diff --git a/Koli_2017/Koli_2017_Stanger.tex b/Koli_2017/Koli_2017_Stanger.tex
index 300d8fc..e8fb5b0 100644
--- a/Koli_2017/Koli_2017_Stanger.tex
+++ b/Koli_2017/Koli_2017_Stanger.tex
@@ -462,8 +462,7 @@
 
 Third, the switch to second semester in 2012--2013 could have negatively impacted students' performance by increasing the length of time between their exposure to basic data management concepts in first year, and their entry into the second year database course. In effect, they had longer to forget relevant material they learned in first year. If so, we could reasonably expect the grades in second semester offerings of the course to be lower. However, mean grades for second semester offerings (76.9\%) were in fact significantly \emph{higher} (\(p \approx 0.015\)) than those for first semester offerings (72.9\%). This should not be surprising, given that 2013 (second semester) had the highest grades overall. This effectively rules out semester changes as a factor.
 
-Fourth, perhaps the years with higher grades used less complex---and therefore easier---scenarios. To test this, we computed the following database complexity metrics for each of the four scenarios used: database complexity index (DCI) \cite{Sinha.B-2014a-Estimation}; referential degree (RD), depth of referential tree (DRT), and number of attributes (NA) \cite{Calero.C-2001a-Database,Piattini.M-2001a-Table}; database complexity (DC) \cite{Pavlic.M-2008a-Database}; and ``Software Metric Analyzer for Relational Database Systems'' (SMARtS)
-\cite{Jamil.B-2010a-SMARtS}. The results are shown in \cref{tab-metrics}. All but the DRT metric clearly showed the ``BDL'', ``used cars'', and ``student records'' scenarios to be of comparable complexity, while the ``postgrad'' scenario was noticeably less complex. It therefore seems unlikely that scenario complexity is a factor in student performance. It is also interesting to note that the ``used cars'' scenario was used in both 2014 and 2015, and yet the 2015 grades were significantly \emph{lower} than those for 2014. The only clear difference here is that our system was not used in 2015.
+Fourth, perhaps the years with higher grades used less complex---and therefore easier---scenarios. To test this, we computed the following database complexity metrics for each of the four scenarios used: database complexity index (DCI) \cite{Sinha.B-2014a-Estimation}; referential degree (RD), depth of referential tree (DRT), and number of attributes (NA) \cite{Calero.C-2001a-Database,Piattini.M-2001a-Table}; database complexity (DC) \cite{Pavlic.M-2008a-Database}; and ``Software Metric Analyzer for Relational Database Systems'' (SMARtS) \cite{Jamil.B-2010a-SMARtS}. The results are shown in \cref{tab-metrics}. All but the DRT metric clearly showed the ``BDL'', ``used cars'', and ``student records'' scenarios to be of comparable complexity, while the ``postgrad'' scenario was noticeably less complex. It therefore seems unlikely that scenario complexity is a factor in student performance. It is also interesting to note that the ``used cars'' scenario was used in both 2014 and 2015, and yet the 2015 grades were significantly \emph{lower} than those for 2014. The only clear difference here is that our system was not used in 2015.
 
 
 \begin{table}