View source for 20240020781. ANALYTICAL PLATFORM FOR IMPROVING THE EDUCATION QUALITY simplified abstract (Deepspatial INC.)

=ANALYTICAL PLATFORM FOR IMPROVING THE EDUCATION QUALITY=


==Organization Name==

[[:Category:Deepspatial INC.|Deepspatial INC.]]


[[Category:Deepspatial INC.]]

==Inventor(s)==

[[:Category:Bushra Zaman of Bengaluru (IN)|Bushra Zaman of Bengaluru (IN)]][[Category:Bushra Zaman of Bengaluru (IN)]]

[[:Category:Rajiv Muradia of Toronto (CA)|Rajiv Muradia of Toronto (CA)]][[Category:Rajiv Muradia of Toronto (CA)]]

[[:Category:Rahul Kushwah of Toronto (CA)|Rahul Kushwah of Toronto (CA)]][[Category:Rahul Kushwah of Toronto (CA)]]

==ANALYTICAL PLATFORM FOR IMPROVING THE EDUCATION QUALITY - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240020781 titled 'ANALYTICAL PLATFORM FOR IMPROVING THE EDUCATION QUALITY

==Simplified Explanation==

The abstract describes a computer implemented analytical platform that aims to improve the quality of education. The platform collects and aggregates data from various sources, removes interrelations among variables, selects relevant variables, calculates their importance and weight, identifies gaps in school coverage, and applies machine learning algorithms to optimize set goals. It also includes an artificial intelligence module for predicting factors related to objectives and a recommendation module for predicting outcomes based on set goals.

* Data aggregation module collects and aggregates data from multiple sources.
* Statistical analysis module removes interrelations among variables.
* Dimensionality reduction module selects relevant variables from a given set.
* Feature engineering module calculates the importance and weight of each variable.
* Geospatial analytics module identifies gaps in school coverage.
* Analytical engine implements machine learning algorithms trained using a training and test dataset.
* Artificial intelligence module predicts factors related to set objectives.
* Recommendation module predicts outcomes based on set goals.

Potential applications of this technology:
* Education system improvement and optimization.
* Personalized learning and adaptive education.
* Early identification of at-risk students.
* Resource allocation and planning in education.

Problems solved by this technology:
* Lack of comprehensive data analysis in education.
* Difficulty in identifying relevant variables and their importance.
* Inefficient resource allocation and planning.
* Limited ability to predict outcomes and factors related to objectives.

Benefits of this technology:
* Improved quality and effectiveness of education.
* Enhanced decision-making and resource allocation.
* Personalized and adaptive learning experiences.
* Early intervention and support for at-risk students.


==Original Abstract Submitted==

a computer implemented analytical platform for improving the quality of education, the computer implemented analytical platform comprises a data aggregation module configured to collect and aggregate data from one or more data source. the computer implemented analytical platform includes a statistical analysis module for removing the interrelation among one or more variables. the results are passed to a dimensionality reduction module to select one or more variables from a given set of variables. after a selection is made on the number of dimensions/variables to be included for analysis, the data is passed to a feature engineering module, which calculates the importance of each feature/variable and the weight associated with each variable/dimension. a geospatial analytics module determines the gaps in coverage of schools. subsequently, the data is passed to an analytical engine implementing machine learning algorithms, which are trained using a training and test dataset, the test dataset comprising one or more variables selected by the feature engineering module to optimize the set goals. the machine learning model is tested on the test dataset. an artificial intelligence module is used for prediction of factors for set objectives and a recommendation module is used for prediction of the outcomes based on the set goals.



[[Category:G06Q50/20]]