Systems and methods for analyzing learner's roles and performance and for intelligently adapting the delivery of education

Abstract

A computer-aided educational system and method to further a student's understanding of a subject matter through analyzing data captured in an electronic learning system so as to determine correlation data corresponding to variables or trends which are determined to enhance, optimize, and/or improve one's learning abilities or understanding of educational content. The system and method generates reports based on the correlation data, develops statistical models that highlight learning and behavioral trends, and/or provides recommendations for adapting the learning system based on the correlation data and statistical models.

1 . An electronic learning system comprising:
 a) a plurality of computing devices that communicate with a plurality of users in an educational community;   b) at least one server in communication with each of the plurality of computing devices, each server in communication with at least one data storage device configured to store information associated with at least one of e-learning environment data, organizational data and usage data,   c) at least one of the servers being in communication with at least one storage device that is configured to host at least one analytics engine, wherein the analytics engine is configured so as to analyze the at least one of e-learning environment data, organizational data, and usage data, and to generate at least one report on at least one of statistical trends or measureables.   
     
     
         2 . The system of  claim 1 , wherein the analytics engine determines at least one positive correlation data within an electronic learning system. 
     
     
         3 . The system of  claim 2 , wherein the at least one positive correlation data corresponds to at least one variable the enhances an educational experience for at least one of the plurality of users. 
     
     
         4 . The system of  claim 3 , wherein the at least one positive correlation data corresponds to factors relating to at least one of user demographic information, user behavioral characteristics, user learning preferences, user teaching preferences, educational delivery mechanisms. 
     
     
         5 . The system of  claim 1 , wherein the analytics engine determines at least one negative correlation data within an electronic learning system. 
     
     
         6 . The system of  claim 5 , wherein the at least one negative correlation data corresponds to at least one variable the acts as a detriment an educational experience for at least one of the plurality of users. 
     
     
         7 . The system of  claim 6 , wherein the at least one negative correlation data corresponds to factors relating to at least one of user demographic information, user behavioral characteristics, user learning preferences, user teaching preferences, educational delivery mechanisms. 
     
     
         8 . The system of  claim 1 , wherein the at least one generated report is in the form of at least one of: a mosaic plot, a heat diagram, a correlogram, a pie chart, a tree diagram, and a chart. 
     
     
         9 . The system of  claim 1 , wherein the at least one report identifies specific learning users that are performing at a level below a predetermined threshold. 
     
     
         10 . The system of  claim 1 , wherein the at least one report identifies subject matter in an educational curriculum which was not adequately understood by learning users. 
     
     
         11 . The system of  claim 1 , wherein the at least one report communicates the level of understanding of at least one of: a course, a subject matter, and an education curriculum. 
     
     
         12 . The system of  claim 1 , wherein the at least one report communicates the learning delivery mechanism to which at least one specific learning user responds better than other learning delivery mechanisms. 
     
     
         13 . An electronic learning system comprising:
 a) a plurality of computing devices that communicate with a plurality of users in an educational community;   b) at least one server in communication with each of the plurality of computing devices, each server in communication with at least one data storage device configured to store information associated with at least one of e-learning environment data, organizational data and usage data,   c) at least one of the servers being in communication with at least one storage device that is configured to host at least one analytics engine, wherein the analytics engine is configured so as to analyze the at least one of e-learning environment data, organizational data, and usage data, and to generate at least one recommendation for adapting a learning environment presented to at least one of the plurality of users.   
     
     
         14 . The system of  claim 11 , wherein the analytics engine determines at least one positive correlation data within an electronic learning system. 
     
     
         15 . The system of  claim 12 , wherein the at least one positive correlation data that corresponds to at least one variable the enhances an educational experience for at least one of the plurality of users. 
     
     
         16 . The system of  claim 13 , wherein the at least one positive correlation data corresponds to factors relating to at least one of user demographic information, user behavioral characteristics, user learning preferences, user teaching preferences, educational delivery mechanisms. 
     
     
         17 . The system of  claim 11 , wherein the analytics engine determines at least one negative correlation data within an electronic learning system. 
     
     
         18 . The system of  claim 17 , wherein the at least one negative correlation data corresponds to at least one variable the acts as a detriment an educational experience for at least one of the plurality of users. 
     
     
         19 . The system of  claim 18 , wherein the at least one negative correlation data corresponds to factors relating to at least one of user demographic information, user behavioral characteristics, user learning preferences, user teaching preferences, educational delivery mechanisms. 
     
     
         20 . The system of  claim 13 , wherein the analytics engine determines correlation data that identifies the interaction between at least two variables of the electronic learning system. 
     
     
         21 . The system of  claim 20 , wherein based on the correlation data, the analytics engine generates at least one recommendation corresponding to mechanisms for enhancing at least one of the plurality of user's interaction with the electronic learning system. 
     
     
         22 . The system of  claim 21 , wherein the at least one recommendation suggests at least one of: a different educational curriculum, additional reading, additional educational activities, and additional courses. 
     
     
         23 . The system of  claim 13 , wherein the analytics engine determines at least one of correlation data and trending models, and based on the at least one of correlation data and trending models, the analytics engine identifies a subset of the plurality of users and generates at least one recommendation for the specific subset of the plurality of users. 
     
     
         24 . The system of  claim 13 , wherein the analytics engine determines at least one of correlation data and trending models, and generates recommendation relating to an alternative educational curriculum for a specific user. 
     
     
         25 . The system of  claim 13 , wherein the analytics engine determines at least one of correlation data and trending models, and generates recommendation relating to an alternative educational delivery mechanism for a specific user. 
     
     
         26 . The system of  claim 13 , wherein the analytics engine determines at least one of correlation data and trending models, and generates recommendation relating to an alternative teaching system for a specific user. 
     
     
         27 . The system of  claim 13 , wherein the analytics engine determines at least one of correlation data and trending models, and generates recommendation relating to an enhancement for delivering educational content to a subset of the plurality of users. 
     
     
         28 . The system of  claim 27 , wherein the analytics engine recommends a grouping of learning users to an instructing user. 
     
     
         29 . The system of  claim 28 , wherein the grouping of learning users corresponds to a recommendation that groups a subset of learning users in such a way that enhances an educational experience for at least some of the learning users. 
     
     
         30 . The system of  claim 29 , wherein the grouping of users corresponds to a grouping that is determined based on correlation data that is used to match respective learning strengths and weaknesses among the subset of learning users. 
     
     
         31 . The system of  claim 29 , wherein the grouping of users relates to at least one of: a seating chart; a study group; a project group; a lab group; and a course enrollment. 
     
     
         32 . A method for analyzing information captured in an electronic learning system, the method comprising:
 a) identifying a plurality of users in an educational community;   b) providing a plurality of computing devices for communicating with the plurality of users in the educational community;   c) providing at least one server in communication with each of the plurality of computing devices, each server having at least one data storage devices coupled thereto and configured to store information associated with at least one of e-learning environment data, organizational data, and usage data, and at least one server being configured to host an analytics engine, where in the analytics engine is configured to analyze the at least one of e-learning environment data, organizational data, and usage data.   
     
     
         33 . The method of  claim 32 , wherein the analytics engine is further configured to generate at least one report on at least one of: at least one statistical trend and at least one measurable. 
     
     
         34 . The method of  claim 32 , wherein the analytics engine analyzes data and determines correlation data that corresponds to an interaction between at least two variables of an electronic learning system. 
     
     
         35 . The method of  claim 32 , wherein the analytics engine is further configured to generate at least one recommendation for adapting a learning environment presented to at least one of the plurality of users. 
     
     
         36 . The method of  claim 34 , wherein based on the correlation data, the analytics engine generates at least one recommendation corresponding to mechanisms for enhancing at least one of the plurality of user's interaction with an electronic learning system.