Using Expert Interviews, Automated Search and Natural Language
Processing to Identify Content for Instructional Materials and
Assessment Development UCLA’s Center for Research on Evaluation,
Standards, and Testing (CRESST), working with its partner
organization, the Center for Advanced Technology in Schools
(CATS), has developed an innovative approach to identifying the
conceptual content for inclusion in instruction and assessment.
With funding from the Institute of Education Sciences, CATS is
developing learning games focused on acquiring pre-algebra
schema and skills. For this effort, CRESST designed approaches
to build in valid outcome development and then strategies to
compare means that lead to the most effective learning. To
develop the goals for the games and to make them congruent with
Common Core State Standards, the R&D team used the design of
ontology to translate the verbal standards statements into
network representations that define the universe of content and
cognition included in the standard