Abstract: An online learning system selects content for a learning session and opens a user interface to start the learning session on a plurality of devices. A mode of instruction is selected for the learning session. An activity to perform associated with the content is presented. Performance of the activity is monitored and a performance metric and/or a heterogeneity metric associated with a key performance indicator for the activity performed is generated. Responsive to determining that the performance metric and/or a heterogeneity metric is outside of the target range, the mode of instruction may be switched automatically.

Abstract: A determination regarding whether to intervene in a dialog to provide system-initiated assistive information involves monitoring a dialog between at least two participants and capturing data from a dialog environment containing at least one of the participants. The captured data represent the content of the dialog and physiological data for one or more participants. Assistive information relevant to the dialog and participants is identified, and the captured data are used to determine an intervention index of delivering the assistive information to one or more participants during the dialog. This intervention index is then used to determine whether or not to intervene in the dialog to deliver the assistive information to one or more participants.

Abstract: Embodiments are directed to a computer-implemented method that includes receiving, at a triggering system, input data about an environment. Machine learning (ML) algorithms extract features from the input data and analyze relationships among and between the features to generate user-interaction-readiness model of person-A's readiness to participate in a human interaction with other persons in the environment. The ML algorithms to apply real-world data about person-A to the user-interaction-readiness model to generate a first classification output comprising data representing person-A's readiness to participate in a human interaction with other persons in the environment. Based at least in part on a determination that person-A and another person(s) in the environment are interaction candidates, the triggering system triggers the initiation of human interaction strategies configured to facilitate human interactions between person-A and the previously identified another person(s) in the environment.

Abstract: Embodiments are directed to a computer-implemented method that includes receiving, at a triggering system, input data about an environment. Machine learning (ML) algorithms extract features from the input data and analyze relationships among and between the features to generate user-interaction-readiness model of person-A's readiness to participate in a human interaction with other persons in the environment. The ML algorithms to apply real-world data about person-A to the user-interaction-readiness model to generate a first classification output comprising data representing person-A's readiness to participate in a human interaction with other persons in the environment. Based at least in part on a determination that person-A and another person(s) in the environment are interaction candidates, the triggering system triggers the initiation of human interaction strategies configured to facilitate human interactions between person-A and the previously identified another person(s) in the environment.

Abstract: An online learning system selects content for a learning session and opens a user interface to start the learning session on a plurality of devices. A mode of instruction is selected for the learning session. An activity to perform associated with the content is presented. Performance of the activity is monitored and a performance metric and/or a heterogeneity metric associated with a key performance indicator for the activity performed is generated. Responsive to determining that the performance metric and/or a heterogeneity metric is outside of the target range, the mode of instruction may be switched automatically.

Abstract: A determination regarding whether to intervene in a dialog to provide system-initiated assistive information involves monitoring a dialog between at least two participants and capturing data from a dialog environment containing at least one of the participants. The captured data represent the content of the dialog and physiological data for one or more participants. Assistive information relevant to the dialog and participants is identified, and the captured data are used to determine an intervention index of delivering the assistive information to one or more participants during the dialog. This intervention index is then used to determine whether or not to intervene in the dialog to deliver the assistive information to one or more participants.

Abstract: A determination regarding whether to intervene in a dialog to provide system-initiated assistive information involves monitoring a dialog between at least two participants and capturing data from a dialog environment containing at least one of the participants. The captured data represent the content of the dialog and physiological data for one or more participants. Assistive information relevant to the dialog and participants is identified, and the captured data are used to determine an intervention index of delivering the assistive information to one or more participants during the dialog. This intervention index is then used to determine whether or not to intervene in the dialog to deliver the assistive information to one or more participants.

Abstract: A determination regarding whether to intervene in a dialog to provide system-initiated assistive information involves monitoring a dialog between at least two participants and capturing data from a dialog environment containing at least one of the participants. The captured data represent the content of the dialog and physiological data for one or more participants. Assistive information relevant to the dialog and participants is identified, and the captured data are used to determine an intervention index of delivering the assistive information to one or more participants during the dialog. This intervention index is then used to determine whether or not to intervene in the dialog to deliver the assistive information to one or more participants.

Abstract: A robot control method, system, and computer program product, include extracting robot data from the robot and user emotional state data of a user interacting with the robot, configuring a mission of a robot comprising a sequence of a plurality of tasks based on the robot data and the user emotional state data, and reconfiguring an order of the sequence of the plurality of tasks if a change in the user emotional state data is detected.

Abstract: An online learning system selects content for a learning session and opens a user interface to start the learning session on a plurality of devices. A mode of instruction is selected for the learning session. An activity to perform associated with the content is presented. Performance of the activity is monitored and a performance metric and/or a heterogeneity metric associated with a key performance indicator for the activity performed is generated. Responsive to determining that the performance metric and/or a heterogeneity metric is outside of the target range, the mode of instruction may be switched automatically.

Abstract: An online learning system selects content for a learning session and opens a user interface to start the learning session on a plurality of devices. A mode of instruction is selected for the learning session. An activity to perform associated with the content is presented. Performance of the activity is monitored and a performance metric and/or a heterogeneity metric associated with a key performance indicator for the activity performed is generated. Responsive to determining that the performance metric and/or a heterogeneity metric is outside of the target range, the mode of instruction may be switched automatically.

Abstract: Dynamically generating and delivering multimedia presentations targeted to an individual and to a group of users at public rendering devices such as displays in a point in time based on customers' static and dynamic context. Customers' emotional state(s) are assessed while and/or after watching personalized multimedia presentations and used to dynamically change presentations and to drive the allocation of resources for other services within the premise.

Abstract: Dynamically generating and delivering multimedia presentations targeted to an individual and to a group of users at public rendering devices such as displays in a point in time based on customers' static and dynamic context. Customers' emotional state(s) are assessed while and/or after watching personalized multimedia presentations and used to dynamically change presentations and to drive the allocation of resources for other services within the premise.

Abstract: Various mechanisms presented in this disclosure provide for dynamically adjusting the exhibition of subtitles. In one example, check-points are introduced into the subtitle file format that, once achieved by a multimedia player, provoke the rescheduling of the moment in which the texts contained in the subtitle file will be exhibited.

Abstract: A determination regarding whether to intervene in a dialog to provide system-initiated assistive information involves monitoring a dialog between at least two participants and capturing data from a dialog environment containing at least one of the participants. The captured data represent the content of the dialog and physiological data for one or more participants. Assistive information relevant to the dialog and participants is identified, and the captured data are used to determine an intervention index of delivering the assistive information to one or more participants during the dialog. This intervention index is then used to determine whether or not to intervene in the dialog to deliver the assistive information to one or more participants.

Abstract: A determination regarding whether to intervene in a dialog to provide system-initiated assistive information involves monitoring a dialog between at least two participants and capturing data from a dialog environment containing at least one of the participants. The captured data represent the content of the dialog and physiological data for one or more participants. Assistive information relevant to the dialog and participants is identified, and the captured data are used to determine an intervention index of delivering the assistive information to one or more participants during the dialog. This intervention index is then used to determine whether or not to intervene in the dialog to deliver the assistive information to one or more participants.

Abstract: A robot control method, system, and computer program product, include extracting robot data from the robot and user emotional state data of a user interacting with the robot, configuring a mission of a robot comprising a sequence of a plurality of tasks based on the robot data and the user emotional state data, and reconfiguring an order of the sequence of the plurality of tasks if a change in the user emotional state data is detected.

Abstract: A computer-implemented method for determining anomalous behavior in a digital education setting is provided. The method including the steps of: receiving, by a processor of a computer system, data corresponding to a position of a device with respect to at least one axis of the device; determining, by the processor of the computer system, whether the position of the device is an appropriate location for an intended use of the device; and notifying, by the processor of the computer system, wherein the device is not in an appropriate location for an intended use of the device.

Abstract: Various mechanisms presented in this disclosure provide for dynamically adjusting the exhibition of subtitles. In one example, check-points are introduced into the subtitle file format that, once achieved by a multimedia player, provoke the rescheduling of the moment in which the texts contained in the subtitle file will be exhibited.

Abstract: An information processing system, a computer readable storage medium, and a method for identifying review material can include collecting assessment data at a server from a plurality of client devices for subject matter in a course, analyzing collectively the assessment data from the plurality of client devices, and based on the analyzing, identifying a deficient subset of topics. The method can further include selecting review material based on the deficient subset of topics identified and sending the review material or a signal representative of the review material to the plurality of client devices which can include presenting the review material to the plurality of client devices. The method can include presenting the review material to each of the plurality of client devices in a format based on a student profile corresponding to each client device in the plurality of client devices.