A method includes identifying an error in computer readable code and identifying a user profile associated with a development environment used to generate the computer readable code. The method also includes searching a database for user profile information indicative of a training sequence performed by a user associated with the user profile. The method further includes identifying a type of the error identified in the computer readable code and comparing the type of the error with one or more properties associated with the training sequence. The method additionally includes calculating a severity of the error based on the comparison with the one or more properties associated with the training sequence, and assigning a competence level to the user associated with the user profile based on the calculated severity. The method further includes generating a remediation suggestion to correct the error in the computer readable code based on the competence level.

Techniques for using online user activity in determining relevance of attributes to improve computer functionality in generating recommendations of online content are disclosed herein. In some embodiments, a computer system calculates a corresponding relevance score for each attribute of a user based on a total number of online postings for which the user has performed at least one of a plurality of online actions within a particular sliding window of time defining a most recent time period, an attribute activity number representing a number of online postings in the plurality of online postings that have the attribute, and an inverse of a frequency value representing how many of a total number of online postings published within the particular sliding window of time have the attribute. In some embodiments, the computer system causes at least one recommendation associated with the user to be displayed based on the calculated relevance scores.

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.

A member profile including a vector containing a field for each of a plurality of skills and a rating of one or more of the skills in the vector for a member of a social networking service is obtained. A first distance indicating a vector distance between the vector of the member profile and a vector of a hypothetical member profile representing the perfect job candidate is obtained. A hypothetical member profile for the member is created by combining the vector of the member profile with the indication of how each of the one or more skills is improved through taking the course from course information. A second distance between the member and the hypothetical perfect candidate for the job is obtained, and the difference between the first distance and the second distance is calculated to determine an estimate of how much the course will increase the member's job chances.

Contemplated herein is a system and method for answering user queries, the system and method including a tracked user skill database, a historical database, and a training content database wherein processing circuitry can determine a tool being utilized by a user; receive a user input query; tokenize the user input query and associate one or more tokens with the user input query; and search the databases so as to match the query with content within the databases. The system can then present potential matches to the user and receiving input regarding user satisfaction with regard to the potential matches presented. If the answer is not satisfactory the system can then analyze the tracked user skill database for user profiles indicating proficiency in the matched feature of the query.

An online career guidance system generates and displays superimposed polygons to a user for a corresponding career. The first polygon may have three to eight vertices, with each vertex representing a career skill. The distance from a focus to each vertex represents a career skill level predetermined to be needed for that career skill for that career. The second polygon has the same number of vertices as the first polygon that represent the same career skills and shares the same focus. The distance from the focus to each vertex of the second polygon represents a user skill level determined from previous achievements of the user. The distance between a career skill level and a user skill level (preferably both normalized to the same scale) visually represents any career skill deficits (or over qualifications) the user may have for the selected career. The online career guidance system may determine, recommend and register the user into one or more classes, clubs and/or projects that are likely to reduce or eliminate any career skill deficits of the user.

A skill acquisition platform may receive input identifying a target role for a user, may identify users from the plurality of users that have worked in the target role, may identify, based on a similarity metric, a subset of the users that have worked in the target role and have similar career trajectories to the user, may cluster vectors of the user and vectors of the subset of the users that have worked in the target role to generate a plurality of skill groups, may generate a directed network graph that represents links between the plurality of skill groups, may identify one or more paths between a first skill group associated with the user and a second skill group associated with the target role, and may automatically generate a descriptive analysis of the one or more paths.

Techniques described herein relate to generation and time-based degradation of digital credentials within digital credentialing environments. A digital credential generation system may include a digital credential issuer and a platform server configured to determine and implement time-based degradation of the digital credentials issued to credential receivers. Different digital credentials may have different associated degradation functions, based on the type of the digital credential and/or additional factors such as the additional credentials issued to the receiver and the monitoring data of the receiver. The magnitude values computed for issued digital credentials, using the degradation functions, may be used to determine credential expiration times, re-credentialing times and/or requirements, and the like.

Techniques relate to managing digital credentials. An employee records database is accessed to identify a plurality of employees. Within the plurality of employees, a first set of employees is identified. For each employee in the first set of employees, a server computer hosting a digital credential repository is accessed to identify a plurality of digital credentials associated with the employee. The plurality of digital credentials of each employee in the first set of employees are analyzed to generate a statistical analysis of the plurality of digital credentials. With the statistical analysis complete, data identifying the statistical analysis is transmitted to a user device.

A system is provided for operator profiling based on pre-installed sensor measurement. In example implementations, the system extracts a set of segmented time series data associated with a unit of operation and build models which distinguish the operators by machine learning algorithms. The system uses the models to output the evaluation score assigned to each operation, identify the key movements for skilled/non-skilled operators, and recommends appropriate actions to improve operation skill or adjust the scheduling of the operators.