Modern adventure games combine storytelling with gameplay using conventions familiar to film and television audiences. These games are different from others in that the experience depends on narrative content, and so is challenging to evaluate for improving content. The subgenre pioneered by Telltale Games uses dramatic choices to shape player experience. I developed and applied an annotation schema to a dataset of 6 player traversals of the first episode of The Wolf Among Us by Telltale Games. The schema supports comparison of player responses consisting of a combination of psychophysiological measurements and self-report events. These events are segmented and classified based on concepts drawn from Robert McKee, including the beat and story values, as well as traditional narrative elements such as character and plot. The results provide evidence that content annotations can facilitate analysis of players responses in longer traversals in spite of different levels of expressivity.

Dramatic choices are the core of the content in contemporary adventure games like those produced by Telltale Games and Dontnod Entertainment, developers of the Life is Strange series. These consist of emotionally-charged and dramatically contextualized player choices rather than strategic or skill-based challenges. Current approaches to understanding and assessing player experience in these games include surveys, interviews, think-aloud and various biometric measurements. These measures, taken alone, are useful for understanding traditional gameplay challenges such as platformers or puzzles but fall short of assessing how emotional content influences player experience or leads to variations in player responses. I collaboratively conducted a study of six players playing the first episode of The Wolf Among Us (TWAU) by Telltale Games, recording gameplay and facial expressions along with heart rate and skin conductivity. To compare and analyze player responses and classify content features, I developed an encoding, the Interactive Cinematic Experience (ICE) schemata, and used it to annotate player traversals with feature locations and player choices. These align player response data to content for comparison and analysis. Researchers can use the model to compare multiple paths through the game that can span hours of gameplay. In order to understand the dataset better, I developed a web-based visual data mining tool, Sherlock, for annotating and analyzing player experience datasets using the schemata and assessed it with a group of game researchers. I compare the encoding method to two other encodings using content from TWAU: Elson's Story Intention Graphs and Mawhorter's choice poetics. Analyzing facial expressions using ICE for the first three chapters revealed a spectrum of variations in responses to content, supporting the presence and effectiveness of emotional content anchors and the characteristics of what I call the Telltale effect. Thematic content that depended on choices differed in the values portrayed from content that was always included. The approach of using multiple player responses, especially facial expression analytics, is essential to a "medium read" of experiences and is a promising direction for interactive narrative research using existing commercial storygame content.