<html><head><meta name="color-scheme" content="light dark"></head><body><pre style="word-wrap: break-word; white-space: pre-wrap;">\documentclass{beamer}

\usepackage{ulem}
\usepackage[utf8]{inputenc}
\usetheme{metropolis}

\title[CE810 GD2]{CE810 - Game Design 2}
\subtitle{Evaluating Performance}
\date{Monday (AM), 14 May 2018}

\author{Joseph Walton-Rivers \&amp; Piers Williams}
\institute{Univeristy of Essex}

\newcommand{\keyterm}[1] {\textbf{\alert{#1}}}

%\usepackage{beamer}

\begin{document}
	
	\begin{frame}
	\titlepage
	\end{frame}

	% What do we want?
	% What do we measure?
	% How do we measure it?
	
	\begin{frame}{What is Player Experience?}
		Player experience
	\end{frame}
	
	\section{Metrics}
	
	\begin{frame}
		Collect data on how players/bots work
		
		\begin{block}{Activity}
			What kinds of features can we collect?
		\end{block}
	\end{frame}

	\begin{frame}{Data from humans}
		high-level human experience
		Biosignals
		Surveys and interviews
	\end{frame}

	\begin{frame}{Data from bots}
		Internal State
		How often does a bot face a difficult choice
	\end{frame}

	\begin{frame}{Data from either}
		Final Score distribution, Game duration, Score 'drama', Statical distribution of states, Degree of challenge
	\end{frame}

	\begin{frame}{Data from populations}
	Variability of scores, skill-depth
	\end{frame}


	\section{Action Sequences}

	\begin{frame}{Data from either}
		Actions taken, Record the sequence of button-pushes
	\end{frame}

	\begin{frame}{Entropy}
	
	\end{frame}

	%% METRICS
	% Simon's raw vs computed metrics.
	
	%% SKILL
	% Evaluating skill depth
	
\end{document}</pre></body></html>