Provided is a learning assistance technology that uses a learning history to check the level of comprehension by a learner in relation to a learning target. Included are a score calculation unit that uses a first occurrence ratio α(n) of a learning target Q(n) calculated using an occurrence frequency R(n) of the learning target Q(n) in a document to be used as a basis for creating a confirmation question and a second occurrence ratio β(n) of the learning target Q(n) weighted by viewing time and calculated using the occurrence frequency R(n) of the learning target Q(n) and a viewing time for each page of the document included in a learning history to calculate one of the difference between the first occurrence ratio α(n) and the second occurrence ratio β(n), the absolute value of the difference, or the ratio as a score S(n) of the learning target Q(n), and a query generation unit that treats the learning target Q(n) corresponding to the n for which the score S(n) is maximized as a query, that is, the learning target with which to create a confirmation question for a learner.

This disclosure generally covers systems and methods that provide guidance to create an electronic survey. In some embodiments, the systems and methods identify and provide a suggested survey topic—with a corresponding option to create an electronic survey—based on user input. In some embodiments, the systems and methods identify and provide one or more suggested electronic survey questions based on user input. In such embodiments, the systems and methods provide, for example, components of suggested electronic survey questions, previously composed and benchmarking electronic survey questions, or suggested revisions to electronic survey questions. In addition, the systems and methods can provide entire premade electronic surveys based on receiving user input from a survey administrator.

This disclosure generally covers systems and methods that provide guidance to create an electronic survey. In some embodiments, the systems and methods identify and provide a suggested survey topic—with a corresponding option to create an electronic survey—based on user input. In some embodiments, the systems and methods identify and provide one or more suggested electronic survey questions based on user input. In such embodiments, the systems and methods provide, for example, components of suggested electronic survey questions, previously composed and benchmarking electronic survey questions, or suggested revisions to electronic survey questions. In addition, the systems and methods can provide entire premade electronic surveys based on receiving user input from a survey administrator.

An apparatus for generating a temperature prediction model is disclosed. The apparatus for generating a temperature prediction model includes the temperature prediction model configured to provide a simulation environment, and a processor configured to set a hyperparameter of the temperature prediction model, train the temperature prediction model, in which the hyperparameter is set, so that the temperature prediction model, in which the hyperparameter is set, outputs a predicted temperature, update the hyperparameter on the basis of a difference between the predicted temperature, which is outputted from the trained temperature prediction model, and an actual temperature, and repeat the setting of the hyperparameter, the training of the temperature prediction model, and the updating of the hyperparameter on the basis of the difference between the predicted temperature and the actual temperature by a predetermined number of times or more to set a final hyperparameter of the temperature prediction model.

Systems and methods are provided for the design and implementation of experiments that facilitate the investigation of process data. The experiments involve recording the completion of a task by participants and then playing back the video of task completion to automatically probe participants about their affective, behavioral, and cognitive experiences. As a result of this system, information about affective, behavioral, and cognitive processes can be more easily investigated by researchers without computer programming knowledge. Corresponding apparatuses, systems, and methods are also discussed.

Systems and methods are provided for the design and implementation of experiments that facilitate the investigation of process data. The experiments involve recording the completion of a task by participants and then playing back the video of task completion to automatically probe participants about their affective, behavioral, and cognitive experiences. As a result of this system, information about affective, behavioral, and cognitive processes can be more easily investigated by researchers without computer programming knowledge. Corresponding apparatuses, systems, and methods are also discussed.

Systems and methods for cognitive training using voice command are described. One aspect includes a device to repeatedly present visual stimuli to a user that require performance of a task. A microphone may be positioned to provide audio input from the user to the device, with the audio input from the user providing input required to measure task performance. A processing system may perform real-time analysis of measured task performance.

Systems and methods for cognitive training using voice command are described. One aspect includes a device to repeatedly present visual stimuli to a user that require performance of a task. A microphone may be positioned to provide audio input from the user to the device, with the audio input from the user providing input required to measure task performance. A processing system may perform real-time analysis of measured task performance.

A method for predicting a test score of a user through an artificial intelligence model by a terminal, includes: delivering training data of the user to a first layer for embedding; embedding the training data through the first layer; delivering an embedding vector from the first layer to a second layer including a compressive transformer; delivering an output value from the second layer to a third layer for predicting the test score; and outputting a prediction value for predicting the test score from the third layer.

A method for predicting a test score of a user through an artificial intelligence model by a terminal, includes: delivering training data of the user to a first layer for embedding; embedding the training data through the first layer; delivering an embedding vector from the first layer to a second layer including a compressive transformer; delivering an output value from the second layer to a third layer for predicting the test score; and outputting a prediction value for predicting the test score from the third layer.