Technology

In this paper we use an existing history learning game with an active audience as a research platform for exploring how humor and "snarkiness" in the dialog script affect students' progression and attitudes about the game. We conducted a 2x2 randomized experiment with 11,804 anonymous 3rd-6th grade students. Using one-way ANOVA and Kruskall-Wallis tests, we find that changes to the script produced measurable results in the self-reported perceived humor of the game and the likeability of the player character.

The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio.

The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio.

The ability for an educational game designer to understand their audience's play styles and resulting experience is an essential tool for improving their game's design. As a game is subjected to large-scale player testing, the designers require inexpensive, automated methods for categorizing patterns of player-game interactions. In this paper we present a simple, reusable process using best practices for data clustering, feasible for use within a small educational game studio.

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

This is the project-created training course for new users to learn about the STEM-OP and how to use it.

The real-time detection of when a player is struggling presents an opportunity for game designers to design timely and meaningful interventions, as well as to provide targeted support that improves student learning and engagement. In this paper, we present a struggle detector in the context of students playing the learning game, Wake: Tales from the Aqualab.

The real-time detection of when a player is struggling presents an opportunity for game designers to design timely and meaningful interventions, as well as to provide targeted support that improves student learning and engagement. In this paper, we present a struggle detector in the context of students playing the learning game, Wake: Tales from the Aqualab.