Learning by Doing in the Context of Distance Learning

1. Learning by Doing in the Context of Distance Learning Allen Munro University of Southern CaliforniaRossier School of EducationBehavioral Technology Laboratory in collaboration withUCLA-CRESST AERASan Diego, CAApril 13, 2004

2. Overview • Learning in simulation contexts requires assessment and instruction in those contexts. • Cost-effective development of simulations for training requires a consistent set of services. • iRides Author was used to develop a ‘simulation’ that provides a decision aiding application to advanced students. • Student use of this decision aiding tool can be automatically recorded for analysis.

3. How Does Learning Happenin Simulation Contexts? • Simulations ==> Learning ??? • Military simulations often require human teachers, frequently with one or two teachers per student. • Fully exploiting the computer • Not only simulation interactions • But also pedagogical interactions

4. The Problem of Distance Learning in Simulation Contexts • Simulation learning often requires human teachers/coaches; how to bring about learning without them? • Provide an artificial tutor to handle the most frequently needed interventions!

5. Modes of Trainingin Simulation Contexts • Demonstration • Often with explanation • Can be student-paced • Monitored Practice • Tight monitoring—e.g., single track • Loose monitoring—a variety of measures can be utilized to assess progress and generate advice

6. Assessmentin Simulation Contexts • Summative assessments—Like practice, but without support features • Micro-assessments • Used during monitored practice • Types • Assessing actions • Assessing effects • Monitoring for special states

7. TUTOR Universal Architecture for Teaching in Simulation Contexts SIMULATION (or REAL SYSTEM) SIMULATION ARTIFICIAL TUTOR USES SERVICES STUDENT STUDENT Human Tutoring Machine Tutoring

8. Universal Service Set for Simulations that Teach • Report Object Selection • Ignore Manipulations • Resume Manipulations • Draw Attention to • Set Value / Set Values • Perform Manipulation • Pause/Resume Manipulation • Require Manipulation • Require State

9. One Approach SIMULATION ENVIRONMENT UNIVERSAL SIMULATION ENGINE ARTIFICIAL TUTOR • Authoring application • Generates data that specify simulation behavior • Delivery system • Incorporates the set of services required • Interprets data that specify simulation behavior • Developers don’t have to figure out how to re-implement the services for every simulation SERVICES STUDENT Machine Tutoring

10. Normal_F = g * sin(20º) Parallel_F = g * cos(20º) Parallel_F Normal_F g Simulation Behavior and Instruction Authored Separately • Simulation author—Behavior accuracy

11. Relational Descriptions of Behavior • Simulation author—Natural specifications

12. Complex Decision Making:Learning in the Context of a Tool • Support systematic evaluation

13. Complex Decision Example: Dealing with a Refueling at Sea Subsystem Crisis • Vendor plans to leave the business. But your project requires RAS! • Evaluate alternatives • List possible approaches / decisions • Consider possible outcomes • Evaluate the utility of each outcome in terms of several attributes • Estimate the probability of each outcome • Tools support this process

14. Huge Project 18 years! • Need to consider proximate & remote outcomes

15. Using the Tool:Edit Node Labels • Name the alternative decisions, resulting events, and possible outcomes.

16. Define ‘Utility’ in Context • Decide on number and names of attributes • Weight the attributes

17. Assign Utilities using Attributes • Give a number to each attribute of utility: Cost, Performance, and Schedule • For every possible outcome, assign values

18. Assign Probabilities to Outcomes • When there are exactly two possible outcomes of an event, automatic adjustment occurs.

19. Tracking Student Actions • Utility attribute value changes • Probability changes • Threshold value changes • Changes to attribute weights • Creation/Deletion of nodes • …

20. Offering Instruction