diff --git a/Koli_2017/Koli_2017_Stanger.tex b/Koli_2017/Koli_2017_Stanger.tex
index e8fb5b0..b2b029c 100644
--- a/Koli_2017/Koli_2017_Stanger.tex
+++ b/Koli_2017/Koli_2017_Stanger.tex
@@ -132,7 +132,7 @@
 
 Previous systems focused almost exclusively on SQL \emph{queries} using the \texttt{SELECT} statement (i.e., DML) rather than SQL \emph{schema definitions} (DDL). This focus is unsurprising given that \texttt{SELECT} is probably the most frequently used of any SQL statement, and also because it is relatively complex compared to most other SQL statements. Only a few of the systems we reviewed even mentioned schema definition. RDBI \cite{Dietrich.S-1993a-An-educational} supported a DDL, but only its own non-SQL language, while esql \cite{Kearns.R-1997a-A-teaching} simply passed anything that was not a \texttt{SELECT} statement through to the DBMS. \citeauthor{Gong.A-2015a-CS-121-Automation}'s ``CS 121 Automation Tool'' \cite{Gong.A-2015a-CS-121-Automation} focuses primarily on SQL DML statements, but appears to be extensible and could potentially be modified to support SQL DDL statements.
 
-Many previous systems have implemented some form of automated or semi-automated grading, e.g., SQLator \cite{Sadiq.S-2004a-SQLator}, AssesSQL \cite{Prior.J-2004a-Backwash}, ActiveSQL \cite{Russell.G-2004a-Improving,Russell.G-2005a-Online}, SQLify \cite{Dekeyser.S-2007a-Computer}, aSQLg \cite{Kleiner.C-2013a-Automated}, \citeauthor{Gong.A-2015a-CS-121-Automation}'s ``CS 121 Automation Tool'' \cite{Gong.A-2015a-CS-121-Automation}, and XDa-TA\footnote{\url{http://www.cse.iitb.ac.in/infolab/xdata/}} \cite{Bhangdiya.A-2015a-XDa-TA,Chandra.B-2015a-Data,Chandra.B-2016a-Partial}. Some (e.g., SQLator, AssesSQL) provided only correct/incorrect responses, while others (e.g., ActiveSQL, SQLify, aSQLg, XDa-TA) had more sophisticated schemes for assigning partial credit, such as ActiveSQL's accuracy score, SQLify's eight-level scale, and XDa-TA's ability to generate data sets designed to catch common errors. In either case, these systems were often able to automatically mark a significant fraction (e.g., about a third for SQLator \cite{Sadiq.S-2004a-SQLator}) of submitted queries as correct without human intervention, thus reducing marking load for teachers.
+Many previous systems have implemented some form of automated or semi-automated grading, e.g., SQLator \cite{Sadiq.S-2004a-SQLator}, AsseSQL \cite{Prior.J-2004a-Backwash}, ActiveSQL \cite{Russell.G-2004a-Improving,Russell.G-2005a-Online}, SQLify \cite{Dekeyser.S-2007a-Computer}, aSQLg \cite{Kleiner.C-2013a-Automated}, \citeauthor{Gong.A-2015a-CS-121-Automation}'s ``CS 121 Automation Tool'' \cite{Gong.A-2015a-CS-121-Automation}, and XDa-TA\footnote{\url{http://www.cse.iitb.ac.in/infolab/xdata/}} \cite{Bhangdiya.A-2015a-XDa-TA,Chandra.B-2015a-Data,Chandra.B-2016a-Partial}. Some (e.g., SQLator, AsseSQL) provided only correct/incorrect responses, while others (e.g, ActiveSQL, SQLify, aSQLg, XDa-TA) had more sophisticated schemes for assigning partial credit, such as ActiveSQL's accuracy score, SQLify's eight-level scale, and XDa-TA's ability to generate data sets designed to catch common errors. In either case, these systems were often able to automatically mark a significant fraction (e.g., about a third for SQLator \cite{Sadiq.S-2004a-SQLator}) of submitted queries as correct without human intervention, thus reducing marking load for teachers.
 
 To our knowledge there has been no work on automated grading of SQL DDL statements. While dealing with \texttt{CREATE} statements should be simpler than dealing with \texttt{SELECT} statements, the ability to at least semi-automate the grading of SQL schema definitions should reap rewards in terms of more consistent application of grading criteria, and faster turnaround time \cite{Douce.C-2005a-Automatic,Russell.G-2004a-Improving,Dekeyser.S-2007a-Computer,Prior.J-2004a-Backwash}.