Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: predicting an exhibited sentiment of a certain user on being presented one or more candidate education segment, wherein the predicting is in dependence on historical sentiment parameter values of one or more user on being exposed to presented educational content, the historical sentiment parameter values being stored in a data repository, wherein the presented education segments comprise digital media content adapted for playing by a digital media player; selecting at least one of the one or more candidate education segment in dependence on a result of the predicting so that a selected at least one of the one or more candidate education resulting from the selecting is personalized for the certain user; and presenting to the certain user the selected at least one of the one or more candidate education segment.

Abstract: A driverless vehicle system comprises a processor that is configured to communicate information related to attributes of a focus autonomous vehicle (FAV) to an other peer vehicle (PV) and/or a central repository system (CRS). The processor is further configured to communicate information about a corrective action by at least one of the FAV and a previously contacted vehicle to the CRS or to a further peer vehicle that is within a predefined region.

Abstract: Methods and systems for delivering personalized learning material are disclosed. A method includes providing, by a computer device, current learning material and advance learning material to a plurality of students; monitoring, by the computer device, a reading performance and a complexity of the advance learning material for a student of the plurality of students; determining, by the computer device, a student comprehension score for the student based on the monitoring the reading performance and the complexity of the advance learning material; and providing, by the computer device, additional learning material to the student based on the student comprehension score being above a threshold.

Abstract: Price transparency via aggregation of real-time quoted prices is provided. Real-time quoted service price data corresponding to a service application is collected from registered client devices. The real-time quoted service price data corresponding to the service application is aggregated. It is determined whether a price quoted to a client device by the service application is fair based on the aggregated real-time quoted service price data. In response to determining that the price quoted to the client device by the service application is unfair, a graphical input button is provided within a price transparency display on the client device that enables a user of the client device to send a complaint to a service provider corresponding to the service application with a price transparency log.

Abstract: Methods and systems for delivering personalized learning material are disclosed. A method includes providing, by a computer device, current learning material and advance learning material to a plurality of students; monitoring, by the computer device, a reading performance and a complexity of the advance learning material for a student of the plurality of students; determining, by the computer device, a student comprehension score for the student based on the monitoring the reading performance and the complexity of the advance learning material; and providing, by the computer device, additional learning material to the student based on the student comprehension score being above a threshold.

Abstract: Methods and systems may provide for technology to calculate an amount of time that it will take to teach a topic based on input data that defines a schedule, compute an updated amount of time that it will take to teach the topic based on information related to an unexpected event and dependent concepts related to the topic, automatically calculate a possible time period in which concepts can be taught before an occurrence of the unexpected event; determine whether concepts are coverable in the calculated time period, and automatically reorganize the schedule based on the updated amount of time that it will take to teach the topic and the calculated time period before the occurrence of the unexpected event.

Abstract: Price transparency via aggregation of real-time quoted prices is provided. Real-time quoted service price data corresponding to a service application is collected from registered client devices. The real-time quoted service price data corresponding to the service application is aggregated. It is determined whether a price quoted to a client device by the service application is fair based on the aggregated real-time quoted service price data. In response to determining that the price quoted to the client device by the service application is unfair, a graphical input button is provided within a price transparency display on the client device that enables a user of the client device to send a complaint to a service provider corresponding to the service application with a price transparency log.

Abstract: Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: predicting an exhibited sentiment of a certain user on being presented one or more candidate education segment, wherein the predicting is in dependence on historical sentiment parameter values of one or more user on being exposed to presented educational content, the historical sentiment parameter values being stored in a data repository, wherein the presented education segments comprise digital media content adapted for playing by a digital media player; selecting at least one of the one or more candidate education segment in dependence on a result of the predicting so that a selected at least one of the one or more candidate education resulting from the selecting is personalized for the certain user; and presenting to the certain user the selected at least one of the one or more candidate education segment.