1. U.S. Army Research, Development and Engineering Command Unclassified – Unlimited Distribution Considerations for adaptive tutoring within serious games: authoring cognitive models and game interfaces Robert Sottilare, Ph.D. U.S. Army Research Laboratory – Human Research & Engineering Directorate (ARL-HRED) robert.sottilare@us.army.mil Stephen Gilbert, Ph.D. Iowa State University – Human Computer Interaction, Psychology gilbert@iastate.edu

2. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution No standards exist for authoring game-based tutors interoperability standards for game-tutor interaction actionable instructional strategies for games No standards exist to assess game-based tutors levels of learning effectiveness levels of adaptability ease of interaction and use Introduction

3. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Motivation Grand Challenges for Educational Technology –Personalize Education –Assess Student Learning –Support Social Learning –Diminish Boundaries –Develop Alternative Teaching Methods –Enhance the Role of Stakeholders –Address Policy Changes How do we make it easier to link game and tutors for adaptive training? Woolf, B. P. (2010). A Roadmap for Education Technology. National Science Foundation # 0637190

4. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Construct for Personalized Adaptive Training in Games

5. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Game-Tutor Abstraction Layer

6. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Game-Tutor Abstraction Layer Detail

7. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Platinum Tutors > 2σ difference from traditional classroom Gold Tutors 2σ difference from traditional classroom Silver Tutors approximately 1σ-2σ difference Bronze Tutors < 1σ difference; many tutors today Assessing Game-based Tutors

8. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution adapt to the learner better than a human tutor enable learning better than a human tutor fully perceive learner behaviors and physiology through remote sensing support fully mobile training are consistently accurate (near 100%) in classifying the learner’s cognitive state in near real-time have an optimized repertoire of instructional strategies are automatically integrated with a variety of training platforms (e.g., serious games, commercial/military training simulations). Platinum Tutors

9. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution adapt to the learner equally as well as a human tutor enable learning equal to the best human tutors fully perceive learner behaviors through unobtrusive sensing methods support mobile training within instrumented spaces are consistently accurate (>80%) in classifying the learner’s cognitive state in near real-time have an optimized repertoire of instructional strategies are easily integrated by laypeople with a variety of training platforms (e.g., serious games, commercial/military training simulations). Gold Tutors

10. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution adapt to the learner nearly as well as a human tutor enable learning equal to good human tutors fully perceive learner behaviors through unobtrusive sensing methods; are not mobile (static classroom systems) are consistently accurate (>80%) in classifying the learner’s cognitive state use prescriptive instructional strategies are easily integrated by professionals with a variety of training platforms (e.g., serious games, commercial/military training simulations). Silver Tutors

11. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution adapt to learner only in prescribed ways based on an intrinsic expert model offer improvements on self-directed learning with no tutor perceive some learner behaviors primarily related to performance are static classroom-based systems can classify the learner’s cognitive state only based on performance and do not account for learner state use prescriptive instructional strategies may be integrated by professionals within a limited class of training platforms (e.g., serious games) Bronze Tutors

12. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Feedback (e.g., direction, support) should be presented when the trainee’s actual performance varies sufficiently from their expected performance. Predictors of expected trainee performance: competency in the domain being trained engagement measures motivational measures task complexity Conclusions: Learning and Feedback in EVS

13. SFC Paul Ray Smith Simulation & Training Technology Center Unclassified – Unlimited Distribution Questions?