Career Academy Saga (Social Learning Platform)
To address a critical shortage of skilled workers, the ATGE has developed an intensive, exclusive career academy. Students selected to attend the academy prove their determination and problem-solving skills as they take on problems where they assist non-player characters (NPCs) in challenges that require creating, constructing, evaluating, assessing, producing, or trouble-shooting using reading skills applied to real-world career situations.
The Player sets up their game character (persona), indicating what type of player their character is and what career aspirations their character has in the game. Through the levels of the game, the Player will choose career challenges that earn experience points in career attributes that they have determined are most important for their Player’s career aspirations.
Enter the arena, select a career path, engage in solving a problem.
Each problem applies reading skills, using discussion to assist NPCs in creating, fixing, solving. Go deep – each problem unfolds in a scaffold. The player works to reach optimal solution (experiential, trial-and-error). Learners experience why reading skills are important in various career paths and degree programs.
Problem in context that allows iteration, improvement cycle. In these problems, the player experiences using different models (informed design, decision-making, trouble-shooting, continuous improvement) – earning new attributes.
In ArenaNEO, the learner is applying skills learned in the course to solve problems situated in a real-world context.
At run-time, the ArenaNEO game system loads from the server an array of text passages (learning objects) that have questions and answers pertaining to active reading (parameters) that are indexed for that object.
In each level of gameplay, depending upon the story selected (career context), random passages are activated – triggering gameplay events that task the learner.
Using a web console for the server, the content team creates and/or revises the passages and accompanying parameters using a simple web form. This information is saved to the game database which is what the learner will encounter the next time they launch the game.
The learning object and its parameters have a difficulty index assigned when first created. During gameplay, the game adapts the challenge level of the learning activities to player’s demonstrated skill level, using the learning object difficulty indexes when selecting objects for the level at run-time. The game system presents a ‘heat map’ visualization of learning challenges encountered during the game session.
The game system incorporates a Virtual Agent using Natural Language Processing to allow the player to ‘ask the virtual agent for assistance.
· Passages with Analysis Questions and Answers
· Narration (Virtual Agent)
The game is intended for English 062 students that are developing reading skills for career path entry.
Through gameplay, these At-risk learners experience success within the curricula context and practice communicating effectively. As a result, persistence and progress toward program entry increases.
The target platform is Oculus Touch.