Sentence examples for readiness data from inspiring English sources

We analyze self-declared career readiness data as well as standard individual learner profiles which include career interests and domain-related qualifications.

The clustering algorithm analyzes different categories of career readiness data to predict a hypothetical career practice and bring learners with similar career patterns together into the same cluster.

To solve the cold-start problem of CoP construction, we use the career readiness data warehouse discussed earlier as a source for initializing groups (or clusters) of learners and denote each such cluster as a CoP (Fig. 6).

In this paper, we focus on career prediction, which derives career readiness data analytics out of an institution-wide portal that stores a data warehouse about learners, along with individual learners' information which are structured into a career profile that includes attributes such as career interests and domain-related qualifications.

Secondly, estimates on how many individuals in their prime working age currently have a college education (i.e. an Associate's degree or higher) ranges from 26%to43%3%, which while varying widely (and a testament to how poor our labor readiness data is), still clearly indicates that we are well short of what future jobs require.

Canada and the United States collected some school readiness data, such as with the EDI, but data collection was patchy and not analyzed at a country-level.

On Tuesday, Mayor Michael R. Bloomberg chose to focus on that steady gain rather than on the dismal college-readiness data.

The current IAT has been very challenging from multiple logistic aspects: recruitment and coordination of biocurators that can properly evaluate the systems; selection of datasets; issues of system readiness and data collection, formatting and processing.

In preparation for the development of a tool to measure readiness for data-sharing, we tested whether organizational capacities known to be related to readiness were associated with successful participation in an American data-sharing collaborative for quality improvement.

A proposed metadata list includes basic data set information and disciplinary information for experimental geomorphology, including metadata for evaluating data quality and readiness for reuse.

In our experience, a business' awareness and a readiness to tackle data theft or staff poaching are directly linked to its experience of such an event.