A computer implemented method, comprising: selecting a plurality of dynamic models for evaluating a scoring request, wherein the dynamic models are stored on a scoring database; deploying each of the dynamic models to one of a plurality of evaluators; receiving, at a scoring node, a scoring request for a score of a lead from at least one requester; separating the scoring request into a plurality of scoring requests, wherein each of the scoring requests is assigned to one of the selected dynamic models wherein the request is separated by model and aggregate model results and sent to an evaluator queue; combining results from each of the dynamic models; evaluating the combined results wherein each of the evaluators sends a model evaluation response to the response queue; and providing a response to the scoring request based on the evaluation of the combined results.

Systems, methods, and computer program products for determining an application status of an applicant for an educational program may include receiving cohort performance data comprising first data entries for participants that have respectively achieved outcomes for the educational program and applicant performance data comprising second data entries for the applicant, calculating adjusted cohort performance data based on the cohort performance data and first data characteristics, providing a predictor model based on the adjusted cohort performance data and the outcomes, sequentially changing predictive parameters of the first data characteristics to create second data characteristics and creating an adjusted predictor model based on the second data characteristics and the outcomes, calculating adjusted applicant performance data based on the applicant performance data and the second data characteristics, and calculating a probability of success for the applicant in the educational program based on the adjusted applicant performance data and the adjusted predictor model.

A computer implemented method, comprising: selecting a plurality of dynamic models for evaluating a scoring request, wherein the dynamic models are stored on a scoring database; deploying each of the dynamic models to one of a plurality of evaluators; synchronizing the dynamic models, the synchronizing including at least determining a same version of the dynamic models is deployed and available to all evaluators; receiving, at a scoring node, a scoring request for a score of a lead from at least one requester; separating the scoring request into a plurality of scoring requests, wherein each of the scoring requests is assigned to one of the selected dynamic models wherein the request is separated by model and aggregate model results and sent to an evaluator queue; combining results from each of the dynamic models; evaluating the combined results wherein each of the evaluators sends a model evaluation response to the response queue; and providing a response to the scoring request based on the evaluation of the combined results.

Examples related to facilitating the training, assessment, and dispatching of technicians to a customer location for at least the purpose of servicing or repairing machines, devices, or systems present at the customer location are presented. The technician can be selected for dispatch to the customer location by review of database information, including training information for the technician, among other things. While at the customer location, the technician can be provided with training information delivered via a training module, where such training material content can comprise written materials, photos, video, virtual assistance, remote assistance, games, or the like. The technician can generate videos or other collateral while at the customer location, which can be included for use in a library of training materials for subsequent use, or for other purposes. Various systems and processes for facilitating the selection, management and deployment of training materials for technicians are also provided herein.

Program placement with desirability indicators can include: generating a user interface that includes an indicator of a relative desirability of at least one of a plurality of programs registered on a placement platform among a set of candidates who use the placement platform to place among the programs; and determining the relative desirability in response to a store of history data pertaining to how the candidates used the placement platform to place among the programs during at least one placement season for the programs.

A system and a method are disclosed for satisfying a curriculum during nomadic travel. In an embodiment, an accommodation management system receives, from a client device, information describing a desired curriculum scope and a time period. The system optimizes a curriculum based on subjects mapped from the curriculum scope and the time period. The system maps the curriculum to destinations and determines a set of geographic regions including the destinations. The system determines a set of geographic regions including the destinations. For each geographic region, the system retrieves, from a listing database, listing available in the geographic region during a subset of the time period. The system ranks the retrieved listings for each geographic region based on the destinations and transmits, for display on a user interface at the client device, an accommodation recommendation including listings in one or more of the geographic regions.

An extensible electronic learning system having at least one learning management system having a learning management processor and a learning management memory operatively coupled thereto, said processor programmed for executing at least one learning management service and providing at least one extensible integration module. Each extensible integration module includes a predefined vendor services interface comprising at least one vendor services definition, and a vendor configuration upload component for receiving vendor configuration settings about at least one vendor. The at least one vendor having a vendor processor and a vendor memory operatively coupled thereto, said vendor processor programmed for executing a least one vendor services, the at least one of said vendor services implementing the at least one of said vendor service definition, and providing at least one vendor integration module, each vendor integration module comprising the predefined vendor services interface and the vendor configuration settings. The vendor configuration settings are received by the extensible integration module such that the learning management system may request the at least one of said vendor services based on the extensible integration module.

Certain embodiments may generally relate to systems and methods for rapidly deploying predictive scoring models on a platform that allows for easy integration. A method may include selecting one or more dynamic models for evaluating a scoring request. The method may also include receiving, at a scoring node, a scoring request from at least one requester. The method may further include separating the scoring request into one or more scoring requests, in which each of the one or more score requests may assigned to each of the selected one or more dynamic models. The method may also include combining results from each of the one or more dynamic models. The method may further include evaluating the combined results. The method may also include providing a response to the scoring request based on the evaluation of the combined results.

Systems, methods, and computer program products for determining an application status of an applicant for an educational program may include receiving cohort performance data comprising first data entries for participants that have respectively achieved outcomes for the educational program and applicant performance data comprising second data entries for the applicant, calculating adjusted cohort performance data based on the cohort performance data and first data characteristics, providing a predictor model based on the adjusted cohort performance data and the outcomes, sequentially changing predictive parameters of the first data characteristics to create second data characteristics and creating an adjusted predictor model based on the second data characteristics and the outcomes, calculating adjusted applicant performance data based on the applicant performance data and the second data characteristics, and calculating a probability of success for the applicant in the educational program based on the adjusted applicant performance data and the adjusted predictor model.

A blockchain configuration may provide a simple and secure infrastructure for students to identify subject matter experts as tutors and career mentors, as well as earn scholarship funds in the form of microtransactions of digital currency. One example method may comprise one or more of transmitting a query to a network of devices to respond in exchange for a microtransaction reward of digital currency; receiving a plurality of responses from the network of devices; determining one or more best responses from among the plurality of responses; transmitting the reward to the one or more devices that provided the one or more best responses; combining data related to the query, responses, best answers, and microtransactions into a block of data; and recording the block of data on a blockchain. The method may also include validating the query and plurality of responses in exchange for a microtransaction according to a blockchain proof-of-work configuration.