homeworks in order to force-multiply teacher effort. Giving articulate, instant feedback is a crucial component of the online learning process and thus by building a homework search engine we hope to take a step towards higher quality free education.
@cite_2: We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.
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@cite_11: In the first offering of Stanford’s Machine Learning Massive Open-Access Online Course (MOOC) there were over a million programming submissions to 42 assignments — a dense sampling of the range of possible solutions. In this paper we map out the syntax and functional similarity of the submissions in order to explore the variation in solutions. While there was a massive number of submissions, there is a much smaller set of unique approaches. This redundancy in student solutions can be leveraged to “force multiply” teacher feedback. Fig. 1. The landscape of solutions for “gradient descent for linear regression” representing over 40,000 student code submissions with edges drawn between syntactically similar submissions and colors corresponding to performance on a battery of unit tests (red submissions passed all unit tests).
The advent of massive online computer science courses has made the problem of automated reasoning with large code collections an important problem. There have been a number of recent papers @cite_11 @cite_2 @cite_1 @cite_5 @cite_7 @cite_12 on using large homework submission datasets to improve student feedback. The volume of work speaks to the importance of this problem. Despite the research efforts, however, providing quality feedback at scale remains an open problem.