Games for Learning : Understanding what makes an Effective Game for Learning Ken Perlin and Jan Plass, NYU

Research Objectives Focus on STEM (Science, Technology, Engineering, & Mathematics) fields “NSF programs planned for FY 2008 that will make investments in computing education …” –Jeanette Wing, Assistant Director for CISE Understanding effective uses of games in middle school curricula Guiding Principles

Problem Statement Specific populations –Children from Inner-cities –Children in rural communities –Children in impoverished nations regions Problems they face –Lack of access to computers –Lack of engaging materials relative to their experiences –Poorly funded educational environments –Rapidly fluctuating, intensely varied technology platforms Underserved populations

Platforms Mobile gaming platforms –Sony PSP –Nintendo DS Of particular interest: Making games for learning on mobile platforms

Platforms Cellular Phones –J2ME (Java 2 Mobile Environment) –Flash Making games for learning on mobile platforms

Overview Educational Game Design Principles Factual Knowledge Conceptual Knowledge Procedural Knowledge Meta-cognitive Knowledge Curricular Integration Educational Games Educational Games Game-based Learning Approach Affective Outcomes Affective Outcomes

Research Plan Identify Design factors (cognitive, emotional, socio-cultural) for effective educational games Study how the way games are integrated into authentic settings affects their educational effectiveness –What are effective integration practices? –What support do teachers require? Study impact of learner characteristics (gender, age, prior knowledge, cognitive ability, self-regulation skills) on educational effectiveness of games Research Questions

Research Plan Overview Game Design Principles Game Prototypes Features of Effective Educational Games Review Research on Games Observation of Game Play Development Team: Implement Education Team: Empirical Research

Research Plan Learner Characteristics –Survey of gaming experience –Tests of cognitive abilities –Tests of interest, motivation, attitudes –Embedded measures in game: Self-regulation, prior knowledge Learning Process –Log file data (pattern analysis) –Video data (rubrics); Think-aloud protocols (rubrics) –Fun, Engagement (multiple measures) –Embedded measures in game Learning Outcomes –Learning outcome tests on multiple levels –Embedded measures in game Measures

Research Plan Retention (Recall of factual knowledge) –Metacognitive test Comprehension (Understanding of key ideas) –Metacognitive test, Cued recall (open-ended) –Concept maps –Game behavior/performance Transfer (Ability to apply knowledge to new situations) –Problem solving test (open-ended) –Design tests (open-ended) –Game behavior/performance Metacognitive knowledge (Learning/exploration strategies) –Game behavior/performance (logs) Learning Outcome Measures

Research Plan Fun, Engagement –Self-reports –In-Game behavior/performance –Flow experience –Overall player behavior: SNS, game play usage patterns Interest, Motivation, Attitudes –Self-reports –Game behavior/performance Subject-specific self-efficacy –Self-reports Affect Outcome Measures

Results –Validated Design Principles for Educational Games –Series of Validated Educational Games –Data Analysis Toolkit for Educational Game Research Anticipated Outcomes WILL ALLOW US TO Positively influence the field of educational game design Positively influence educational game research models Positively influence the integration of educational games into curriculum Positively influence students’ achievement in STEM areas

Funding Sources NSF Opportunities, based on the mandate for computational thinking and education –E.g., Repunsel- Corporate Funding –Negotiation ongoing Educational Partners –Teaming with several other universities and researchers to optimize skills, while minimizing expenses