Methods, systems, and apparatus, including computer-readable storage media, for course assignment by a multi-learning management system. The system can receive data from a variety of individual learning management systems offering different courses. The system can use feedback data of a user base for the system to cluster courses by predicted difficulty, and generate, from the clusters, a sequence of courses for a target user. The sequence of courses can include at least one course from each cluster, with courses from a first cluster with a lower overall difficulty measure preceding courses in a second cluster with a higher overall difficulty measure in the sequence, wherein the starting cluster can be calculated according to the estimated level of the target user.

A computer that selectively provides a recommendation is described. During operation, the computer may obtain game information associated with one or more video games played by an individual, where the game information specifies decisions and behaviors in the one or more video games while the individual played the one or more video games. Then, the computer may compute, for the individual, scores for a set of predefined attributes associated with occupations based at least in part on the decisions and/or the behaviors. Next, the computer may selectively provide the recommendation based at least in part on the computed scores, where the recommendation is associated with: an academic area of study for the individual, an employment opportunity for the individual, and/or an occupation for the individual.

A method and system for creating a learning graph may include accessing a general knowledge graph, the general knowledge graph including a plurality of items of information about a plurality of general knowledge topics, extracting a plurality of learning topics, the plurality of learning topics being topics associated with a desired learning curriculum, identifying associations between one or more of the plurality of learning topics and one or more of the plurality of items of information in the general knowledge graph, upon identifying the associations, utilizing the associations to create a learning graph based on at least one of the one or more of the plurality of learning topics, the one or more of the plurality of items of information and the associations between them, the learning graph being a learning knowledge visualization graph, and transmitting the learning graph to a learning application for use in providing the desired learning curriculum.

The present disclosure provides a system and method for recommending context and sequence aware based training set to a user. The system identifies various items and keywords of a plurality of earlier trainings of the users' interest and generates a context and sequence aware recommendation model based on the context of the identified keywords. It uses a collapsed Gibbs Sampling as in generative modelling for prior trainings. Further, it applies the context and sequence aware recommendation model on various keywords that are of users' interest. The context and sequence aware recommendation model infers a plurality of subsequent trainings based on context derived from the keywords. In addition to this, the model is generated to rank the inferred plurality of subsequent topics using a probability distribution over subsequent keywords. At the last, it recommends at least one topic to the user based on ranking of the plurality of trainings.

Techniques are described for assigning users to swarms based on skill and generating recommendations for increasing skill proficiency. In some implementations, a request specifying a set of requirements for completing a task includes at least one required skill, optionally in combination with a required minimum proficiency value(s). Metrics can be generated based on statistical analysis of instances in which a task is at least one of: (i) assigned to a user who is not associated with a required skill, (ii) assigned to a user whose proficiency value is below a required minimum proficiency value, or (iii) assigned after a delay due to unavailability of a user who is associated with a required skill. The computer system can identify a first skill for improving the metrics, identify a user for becoming associated with the first skill, and send a notification recommending an online learning resource relating to the first skill.

The invention is a video directory. The directory is a digital advertising product, having a system of presenting business owners and businesses to the public. The system is a compilation of videos on one or more platforms or listing sites that use a directory format.

A system for accreditation of industrial professionals is disclosed. The plurality of subsystems includes a profile generation subsystem, configured to create user profiles for one or more registered users using one or more user credentials. The plurality of subsystems includes an event manager subsystem, configured to provide access to continued industrial education content to the one or more registered users based on the created user profiles and also configured is manage one or more tasks of the user profiles relating to the content. The plurality of subsystems also includes a credit generation subsystem, configured to generate credit scores for the user profiles based on the one or more tasks completed by the one or more registered users. The plurality of subsystems also includes a report generation subsystem configurate to generate and publish a report on the one or more tasks.

Methods, computer-readable media, software, and career planning system may receive inputs from a user in response to questions related to personality and may provide outputs related to preferences for schooling and other career options, such as courses, colleges, universities, programs, majors, careers, jobs, and/or companies. The personality of the user may be broken into four (or other numbers) different pillars, life vectors, and/or personality characteristics and the user may be provided a score in each of the pillars, life vectors, and/or personality characteristics based on the responses to the questions. These scores for each of the pillars, life vectors, and/or personality characteristics may be then matched to various schooling and career option recommendations for the user.

There is disclosed a method and system for determining a productivity rate of a user in a computer-implemented crowd-sourced environment. The method comprises, in a training phase: acquiring a training project; determining, set of project-specific features indicative of one or more characteristics of the training project; acquiring, a plurality of training results; determining a set of user-task specific features; determining, a user activity history associated with the user; generating a set of training data including the set of project-specific features, the set of user-task specific features, and the user activity history; training a machine learning algorithm (MLA), the training including: determining, a set of features representative of a property of the set of training data; and generating an inferred function based on the set of features, the inferred function being configured to determine the productivity rate of the user for a given project.

Systems and methods for enhanced collection of training data for machine learning to improve worksite safety and operations. One embodiment is a system with an interface to instruct a user device of a user to capture first sensor data of an object performing one or more known actions. The system also includes a training controller to allocate points as an award to the user of the user device for correctly capturing the object performing the one or more known actions, and to generate an instruction for the user to capture second sensor data of the object performing one or more unknown actions if the user has exceeded a threshold of awarded points. The training controller trains a machine learning function based on the second sensor data of the object performing the one or more unknown actions as identified by the user having exceeded the threshold of awarded points.