A method of automatically evolving survey questions includes receiving, at a processor, a first set of textual responses to at least one open-ended question in a survey, reformatting the first set of textual responses as a set of possible textual responses in the form of closed-ended questions with numerical ratings as options, storing the set of possible textual responses in a memory in communication with the processor, selecting and transmitting a subset of the set of possible textual response to survey recipients, receiving, at the processor, participant numerical ratings for each textual response, using the processor to generate a confidence measure and score for each possible textual response based upon the participant ratings, generating a ranked list, with the processor, including the confidence measures, storing the ranked list, and converting at least one of the original open-ended question in the survey to a closed-ended ended question, using a subset of the textual responses.

A method of automatically evolving survey questions includes receiving, at a processor, a first set of textual responses to at least one open-ended question in a survey, reformatting the first set of textual responses as a set of possible textual responses in the form of closed-ended questions with numerical ratings as options, storing the set of possible textual responses in a memory in communication with the processor, selecting and transmitting a subset of the set of possible textual response to survey recipients, receiving, at the processor, participant numerical ratings for each textual response, using the processor to generate a confidence measure and score for each possible textual response based upon the participant ratings, generating a ranked list, with the processor, including the confidence measures, storing the ranked list, and converting at least one of the original open-ended question in the survey to a closed-ended ended question, using a subset of the textual responses.

Techniques of randomized testing of massively open online courses (MOOCs) involve generating independent A/B tests on the plurality of individual sections of a MOOC. Along these lines, a MOOC may have many learning modules, with many students enrolled in the MOOC. A course instructor may wish to experiment with different variations of course content in order to discover whether any such variations may improve the MOOC. Rather than perform a single A/B test during the MOOC to obtain results for which the course instructor would have to wait weeks, the instructor submits variations of various individual learning modules of the MOOC to a A/B testing server. The A/B testing server may then assign students in each lecture to different versions of a learning module. The A/B testing server may also evaluate the results of the testing in order to provide a recommendation about the MOOC as a whole.

A computer implemented method for significantly increasing the efficacy of computer adaptive learning (which currently measures and acts on a learner's performance in the cognitive or objective learning domain of education) by additionally detecting and identifying the learner's emotional response data and together with the cognitive data, optimizing the educational content being presented. These emotional responses constitute the affective or emotional domain of education.

A computer implemented method for significantly increasing the efficacy of computer adaptive learning (which currently measures and acts on a learner's performance in the cognitive or objective learning domain of education) by additionally detecting and identifying the learner's emotional response data and together with the cognitive data, optimizing the educational content being presented. These emotional responses constitute the affective or emotional domain of education.

The present invention relates to the design and development of a learning game platform, system and method that is implemented in an electronic device to promote autonomous work and active learning, integrating all students, with self and peer assessment, supported by instructional videos that can be used for any subject/course implemented on electronic devices for the teaching and learning of mathematics, languages, physics or any other subject. Students can use this in the classroom or outside the classroom in an online, blended or flipped learning setup to study and solve problems, combining analog input and digital outputs.

The innovative part of this platform is that it integrates: analog input; a gamification scheme; videos; levels of problems and levels of instructional videos, organized in a video-book; digital-portfolio; self-assessment and peer-assessment.

Different difficulty levels of problems and of video instructional resolutions, self and peer-assessment is implemented to include all students and promote autonomous and active learning in a gamified platform to motivate students.

The application of the present invention can be used for online, mobile, blended and flipped learning. Teachers can follow what students are doing in another back office application, which is also part of the system of the present invention.

The present invention can be implemented also on electronic devices such as smartphones, tablets, etc. allowing students to use this it in the classroom or outside the classroom in a blended learning model to solve problems.

The learning gamified platform, system and method, for an electronic device executing an application, comprises a server for a reading medium and storing user information, scores and user progress and e-portfolio within a play and learn app platform. The server communicates through the Internet with electronic devices having Internet access operated by at least one player. Registration means allows the player to register with and log on to the play and learn app. Also the app allows the teachers to follow the performance of the players and to improve some aspects of the teaching program in a given classroom.

The present invention relates to the design and development of a learning game platform, system and method that is implemented in an electronic device to promote autonomous work and active learning, integrating all students, with self and peer assessment, supported by instructional videos that can be used for any subject/course implemented on electronic devices for the teaching and learning of mathematics, languages, physics or any other subject. Students can use this in the classroom or outside the classroom in an online, blended or flipped learning setup to study and solve problems, combining analog input and digital outputs.

The innovative part of this platform is that it integrates: analog input; a gamification scheme; videos; levels of problems and levels of instructional videos, organized in a video-book; digital-portfolio; self-assessment and peer-assessment.

Different difficulty levels of problems and of video instructional resolutions, self and peer-assessment is implemented to include all students and promote autonomous and active learning in a gamified platform to motivate students.

The application of the present invention can be used for online, mobile, blended and flipped learning. Teachers can follow what students are doing in another back office application, which is also part of the system of the present invention.

The present invention can be implemented also on electronic devices such as smartphones, tablets, etc. allowing students to use this it in the classroom or outside the classroom in a blended learning model to solve problems.

The learning gamified platform, system and method, for an electronic device executing an application, comprises a server for a reading medium and storing user information, scores and user progress and e-portfolio within a play and learn app platform. The server communicates through the Internet with electronic devices having Internet access operated by at least one player. Registration means allows the player to register with and log on to the play and learn app. Also the app allows the teachers to follow the performance of the players and to improve some aspects of the teaching program in a given classroom.

A data collection, display, and report generation platform has a first input interface configured to present a learning module comprising a series of questions and answers on a plurality of successive question/answer screens comprising a plurality of radio buttons, at least one of the radio buttons configured to accept both a first input action and a subsequent second input action, each providing a different visual indication. The first and second input action each indicate a different confidence level of a learner's answer. A display dashboard displays a plurality of data visualizations of metrics of misinformation and struggle of plurality of learners based on a plurality of answers collected through the first input interface, and comprises one or more bar graph displays, one or more heat map displays, and one or more sorting tools configured to alter the one or more bar graph displays or one or more heat map displays.

A data collection, display, and report generation platform has a first input interface configured to present a learning module comprising a series of questions and answers on a plurality of successive question/answer screens comprising a plurality of radio buttons, at least one of the radio buttons configured to accept both a first input action and a subsequent second input action, each providing a different visual indication. The first and second input action each indicate a different confidence level of a learner's answer. A display dashboard displays a plurality of data visualizations of metrics of misinformation and struggle of plurality of learners based on a plurality of answers collected through the first input interface, and comprises one or more bar graph displays, one or more heat map displays, and one or more sorting tools configured to alter the one or more bar graph displays or one or more heat map displays.

Textual indicators are extracted from an electronic document. A rule for monitoring information technology user behavior is generated from the extracted textual indicators.