Abstract: Systems and methods are provided for generating texts appropriate for a reading level of an individual. An existing exam unit is accessed, wherein the existing exam unit includes a reading passage and a plurality of questions related to the reading passage. The plurality of questions are filtered based on a criterion to form a subset of questions. A first difficulty score is determined based on the reading passage. A second difficulty score is determined based on the subset of questions. A correlation between the first difficulty score and the second difficulty score is determined, and a text is generated that is appropriate for a reading level of an individual based on performance of the individual on the exam unit.

Abstract: A method for diagnostic assessment and proficiency scaling of test results is provided. The method uses as input a vector of item difficulty estimates for each of n items and a matrix of hypothesized skill classifications for each of said n items on each of skills. The method includes using a tree-based regression analysis based on the vector and matrix to model ways in which required skills interact with different item features to produce differences in item difficulty. This analysis identifies combinations of skills required to solve each item, and forms a plurality of clusters by grouping the items according to a predefined prediction rule based on skill classifications. A nonparametric smoothing technique is used to summarize student performance on the combinations of skills identified in the tree-based analysis. The smoothing technique results in cluster characteristic curves that provide a probability of responding correctly to items with specified skill requirements.

Abstract: A method for diagnostic assessment and proficiency scaling of test results is provided. The method uses as input a vector of item difficulty estimates for each of n items and a matrix of hypothesized skill classifications for each of said n items on each of k skills. The method includes using a tree-based regression analysis based on the vector and matrix to model ways in which required skills interact with different item features to produce differences in item difficulty. This analysis identifies combinations of skills required to solve each item, and forms a plurality of clusters by grouping the items according to a predefined prediction rule based on skill classifications. A nonparametric smoothing technique is used to summarize student performance on the combinations of skills identified in the tree-based analysis. The smoothing technique results in cluster characteristic curves that provide a probability of responding correctly to items with specified skill requirements.

Abstract: A carrier for doll-type toys is provided having a pocket like enclosure for carrying the doll-type toy in a partially displayed position. The enclosure includes a double wall section forming an envelope or bag, in which the doll-type toy is carried, and a single wall section, against which the doll-type toy is partially displayed. This single wall section extends beyond and above the double walled section. Carrying straps permit the enclosure to be carried on the back of a child, in back-pack fashion. The carrying straps are attached adjacent to the free end of the single wall section and at the side of the double wall section. A second and smaller pocket enclosure may be attached to the front face of the carrier.

Abstract: A computerized mastery testing system providing for the computerized implementation of sequential testing in order to reduce test length without sacrificing mastery classification accuracy. The mastery testing system is based on Item Response Theory and Bayesian Decision Theory which are used to qualify collections of test items, administered as a unit, and determine the decision rules regarding examinee's responses thereto. The test item units are randomly and sequentially presented to the examinee by a computer test administrator. The administrator periodically determines, based on previous responses, whether the examinee may be classified as a nonmaster or master or whether more responses are necessary. If more responses are necessary it will present as many additional test item units as required for classification.