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Deep Knowledge Tracing
TLDR
The utility of using Recurrent Neural Networks to model student learning and the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks are explored. Expand
Deconstructing disengagement: analyzing learner subpopulations in massive open online courses
TLDR
A simple, scalable, and informative classification method is presented that identifies a small number of longitudinal engagement trajectories in MOOCs and compares learners in each trajectory and course across demographics, forum participation, video access, and reports of overall experience. Expand
Tuned Models of Peer Assessment in MOOCs
TLDR
Algorithms for estimating and correcting for grader biases and reliabilities are developed, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera's HCI course offerings --- the largest peer grading networks analysed to date. Expand
Autonomously Generating Hints by Inferring Problem Solving Policies
TLDR
This paper autonomously generate hints for the Code.org `Hour of Code,' (which is to the best of the authors' knowledge the largest online course to date) using historical student data, and discovers that this statistic is highly predictive of a student's future success. Expand
Learning Program Embeddings to Propagate Feedback on Student Code
TLDR
A neural network method is introduced to encode programs as a linear mapping from an embedded precondition space to an embedded postcondition space and an algorithm for feedback at scale is proposed using these linear maps as features. Expand
Codewebs: scalable homework search for massive open online programming courses
TLDR
A method for decomposing online homework submissions into a vocabulary of "code phrases", and based on this vocabulary, a queryable index that allows for fast searches into the massive dataset of student homework submissions is designed. Expand
Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming
TLDR
This article presents studies focused on how students learn computer programming, based on data drawn from 154,000 code snapshots of computer programs under development by approximately 370 students enrolled in an introductory undergraduate programming course, using methods from machine learning to discover patterns in the data and to predict final exam grades. Expand
Modeling how students learn to program
TLDR
This paper presents a methodology which uses machine learning techniques to autonomously create a graphical model of how students in an introductory programming course progress through a homework assignment, and shows that this model is predictive of which students will struggle with material presented later in the class. Expand
Syntactic and Functional Variability of a Million Code Submissions in a Machine Learning MOOC
TLDR
The syntax and functional similarity of the submissions are mapped out in order to explore the variation in solutions in the first offering of Stanford's Machine Learning Massive Open-Access Online Course. Expand
Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
TLDR
An adversarially-trained neural network is presented that predicts recidivism and is trained to remove racial bias and gains predictive accuracy and gets closer to achieving two out of three measures of fairness: parity and equality of odds. Expand
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