Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for determining learning paths can include a computer system identifying a target performance score for a plurality of respondents with respect to a plurality of first assessment items, and determining for each respondent an ability level and a target ability level. The computer system can cluster the respondents into a sequence of groups of respondents based on ability levels of the respondents, determine a sequence of mastery levels, each mastery level having a corresponding item difficulty range, using the respective ability levels and the target ability level of the respondents. The computer system can assign to each mastery level a corresponding set of second assessment items using the difficulty range of the mastery level, and map each group of respondents to a corresponding first mastery level. The corresponding first mastery level and subsequent mastery levels represent a learning path of the group of respondents.

Abstract: Systems and methods for learner-specific recommendations can include a computer system identifying one or more reference performance profiles. The computer system can receive assessment data indicative of performances of a plurality of respondents with respect to the plurality of assessment items. The computer system can determine, using the assessment data and the reference performance profiles, an ability level for each respondent of the plurality of respondents and a reference ability level for each reference performance profile of the one or more reference performance profiles. The computer system can compare, for a first respondent, an ability level of the first respondent to at least one reference ability level of the one or more reference performance profiles. The computer system can determine a recommendation for the first respondent based on the comparison.

Abstract: Systems and methods for matching roles and respondents can include a computer system identifying one or more target competency level profiles of one or more roles. The computer system can receive assessment data indicative of performances of a plurality of respondents with respect to the plurality of assessment items. The computer system can determine, using the assessment data and the target competency level profiles, an ability level for each respondent of the plurality of respondents and a target ability level for each target competency level profile of the one or more target competency level profiles. The computer system can associate a first respondent of the plurality of respondents with a first role of the one or more roles based on a difference between an ability level of the first respondent and a target ability level of a first target competency level profile of the first role.

Abstract: Systems and methods for determining learning paths can include a computer system identifying a target performance score for a respondent with respect to a plurality of first assessment items, determine an ability level of the respondent and a target ability level corresponding to the target performance score for the respondent using assessment data indicative of performances of a plurality of respondents with respect to a plurality of first assessment items. The computer system can determine a sequence of mastery levels of the respondent, where each mastery level can have a corresponding item difficulty range. The computer system can determine, for each mastery level of the sequence of mastery levels, a corresponding set of second assessment items using. The sequence of mastery levels and the corresponding sets of second assessment items represent a learning path of the respondent to progress from the ability level to the target ability level.

Abstract: Systems and methods for education instrumentation can include a computer system receiving assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items. The computer system can determine, using the assessment data, difficulty levels of the plurality of assessment items and ability levels of the plurality of respondents. The computer system can determine respondent-specific parameters for each respondent of the plurality of respondents, using the difficulty levels of the plurality of assessment items and the ability levels of the plurality of respondents. The computer system can determine contextual parameters common to the plurality of assessment items. The computer system can provide access to the respondent-specific parameters of the plurality of respondents and the one or more contextual parameters.

Abstract: Systems and methods for education instrumentation can include a computer system receiving assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items. The computer system can determine, using the assessment data, item difficulty parameters of the plurality of assessment items and respondent ability parameters of the plurality of respondents. The computer system can determine item-specific parameters for each assessment item of the plurality of assessment items, using the item difficulty parameters of the plurality of assessment items and respondent ability parameters of the plurality of respondents. The computer system can determine contextual parameters common to the plurality of assessment items. The computer system can provide access to the item-specific parameters of the plurality of assessment items and the one or more contextual parameters.

Abstract: Systems and methods for education instrumentation can include a computer system receiving first assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items. The computer system can identify reference assessment data associated with one or more reference assessment items, and determine, using the first assessment data and the reference assessment data, (i) difficulty levels for the plurality of assessment items and the one or more reference assessment items, and (ii) an ability levels for the plurality of respondents. The computer system can determine, for each assessment item of the plurality of assessment items, one or more item-specific parameters including a normalized difficulty level defined in terms of the difficulty level of the assessment item and one or more difficulty levels of the one or more reference assessment items.

Abstract: Systems and methods for learner-specific recommendations can include a computer system identifying one or more reference performance profiles. The computer system can receive assessment data indicative of performances of a plurality of respondents with respect to the plurality of assessment items. The computer system can determine, using the assessment data and the reference performance profiles, an ability level for each respondent of the plurality of respondents and a reference ability level for each reference performance profile of the one or more reference performance profiles. The computer system can compare, for a first respondent, an ability level of the first respondent to at least one reference ability level of the one or more reference performance profiles. The computer system can determine a recommendation for the first respondent based on the comparison.

Abstract: Systems and methods for education instrumentation can include a computer system receiving first assessment data indicative of performances of a plurality of respondents with respect to a plurality of assessment items. The computer system can identify reference assessment data for one or more reference respondents with respect to the plurality of assessment items. The computer system can determine, using the first assessment data and the reference assessment data, (i) difficulty levels for the plurality of assessment items, and (ii) ability levels for the plurality of respondents and for each reference respondent of the one or more reference respondents. The computer system can determine, for each respondent of the plurality of respondents, one or more respondent-specific parameters including a normalized respondent ability parameter defined in terms of the respondent ability parameter of the respondent and one or more respondent ability parameters of the one or more reference respondents.

Abstract: Systems and methods for learner-specific recommendations can include a computer system identifying one or more reference performance profiles. The computer system can receive assessment data indicative of performances of a plurality of respondents with respect to the plurality of assessment items. The computer system can determine, using the assessment data and the reference performance profiles, an ability level for each respondent of the plurality of respondents and a reference ability level for each reference performance profile of the one or more reference performance profiles. The computer system can compare, for a first respondent, an ability level of the first respondent to at least one reference ability level of the one or more reference performance profiles. The computer system can determine a recommendation for the first respondent based on the comparison.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout a life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: Systems and methods for education instrumentation can include one or more servers configured generate a plurality of models for modeling various aspects of an education process using training data related to academic performance of students. The one or more servers can collect data from client devices associated with various education institutions or stakeholders throughout an life cycle of the education process. The one or more servers can use the generated models and the collected data to assess the addressing of education standards and predict or estimate performance metrics associated with the education process. The one or more servers can provide computed metrics or assessments of how well education standards are addressed to one or more client devices for display.

Abstract: There is provided improved management of a communications network including a plurality of sub-systems for providing services to subscribers. Data is received from a plurality of source systems in a communications network, said source systems encoding data according to different data models. Rules of processing the data received data from each of the source systems are defined. At least one customer experience event of the communications network is generated on the basis of the rules.