Learning Parallel Programming Concepts Through an Adaptive Game
Modern computing is increasingly handled in a parallel fashion and despite the growing body of work on how to teach parallel programming, little is understood about the learning of this subject. This project will shed light on the challenge of learning parallel programming and gather initial data on ways to scaffold it in college-level courses. We propose to develop a genre of adaptive learning games in which we will gather data on how experts and novices address parallel programming problems and study ways to scaffold learning.
Sustaining Motivation for Physical Activity through Social Motion-Based Game for Health
Social interaction is a known factor to sustain individuals’ motivation in digital games. However, we still know very little about how to integrate social interaction and multi-player game design in order to sustain players’ motivation for physical activities for a long period of time. This Project aims to bring together artificial intelligence, game design, and behavior science. Our current prototype, called StepQuest, uses both competition and cooperation mechanics to increase social interaction between players.
Mirrors of Grimaldi
Mirrors of Grimaldi is an experimental local multiplayer splitscreen game where your health is represented by the size of your screen. Players will be pitted against each other as Grimaldi’s Interdimensional Demonic Carnival invades the same medieval town in parallel timelines. As swarms of demonic minions attack the players, their screens will shrink and eventually crush them, knocking them out of the game. The last surviving player is declared the winner and is allowed to fight another day.
TAEMILE: Towards Automating Experience Management in Interactive Learning Environment
A key challenge for interactive learning environments is how to automatically co-regulate, balancing learners’ autonomy and the pedagogical processes intended by educators. In order to achieve personalized and dynamic co-regulation, we explore the use of artificial intelligence (AI) techniques of experience management (EM) in combination with a play-based pedagogical model. As a first step, this NSF-funded exploratory project seeks to collect preliminary data about 1) the relationship between a learner’s achievement goal orientations (learning orientation) and play style, and 2) the impact of dynamically adjusting the learning environment using EM on learner’s autonomy and learning outcomes.
Variable Space is a collaborative project that seeks to better define our individual perceptions of space. With a shared understanding that space consists of both our physical surroundings and also intangibles, such as time and emotion, our intent is to develop an investigative process that reveals both the commonality and anomaly in interpretation.
The project employs language as its catalyst and movement as its medium. Trained dancers will react to prompts, which will vary in style from lyrical to instructional to narrative. Through motion capture and digital modeling of the dancers’ movements, defining variables will be studied in greater detail. Because this investigation is both scientific and aesthetic, data and findings will be analyzed and presented through a range of methods and visualizations. Variable Space is a collaboration between Drexel faculty: Valerie Fox, PhD (English and Philosophy), Jacklynn Niemiec (Architecture and Interiors), Leah Stein (Dance), and Jichen Zhu, PhD (Digital Media). The collaboration is funded through the ExCITe Center Seed Grant (Class of 2014).