\vspace{1mm}
\begin{columns}
\begin{column}{0.5\linewidth}
\begin{block}{Predicting student performance}
\vspace{2mm}
\parbox{6mm}{over\time} $\xdownarrow$ \parbox{4.2cm}{User 1 attempts Item 1 \correct
User 1 attempts Item 2 \mistake
User 1 attempts Item 2 \correct
User 2 attempts Item 1 ???
User 2 attempts Item 1 ???
User 2 attempts Item 2 ???}
\end{block}
\end{column}
\begin{column}{0.5\linewidth}
\begin{block}{Existing work}
\vspace{-7mm}
\(\underbrace{\textnormal{PFA}}_{\textnormal{LogReg}} \! \leq \underbrace{\textnormal{DKT}}_{\textnormal{LSTM}} \leq \! \underbrace{\textnormal{IRT}}_{\textnormal{LogReg}} \! \alert{\leq \underbrace{\textnormal{KTM}}_{\textnormal{FM}}}\)
Using different features
\end{block}
\end{column}
\end{columns}
\vspace{4mm}
\includegraphics[width=\linewidth]{figures/ktm-archi.pdf}
Each \textcolor{blue!80}{user}, \textcolor{orange}{item}, \textcolor{green!50!black}{skill} $k$ is modeled by bias $\alert{w_k}$ and embedding $\alert{\bm{v_k}}$.\vspace{2mm} \begin{columns} \begin{column}{0.47\linewidth} \includegraphics[width=\linewidth]{figures/fm.pdf} \end{column} \begin{column}{0.53\linewidth} \includegraphics[width=\linewidth]{figures/fm2.pdf} \end{column} \end{columns}\vspace{-2mm}
\hfill $\probit p(\bm{x}) = \mu + \underbrace{\sum_{k = 1}^N \alert{w_k} x_k}{\textnormal{logistic regression}} + \underbrace{\sum{1 \leq k < l \leq N} x_k x_l \langle \alert{\bm{v_k}}, \alert{\bm{v_l}} \rangle}_{\textnormal{pairwise relationships}}$
\begin{columns} \begin{column}{0.4\linewidth} \vspace{-5mm} \begin{block}{Results on Assistments} \scriptsize 347k samples: 4k users, 27k items \includegraphics[width=\linewidth]{figures/barchart.pdf} \end{block} \end{column} \begin{column}{0.6\linewidth} \scriptsize \input{tables/assistments42-full-simple} \end{column} \end{columns}