Phillip E. McClean Bernhardt Saini-Eidukat Donald P. Schwert Brian M. Slator Alan R. White North Dakota State University, Fargo Virtual Worlds in Large Enrollment Science Classes Significantly Improve Authentic Learning

NDSU WWWIC World Wide Web Instructional Committee WWWIC’s virtual worlds research supported by NSF grants DUE , EAR , DUE , ITR and EPSCoR WWWIC faculty supported by large teams of undergraduate and graduate students. Paul JuellDonald Schwert Phillip McCleanBrian Slator Bernhardt Saini-EidukatAlan White Jeff Clark

l MultiUser l Exploration l Spatially-oriented virtual worlds l Practical planning and decision making Educational Role-playing Games “Learning-by-doing” Experiences

l Problem solving l Scientific method l Real-world content l Mature thinking

Virtual Worlds Can: l Collapse virtual time and distance l Allow physical or practical impossibilities l Allow participation from anywhere l Let you interact with other users, virtual artifacts, and software agents l Afford multi-user collaborations and competitive play

Balancing Pedagogy with Play Games have the capacity to engage! Powerful mechanisms for instruction Illustrate real-world content and structure Promote strategic maturity (“learning not the law, but learning to think like a lawyer”)

Technical Approach Networked, internet based, client-server simulation UNIX-based MOO (Multi-User Dungeon, Object Oriented) Java-based clients (text version - telnet based; graphical versions)

The Projects l The Virtual Cell l The Geology Explorer l Dollar Bay l Like-a-Fish hook Village l Digital Archive for Archaeology l Others

The Virtual Cell Rendered in VRML (Virtual Reality Modeling Language)

Users can “fly around” inside the cell

Users are assigned specific goals For example: Identify 5 different organelles

User movements and actions are tracked by MOO software

The Virtual Cell User Interface

Users set up experiments in the Cell to accomplish their assigned goals

Or... take samples from the Cell back to the Laboratory to use instruments, inhibitors, or perform mutations

Outcomes: Cell Biology ContentLearning-by-Doing Problem SolvingHypothesis Formation Deductive ReasoningMature Thinking

Similar to Earth, but opposite the Sun Students “land” on Oit to undertake exploration Authentic Geoscience goals - e.g., to locate, identify, and report valuable minerals Planet Oit

~50 places: desert, cutbank, cave, etc. ~100 different rocks and minerals ~15 field instruments: rock pick, acid bottle, magnet, etc. ~Software Tutors: agents for equipment, exploration, and deduction Planet Oit The simulation

l Students can join from any remote location l They can log in at any time of day or night l Human tutors cannot be available at all times to help l Students can become discouraged or “lost” in the world and not know why In Virtual Environments: Tutors are Needed

l Information is readily available l The simulation can track actions l The simulation can generate warnings and explanations l Tutor “visits” are triggered by user action In Virtual Environments: Tutors are Needed

l Student interact with the intelligent tutoring agent l Students can ignore advice and carry on at their own risk In Virtual Environments: Tutors are Needed

Intelligent Software Tutoring Agents. (example: Deductive Tutors) 1. Equipment tutor 2. Exploration tutor 3. Science tutor Detects when a student makes a wrong guess and why (i.e. what evidence they are lacking); or when a student makes a correct guess with insufficient evidence (i.e. a lucky guess) Tutoring is Done by:

Rejects the notion of standardized multiple choice tests Pre-game narrative-based survey short problem-solving stories students record their impressions and questions Similar post-game survey with different but analogous scenarios Surveys analyzed for improvement in problem-solving Assessment Subjective

The Geology Explorer: Assessment Protocol Pre-course Assessment: 400+ students Computer Literacy Assessment: (244 volunteers) Divide by Computer Literacy and Geology Lab Experience Geomagnetic (Alternative) Group: (122 students) Geology Explorer Geology Explorer Treatment Group: (122 students) Non-Participant Control Non-Participant ControlGroup: (150 students, approx.) Completed Completed (78 students) Non- completed Non- completed (44 students) Completed Completed (95 students) Non- completed Non- completed (27 students) Post-course Assessment: 368 students Example: Fall, 1998

Mean Post-Intervention Scenario Scores for 1998 Geology Explorer - NDSU Physical Geology Students GraderGraderGrader GroupNo.OneTwoThree Alternate a27.0a42.6a Control a25.5a44.5a Planet Oit b35.4b53.4b Within any column, any two means followed by the same letter are not significantly different at P=0.05 using Duncan’s multiple range mean separation test.

for the Virtual Cell: 1999 NDSU General Biology Mean Post-Intervention Scenario Scores for the Virtual Cell: 1999 NDSU General Biology Module: GroupNo.Organelle IDCellular Resp. Alternate b13.7b Control a10.6a Vcell c17.3c Within any column, any two means followed by the same letter are not significantly different at P=0.05 using the LSD mean separation test.

To visit the Environments: To view VRML files, you will need a browser plug-in: CosmoPlayer