Scaffolding of Prediction (And Control Conditions) Will Be Integrated Into The Game-Play Navigation Interfaces

We propose that navigation interface provides an excellent opportunity for prediction. Essentially, while real-time and just-in-time navigation formats are common in games like SURGE: EPIGAME, formats supporting prediction are also not uncommon and could be developed without breaking the game aspects of SURGE: EPIGAME. Furthermore, a game like SURGE: EPIGAME provides excellent opportunities for research on the integration of prediction into games because of the range of interface formats afforded along a prediction/real-time continuum. Essentially, real-time or just-in-time navigation formats engage the player in making decisions during the flow of the level often in a reflexive manner mirroring constraint-based thinking (e.g., the player continually micro-adjusts direction and velocity as it becomes apparent that adjustments are required.

These more predictive interfaces, we hypothesize, should support a higher level of model-based thinking than constraint based thinking, as well as a higher percentage of explicit articulation of thinking versus implicit intuitive thinking that might stay at the level of unaware application of p-prims. This should be true in terms of comparisons between the Predictive and Real-Time categories as well as comparisons within the Real-Time category and comparisons within the Predictive category. We further hypothesize that the GUI variants within a given sub-category will prove superior to the text-based variants in terms of engagement, accessibility, and learning. The specific schedule for conducting these pilot comparisons is outlined later under the research and development timeline.

Scaffolding of Explanation (And Control Conditions) Will Be Integrated Into Dialog With Computer Controlled Characters in The Game

We now outline the planned explanation variants for development and piloting. In addition to the theoretical literature discussed earlier, we are building on the early design paradigms represented in Mayer and Johnson (2010), who focused on adding an explanation task following a feedback event. While playing an electronics quiz-based environment that Mayer and Johnson defined as being game-like in the sense that a score was kept and some other core features of games were included, students were tasked with answering questions posed as circuit diagrams. The research contrast was whether or not students received immediate feedback on whether their answers were correct or not, and whether or not students were tasked with providing self-explanations (chosen from a list of possibly relevant principles) for their answers. Mayer and Johnson found that the self-explanation alone was more effective than feedback alone at improving responses on a transfer task, and as effective as self-explanation combined with feedback. Our interpretation of these results is that tasking students with some activity that connects the general scientific principles with the task the student just performed (whether they were successful or not), could be very conducive for learning in a true game context, particularly if the explanation was integrated within the fabric of the game and was more responsive to student ideas. We hypothesize that players in conditions that include explanation functionality toggled on will outperform players in null explanation conditions in keeping with the discussed literature on explanation and the findings of Mayer and Johnson. We hypothesize further, however, that player-explanation conditions that provide more opportunity for the player to articulate his or her thinking (i.e., the icon-based self-explanation and argumentation as explanation variants) will outperform the fixed text-based self-explanation conditions as well as generate greater engagement in the game and ideas. Furthermore, we hypothesize that the player-explanations will outperform the didactic explanations, but that combining player explanations with didactic explanations, particularly the reactive didactic explanations, will result in the highest learning gains for players.