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Deep Knowledge Tracing
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 studentExpand
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Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
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 thatExpand
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Tuned Models of Peer Assessment in MOOCs
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.Expand
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Autonomously Generating Hints by Inferring Problem Solving Policies
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 thisExpand
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Learning Program Embeddings to Propagate Feedback on Student Code
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 ofExpand
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Codewebs: scalable homework search for massive open online programming courses
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 higherExpand
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Modeling how students learn to program
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 universityExpand
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Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC
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 ofExpand
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Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming
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 generateExpand
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Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
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'sExpand
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