Chris Piech

Stanford University | SU · Department of Computer Science

Objective To develop and evaluate an automated, portable algorithm to differentiate active corneal ulcers from healed scars using only external photographs. Design A convolutional neural network was trained and tested using photographs of corneal ulcers and scars. Subjects De-identified photographs of corneal ulcers were obtained from the Steroid...

For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in need of this technology are often the most underserved by it. In many countries, illiterate people tend to speak only low-resource languages, for which the datasets necessary f...

The use of the Internet for learning provides a unique and growing opportunity to revisit the task of quantifying what people learn about a given subject in different regions around the world. Google alone receives over 5 billion searches a day, and its publicly available data provides insight into the learning process that is otherwise unobservabl...

In the ideal CS1 classroom, we should understand programming process---how student code evolves over time. However, for graphics-based programming assignments, the task of understanding and grading final solutions, let alone thousands of intermediate steps, is incredibly labor-intensive. In this work, we present a challenge, a dataset, and a promis...

In large undergraduate computer science classrooms, student learning on assignments is often gauged only by the work on their final solution, not by their programming process. As a consequence, teachers are unable to give detailed feedback on how students implement programming methodology, and novice students often lack a metacognitive understandin...

Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and Florida, which past research ha...

This paper presents BlueBook, a lightweight, cross-platform, computer-based, open source examination environment that overcomes traditional hurdles with computerized testing for computer science courses. As opposed to paper exam testing, BlueBook allows students to type coding problems on their laptops in an environment similar to their normal prog...

As computer science classes grow, instructor workload also increases: teachers must simultaneously teach material, provide assignment feedback, and monitor student progress. At scale, it is hard to know which students need extra help, and as a result some students can resort to excessive collaboration--using online resources or peer code--to comple...

Modeling a student's knowledge state while she is solving exercises is a crucial stepping stone towards providing better personalized learning experiences at scale. This task, also referred to as "knowledge tracing", has been explored extensively on exercises where student submissions fall into a finite discrete solution space, e.g. a multiple-choi...

In recent years, enrollments in undergraduate computer science programs have seen tremendous growth nationally. Often accompanying such growth is a concern from faculty that the additional students choosing to pursue computing may not have the same aptitude for the subject as was seen in prior student populations. Thus such students may exhibit wea...

Knowledge tracing---where a machine models the knowledge of a student as they interact with coursework---is a well established problem in computer supported education. Though effectively modeling student knowledge would have high educational impact, the task has many inherent challenges. In this paper we explore the utility of using Recurrent Neura...

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...

Exploring the whole sequence of steps a student takes to produce work, and the patterns that emerge from thousands of such sequences is fertile ground for a richer understanding of learning. In this paper we autonomously generate hints for the Code.org 'Hour of Code,' (which is to the best of our knowledge the largest online course to date) using h...

New high-frequency, automated data collection and analysis algorithms could offer new insights into complex learning processes, especially for tasks in which students have opportunities to generate unique open-ended artifacts such as computer programs. These approaches should be particularly useful because the need for scalable project-based and st...

The article addresses the question of how the assessment process with large–scale data derived from online learning environments will be different from the assessment process without it. Following an explanation of big data and how it is different from previously available learner data, we describe three notable features that characterize assessmen...

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 classr...

In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for est...

As MOOCs grow in popularity, the relatively low completion rates of learners has been a central criticism. This focus on completion rates, however, reflects a monolithic view of disengagement that does not allow MOOC designers to target interventions or develop adaptive course features for particular subpopulations of learners. To address this, we...

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. Whil...

Despite the potential wealth of educational indicators expressed in a student's approach to homework assignments, how students arrive at their final solution is largely overlooked in university courses. In this paper we present a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introd...

Despite the extraordinary growth in the volume and variety of material available on the World Wide Web, the Internet revolution has had surprisingly little effect on the delivery of informatics education. In this paper, we present an entirely web-based programming environment called StanfordKarel that introduces students to the fundamental techniqu...