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Individual characteristics:

physical - emotion - cognition - history

Global - national - regional

Family - friends - colleagues - professionals

AND MORE IMPORTANT

The knowledge community with special language, concepts & practices

Epistemological Sources:

Explanatory - Deductive - Conceptual Analogical - Perceptual

Qualitative-Complex Systems-Quantitative

...tests, observations, examining artifacts...

Evidence Centered Design

Learning Theory

Cognitivism

Knowledge

Digital Media Assessment Theory

Primary proposition:

Adjacency matrices are used to create network maps of brain states

Data is transformed to represent time slices of interest via adjacency matrices

Sensors capture 128 records per second during digital media performance and learning

Contructivism

Adaptive Digital Media

“Network-Based”

Online data gathering +

Distributed interoperating web services

The sensors provide simultaneous multi-channel data

"Assessment"

Measures and feedback for improvement of performance and evaluating learners, that are “multidimensional, integrated, and revealed in performance over time” (Walvoord & Anderson, 1998)

Next stops on the path are examples of "semantic data" generation

Personalization

Analytics

Prediction

Intervention

See George Siemans blog re: Learning Analytics

http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/comment-page-1/#comment-68911

GIS Mapping of Social Characteristics

How do student grades relate to where they live?

Equity Alliance & Leadscape @ ASU

http://www.niusileadscape.org/mp/City/N1709930?year=2005

Imagine teaching an online course and mapping the distribution of grades on the last assignment.

New Media Affordances

Learner

A design for any

digital media learning experience...with computational representations of assessment functions

http://www.equityallianceatasu.org/ea/equity-matters

Emerging capabilities in metadata generation offer the potential for identifying the problem-solving strategies of learners.

Complex performances can be supported and documented in network-based assessments via multimedia, multileveled, and multiply connected bases of knowledge.

Analysis of expert-novice differences can be facilitated across groups, across space and time, drawing from an evolving common knowledge store.

An example of metadata generation is coming next

Network-based assessments can include statistical analysis and displays of information to assist learners and teachers in making inferences about performance.

The interactive potential of network-based assessment opens up new possibilities for fostering and determining metacognitive skills of the learner.

With many instances of the learner interacting with applications in different times, places and contexts, network-based assessments can build a long-term record of documentation, showing how learners change over time.

An Example of Peer-to-Peer Assessment

Decisions & Dilemmas of Assessment

Community

Imagine these as performance levels in the skills to be acquired...

http://stackoverflow.com/badges

Assessment

Your unique

solution suite of

tools & practices

Behaviorism

NY TIMES (Mixing traditional and new methods, the journal posted online four essays not yet accepted for publication, and a core group of experts — what Ms. Rowe called “our crowd sourcing” — were invited to post their signed comments on the Web site MediaCommons, a scholarly digital network. Others could add their thoughts as well, after registering with their own names. In the end 41 people made more than 350 comments, many of which elicited responses from the authors. The revised essays were then reviewed by the quarterly’s editors, who made the final decision to include them in the printed journal, due out Sept. 17.

Structural modeling can now start with drawings and qualitative understandings...

...& be mathematically rigorous.

http://www.my-efolio.com/

New Media Affordances

Evidence Centered Design

Feed in some documents or web pages, then...

http://www.nytimes.com/2010/08/24/arts/24peer.html?

Author

Project & Challenge Based

Learning

Experts

An example of metadata generation

Illustrates the need to understand...

Intersectionality

Engineering Concepts

...where complexity analysis is

going to be helpful in finding coherent resolutions among the layered & overlapping frames of reference

Editors

Automated Tagging & Text Mining

Optimization

Systems

Crowd

Self-Directed,

Personalized Learning

I fed in an 8 page conference paper

and got several terms...

Structure-behavior-function

Emergent properties

Control/feedback

Processes

Boundaries

Subsystems

Interactions

Multiple variables

Trade-offs

Requirements

Resources

Physical Laws

Social constraints

Cultural norms

Side effects

Software from the Computational Epistemology Laboratory

Needs human shaping...called "supervised learning" of the artificial intelligence

http://www.alchemyapi.com/api/demo.html

http://cogsci.uwaterloo.ca/Index.html#software

http://semanticproxy.com/

Machines will learn to make their own topic maps by observing performances and artifacts - and will be able to compare their topic maps to those in order to make an assessment.

http://cogsci.uwaterloo.ca/JavaECHO/echoApplet.html

...which leads to topic maps and other forms of semantically organized information

Peer-to-Peer Pedagogy

http://www.wandora.org/wandora/wiki/index.php?title=Topic_Maps

Automated Essay Scoring

BETSY

Automated assessment

with topic maps will include pattern

recognition & judgment

assistance for humans

and computer agents

Bayesian Essay Test Scoring sYstem

http://echo.edres.org:8080/betsy/

Knowledge

In this example, the

computer recognizes similarities between

two visual examples (e.g. "same color" & "same cone-like shape")

See Paul Thagard's work

ePortfolios

Learner

Decisions & Dilemmas

Intersection of MIRROR, PUBLIC, MEDIA

Media

Ownership

Intersection of MAP, TRUSTED OTHER, ARTIFACT

Focus

Intersection of SONNET, PUBLIC, FOCUS

Community

Assessment

Assessment & Digital Media Learning

New possibilities for assessment stem from

  • evidence centered design theory,
  • traditional decisions and dilemmas of performance assessment, and
  • the affordances of immersive digital media learning environments.

Thank you!

david.c.gibson@asu.edu

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