
C. Piech, M. Sahami, J. Huang, and L. Guibas, Autonomously Generating Hints by Inferring Problem Solving Policies, Learning at Scale 2 (2015), 195204.
Abstract:
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 historical
student data. We first develop a family of algorithms that can
predict the way an expert teacher would encourage a student
to make forward progress. Such predictions can form the basis
for effective hint generation systems. The algorithms are
more accurate than current stateoftheart methods at recreating
expert suggestions, are easy to implement and scale well.
We then show that the same framework which motivated the
hint generating algorithms suggests a sequencebased statistic
that can be measured for each learner. We discover that
this statistic is highly predictive of a student’s future success.
Bibtex:
@inproceedings{piech2015autonomously,
author = {Piech, Chris and Sahami, Mehran and Huang, Jonathan and Guibas, Leonidas},
title = {Autonomously Generating Hints by Inferring Problem Solving Policies},
booktitle = {Proceedings of the Second (2015) ACM Conference on Learning @ Scale},
series = {L@S '15},
year = {2015},
isbn = {9781450334112},
identifier = {pmhgaghipss15},
location = {Vancouver, BC, Canada},
pages = {195204},
numpages = {10},
url = {http://doi.acm.org/10.1145/2724660.2724668},
doi = {10.1145/2724660.2724668},
acmid = {2724668},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {educational datamining., hint generation, problem solving policy},
}

