Techniques are provided for machine learning-based educational content adaptation based on user personal characteristics. One method comprises obtaining personal characteristics of at least one user; applying the personal characteristics of the at least one user to at least one machine learning model to automatically adapt at least one educational content item for the at least one user using one or more of the applied personal characteristics of the at least one user; and initiating a provision of the at least one automatically adapted educational content item to the at least one user. Technology-related characteristics of the at least one user may also be applied to the at least one machine learning model to further adapt the at least one educational content item for the at least one user using the applied technology-related characteristics.

Techniques are provided for machine learning-based educational content adaptation based on user personal characteristics. One method comprises obtaining personal characteristics of at least one user; applying the personal characteristics of the at least one user to at least one machine learning model to automatically adapt at least one educational content item for the at least one user using one or more of the applied personal characteristics of the at least one user; and initiating a provision of the at least one automatically adapted educational content item to the at least one user. Technology-related characteristics of the at least one user may also be applied to the at least one machine learning model to further adapt the at least one educational content item for the at least one user using the applied technology-related characteristics.

According to techniques and systems disclosed herein a pre-assessment profile may be generated for a learner based on a designated activity. The pre-assessment profile may be based on evaluating learner provided inputs or responses to one or more inquiries. A designated activity may be received and a plurality of activity steps to perform the designated activity may be generated and may be based on the pre-assessment profile. A media type may be identified for each activity step of the plurality of activity steps based on the pre-assessment profile for the learner, and may be provided to the learner. A learner's ability to perform the designated activity may be determined, based on applicable feedback information. According to techniques disclosed herein, a trigger event may be learned during learning mode and may include trigger event surrounding data for a behavioral attribute. A response may be generated based on detecting the trigger event.

According to techniques and systems disclosed herein a pre-assessment profile may be generated for a learner based on a designated activity. The pre-assessment profile may be based on evaluating learner provided inputs or responses to one or more inquiries. A designated activity may be received and a plurality of activity steps to perform the designated activity may be generated and may be based on the pre-assessment profile. A media type may be identified for each activity step of the plurality of activity steps based on the pre-assessment profile for the learner, and may be provided to the learner. A learner's ability to perform the designated activity may be determined, based on applicable feedback information. According to techniques disclosed herein, a trigger event may be learned during learning mode and may include trigger event surrounding data for a behavioral attribute. A response may be generated based on detecting the trigger event.

According to an embodiment of the present invention, a method of recommending educational content includes acquiring search information of a user, extracting searched question information based on the search information, acquiring a solution content set related to the question information, the solution content set including first solution information and second solution information, calculating learning ability information of the user based on the search information, calculating an index related to an expected educational effect based on the learning ability information and the solution content set, selecting target solution content from the solution content set based on the index, and transmitting the target solution content.

According to an embodiment of the present invention, a method of recommending educational content includes acquiring search information of a user, extracting searched question information based on the search information, acquiring a solution content set related to the question information, the solution content set including first solution information and second solution information, calculating learning ability information of the user based on the search information, calculating an index related to an expected educational effect based on the learning ability information and the solution content set, selecting target solution content from the solution content set based on the index, and transmitting the target solution content.

A computer-implemented method for generating a description of a target skill set using domain specific language, a computer program product, and a system. Embodiments may comprise, on a processor, ingesting a data set related to the target skill from a data store, semantically analyzing the data set to generate a skill ontology, generating a hyperplane to separate one or more priority skills from among the plurality of related skills, generating a description for the target skill from the one or more priority skills, and presenting the generated description to a user. The skill ontology may include relationships between the target skill and a plurality of related skills.

A computer-implemented method for generating a description of a target skill set using domain specific language, a computer program product, and a system. Embodiments may comprise, on a processor, ingesting a data set related to the target skill from a data store, semantically analyzing the data set to generate a skill ontology, generating a hyperplane to separate one or more priority skills from among the plurality of related skills, generating a description for the target skill from the one or more priority skills, and presenting the generated description to a user. The skill ontology may include relationships between the target skill and a plurality of related skills.

A method of providing visual feedback to a driver based on data collected during vehicle operation. A processor at the vehicle analyzes vehicle data and determines when predetermined threshold values have been reached for particular parameters. Whenever such a threshold is reached, an audible indication is provided to the driver, indicating that the baseline has been exceeded. Certain parameters have at least two threshold values. When a first threshold value is reached, an alert is presented to the driver, but no data is recorded or reported. When a second threshold value is reached, another alert is presented to the driver, and data is recorded for reporting to a driver manager or supervisor. This approach provides a driver warning, that if they correct the triggering behavior, their supervisor is never notified of that behavior. However, if the behavior escalates, and the second threshold is breached, the behavior is recorded.

Embodiments generally relate to improving education and training sessions. In some embodiments, a method includes determining an attentiveness level associated with a first user during a learning session. The method further includes determining one or more drops in the attentiveness level during the learning session. The method further includes tracking inattentiveness information associated with the one or more drops in the attentiveness level. The method further includes performing one or more corrective actions in response to the one or more drops in the attentiveness level based at least in part on the inattentiveness information.