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h2. [Shute - Stealth Assessment in Virtual Worlds|^Shute - Stealth Assessment in Virtual Worlds.doc]
h3. Valerie J. Shute and J. Michael Spector, Florida State University
h2. Summary
We envision SCORM 2.0 as being enhanced by a stealth assessment engine that can be run within games, simulations, and other types of virtual worlds. This engine will collect ongoing and multi-faceted information about the learner while not disrupting attention or flow, and make reasoned inferences about competencies, which form the basis for diagnosis and adaptation. This innovative approach for embedding assessments in immersive virtual worlds (Shute et al., in press) draws on recent advances in assessment design, cognitive science, instructional design, and artificial intelligence (Milrad, Spector & Davidsen, 2003; Shute, Graf, & Hansen, 2005; Spector &
Koszalka, 2004). Key elements of the approach include: (a) evidence-centered assessment design, which systematically analyzes the assessment argument, including the claims to be made about the learner and the evidence that supports those claims (Mislevy, Steinberg, & Almond, 2003); (b) formative assessment and feedback to support learning (Black & Wiliam, 1998a; 1998b; Shute, 2008); and (c) instructional prescriptions to deliver tailored content via an adaptive algorithm coupled with the SCORM 2.0 assessments (Shute & Towle, 2003; Shute & Zapata-Rivera, 2008a). Information will be maintained within a student model which provides the basis for deciding when
and how to provide personalized content to an individual, and may include cognitive as well as noncognitive information.
h2. Requirements/Needs Outlined
h2. Recommendations
h2. [Shute - Stealth Assessment in Virtual Worlds|^Shute - Stealth Assessment in Virtual Worlds.doc]
h3. Valerie J. Shute and J. Michael Spector, Florida State University
h2. Summary
We envision SCORM 2.0 as being enhanced by a stealth assessment engine that can be run within games, simulations, and other types of virtual worlds. This engine will collect ongoing and multi-faceted information about the learner while not disrupting attention or flow, and make reasoned inferences about competencies, which form the basis for diagnosis and adaptation. This innovative approach for embedding assessments in immersive virtual worlds (Shute et al., in press) draws on recent advances in assessment design, cognitive science, instructional design, and artificial intelligence (Milrad, Spector & Davidsen, 2003; Shute, Graf, & Hansen, 2005; Spector &
Koszalka, 2004). Key elements of the approach include: (a) evidence-centered assessment design, which systematically analyzes the assessment argument, including the claims to be made about the learner and the evidence that supports those claims (Mislevy, Steinberg, & Almond, 2003); (b) formative assessment and feedback to support learning (Black & Wiliam, 1998a; 1998b; Shute, 2008); and (c) instructional prescriptions to deliver tailored content via an adaptive algorithm coupled with the SCORM 2.0 assessments (Shute & Towle, 2003; Shute & Zapata-Rivera, 2008a). Information will be maintained within a student model which provides the basis for deciding when
and how to provide personalized content to an individual, and may include cognitive as well as noncognitive information.
h2. Requirements/Needs Outlined
h2. Recommendations