WO2023152556A1

WO2023152556A1 - System and method for monitoring and controlling educational growth

Info

Publication number: WO2023152556A1
Application number: PCT/IB2022/053204
Authority: WO
Inventors: Kumkumaveluswamy M; Arshiya Sultana
Original assignee: Kumkumaveluswamy M; Arshiya Sultana
Priority date: 2022-02-08
Filing date: 2022-04-06
Publication date: 2023-08-17

Abstract

A system for monitoring and controlling educational growth is disclosed. The system includes a processing subsystem which includes an input module (40) which receives preference(s) corresponding to the educational growth, from entities upon registration. The processing subsystem also includes a data collection module (50) which collects background data corresponding to the learner(s). The processing subsystem also includes a performance monitoring module (60) which collects multiple results corresponding to multiple activities associated with the learner(s) and monitors a current performance of the learner(s). The processing subsystem also includes a prediction module (70) which trains a prediction model with multiple datasets corresponding to the learner(s) and predicts a future performance of the learner(s). The processing subsystem also includes a recommendation module (80) which generates recommendation(s) corresponding to a preferred learning path to be followed by the learner(s) to match the future performance predicted by the prediction module (70), thereby monitoring and controlling the educational growth of the learner(s).

Description

SYSTEM AND METHOD FOR MONITORING AND CONTROLLING EDUCATIONAL GROWTH

EARLIEST PRIORITY DATE

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202241006764, filed on February 08, 2022, and titled “SYSTEM AND METHOD FOR MONITORING AND CONTROLLING EDUCATIONAL GROWTH”.

FIELD OF INVENTION

Embodiments of a present disclosure relate to the education system, and more particularly to a system and method for monitoring and controlling educational growth.

BACKGROUND

Education is referred to as a process of assisting learning, or the acquisition of information, skills, values, morals, beliefs, habits, and personal growth. Although most education takes place under the supervision of instructors, learners can also educate themselves. Formal education takes place in a controlled atmosphere with the avowed goal of instructing students. Formal education usually takes place in a school setting, with several pupils learning in a classroom with a subject-trained, licensed instructor. Most school systems are built on a set of beliefs or ideas that guide all educational decisions. Curriculum, organizational structures, physical learning environments (e.g., classrooms), student-teacher relationships, assessment systems, class size, educational activities, and more are all options.

For those living in impoverished areas and developing nations, technology is becoming increasingly important in facilitating access to education. However, in poor nations, a lack of technical innovation continues to obstruct educational quality and access. During the epidemic, many schools devised alternate plans, including in- person, hybrid, and online-only classes, posing obstacles for many students, instructors, and families, including those with learning impairments and those studying in a language other than their mother tongue. In both the formal education way and the alternative way, the educational growth of the students is important. In order to improvise the educational growth of the learners, there are multiple approaches being implemented. One such approach includes a system and method of virtual schooling through artificial intelligence and predictive analysis. However, such an approach performs the predictive analysis through facial recognition, and results obtained based on the facial recognition may be less accurate, thereby making such as approach less reliable and less efficient.

Hence, there is a need for an improved system and method for monitoring and controlling educational growth which addresses the aforementioned issues.

BRIEF DESCRIPTION

In accordance with one embodiment of the disclosure, a system for monitoring and controlling educational growth is provided. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an input module. The input module is configured to receive one or more preferences corresponding to the educational growth, from one or more entities upon registration. The one or more entities may include one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, and one or more learners. The processing subsystem also includes a data collection module operatively coupled to the input module. The data collection module is configured to collect background data corresponding to the one or more learners based on the one or more preferences. The background data corresponds to one or more fields including at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners. Further, the processing subsystem also includes a performance monitoring module operatively coupled to the data collection module. The performance monitoring module is configured to collect a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data. The performance monitoring module is also configured to monitor a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results. Furthermore, the processing subsystem also includes a prediction module operatively coupled to the performance monitoring module. The prediction module is configured to train a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning techniques, upon monitoring the current performance. The plurality of datasets includes at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria. The prediction module is also configured to predict a future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners. Furthermore, the processing subsystem also includes a recommendation module operatively coupled to the prediction module. The recommendation module is configured to generate one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module, thereby monitoring and controlling the educational growth of the one or more learners.

In accordance with another embodiment, a method for monitoring and controlling educational growth is provided. The method includes receiving one or more preferences corresponding to the educational growth, from one or more entities upon registration, wherein the one or more entities includes one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, and one or more learners. The method also includes collecting background data corresponding to the one or more learners based on the one or more preferences, wherein the background data corresponds to one or more fields including at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners. Further, the method also includes collecting a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data. Furthermore, the method also includes monitoring a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results. Furthermore, the method also includes training a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning techniques, upon monitoring the current performance, wherein the plurality of datasets includes at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria. Furthermore, the method also includes predicting a future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners. Furthermore, the method also includes generating one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module, thereby monitoring and controlling the educational growth of the one or more learners.

To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram representation of a system for monitoring and controlling educational growth in accordance with an embodiment of the present disclosure;

FIG. 2 is a block diagram representation of an exemplary embodiment of the system for monitoring and controlling educational growth of FIG. 1 in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram of a monitoring and controlling computer or a monitoring and controlling server in accordance with an embodiment of the present disclosure; and

FIG. 4 is a flow chart representing steps involved in a method for monitoring and controlling educational growth in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. Embodiments of the present disclosure relate to a system for monitoring and controlling educational growth. As used herein, the term “educational growth” refers to an ongoing succession of educative experiences that shape and develop a person. Further, monitoring and controlling the educational growth is important to make sure that the educational growth is happening properly and moving in a current direction. Thus, the system described hereafter in FIG. 1 is the system for monitoring and controlling the educational growth.

FIG. 1 is a block diagram representation of a system (10) for monitoring and controlling educational growth in accordance with an embodiment of the present disclosure. The system (10) includes a processing subsystem (20) hosted on a server (30). In one embodiment, the server (30) may include a cloud server. In another embodiment, the server (30) may include a local server. The processing subsystem (20) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as a local area network (FAN). In another embodiment, the network may include a wireless network such as wireless fidelity (Wi-Fi), Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID), or the like.

Basically, one or more entities may be a part of an education system, and for the system (10) to be able to be monitoring and controlling the educational growth, the system (10) may receive certain inputs from the corresponding one or more entities. Therefore, the processing subsystem (20) includes an input module (40). The input module (40) is configured to receive one or more preferences corresponding to the educational growth, from the one or more entities upon registration. The one or more entities may include one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, one or more learners, and the like. In one exemplary embodiment, the one or more educational institutions may include a school, a college, a training institution, or the like. The one or more knowledge distributors may correspond to a teacher, a trainer, a tutor, or the like. The one or more guardians may correspond to parents, relatives, caretakers, or the like. The one or more drivers may correspond to a school vehicle driver, a personal vehicle driver, a family vehicle driver, or the like. The one or more learners may correspond to students, pupils, employees, trainees, or the like.

Further, in one embodiment, the one or more preferences may include at least one of one or more educational institution-related preferences, one or more knowledge distributer-related preferences, one or more guardian-related preferences, one or more driver-related preferences, and one or more learner-related preferences. In one exemplary embodiment, the one or more educational institution-related preferences may include adding, removing, or modifying one or more classes, one or more sections, one or more events, one or more knowledge distributers, a count of the one or more knowledge distributers, or the like. The one or more knowledge distributer- related preferences may include adding, removing, or modifying one or more learners, homework, one or more documents, one or more pictures, a progress report, a timetable, uploading of one or more question papers, scheduling online classes, create a group chat with the one or more learners, or the like. The one or more guardian- related preferences may include being able to add and remove children, view learner schedule, view one or more progress reports, view driver schedule, view driver profile, or the like. The one or more driver-related preferences may include being able to update a pick-and-drop status of the one or more learners, add or remove the one or more learners, or the like. The one or more learner-related preferences may include being able to view the timetable, view the one or more progress reports, view the homework, upload the homework, upload one or more documents, upload one or more pictures, upload one or more answers to one or more questions, or the like. Therefore, all of the one or more preferences received from the one or more entities are having an impact on the educational growth of the one or more learners.

Prior to receiving the one or more preferences, the one or more entities may have to be registered. Therefore, in an embodiment, the processing subsystem (20) may also include a registration module (as shown in FIG. 2) operatively coupled to the input module (40). The registration module is configured to register the one or more entities with the system (10) upon receiving the plurality of details via an entity device. In one embodiment, the plurality of details may be stored in a database (as shown in FIG. 2) of the system (10). In one exemplary embodiment, the database may include a local database or a cloud database. In one exemplary embodiment, the plurality of details may include an entity name, entity contact details, qualification details, education details, or the like. The entity contact details may include an entity contact number, an entity e-mail identity, or the like. Also, in an embodiment, the entity device may include a mobile phone, a tablet, a laptop, or the like.

Upon registration, an authenticity of each of the one or more entities may have to be verified. Therefore, in an embodiment, the processing subsystem (20) may also include a verification module (as shown in FIG. 2) operatively coupled to the registration module. The verification module is configured to verify the authenticity of the one or more entities using a predefined verification technique upon registration. In one embodiment, the predefined verification technique may correspond to verifying the authenticity of the one or more entities by generating one-time password (OTP) for the one or more entities to enter along with the entity name or the entity contact details as login identity and auto-generated password. Then a unique entity identifier may be generated to be used as login credentials in the future. Further, upon verification, for the one or more entities to be able to use the system (10), the one or more entities may have to purchase one or more subscription plans. Therefore, in an embodiment, the processing subsystem (20) may also include a subscription module (as shown in FIG. 2) operatively coupled to the verification module. The subscription module may be configured to generate a subscription confirmation notification upon receiving a corresponding payment for the corresponding one or more subscription plans selected by the corresponding one or more entities. In one embodiment, the one or more subscription plans may include a one-month plan, a six-month plan, a twelvemonth plan, or the like. In an embodiment, the verification and the subscription may be considered as a part of a process of the registration.

Later, upon receiving the one or more preferences, the system (10) may need to know background details corresponding to the one or more learners, in order for the system (10) to take a certain future decision on a proper basis. Therefore, the processing subsystem (20) also includes a data collection module (50) operatively coupled to the input module (40). The data collection module (50) is configured to collect background data corresponding to the one or more learners based on the one or more preferences. The background data corresponds to one or more fields including at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners. In one exemplary embodiment, the background data may include at least one of past academic performance, financial status, family income, family expenditure, past extracurricular performance, past physical health conditions, past mental health conditions, and the like.

Furthermore, a real-time performance of the one or more learners may also have to be monitored. Therefore, the processing subsystem (20) also includes a performance monitoring module (60) operatively coupled to the data collection module (50). The performance monitoring module (60) is configured to collect a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data. The performance monitoring module (60) is also configured to monitor a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results. In one embodiment, the plurality of activities may include at least one of one or more tests, one or more extracurricular activities, one or more assignments, a finance-related activity, one or more regular academic submissions, and the like. In one exemplary embodiment, the plurality of results may correspond to performed well, performed poor, received medals, didn’t receive any medal, completed the one or more assignments or the one or more regular academic submissions or didn’t complete, applied for a scholarship and received the same on merit basis or didn’t receive, or the like.

Moreover, in an embodiment, the current performance may correspond to a good performance or a better performance, when the current trend of the plurality of results may be increasing or better than the background trend of the background data. In another embodiment, the current performance may correspond to a bad performance or a poor performance, when the current trend of the plurality of results may be decreasing, constant, or poor than the background trend of the background data.

Upon monitoring the current performance of the one or more learners, a future performance of the one or more learners may be predicted. Therefore, the processing subsystem (20) also includes a prediction module (70) operatively coupled to the performance monitoring module (60). The prediction module (70) is configured to train a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning (ML) techniques, upon monitoring the current performance. The plurality of datasets includes at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria. In one exemplary embodiment, the predefined prediction criteria may correspond to a plurality of probable predictions, a plurality of similar case study results, and the like. The prediction module (70) is also configured to predict the future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners.

As used herein, the term “machine learning” is defined as an application of artificial intelligence (Al) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. In one embodiment, the one or more machine learning techniques may include at least one of a k-means clustering technique, a support vector machine (SVM)-based technique, and the like. The k- means clustering technique is one of the simplest unsupervised learning techniques, that solve a well-known clustering problem. The procedure follows a simple and easy way to classify a given dataset through a certain number of clusters (assume k clusters) fixed theoretically. The main idea is to define k centers, one for each cluster. These centers should be placed cunningly because different locations cause different results. So, the scheme, and that the sparse configuration and rank one significantly improve the performance of the recommendation. A better choice is to place them as much as possible far away from each other. The next step is to take each point belonging to a given dataset and associate it to the nearest center. When no point is pending, the first step is completed, and an early group age is done. At this point, new centroids may be re-calculated as the barycenter of the clusters resulting from the previous step. Once k new centroids are obtained, a new binding must be done between the same dataset points and the nearest new center. A loop has been generated. As a result of this loop, it can be noticed that the k centers change their location step by step until no more changes are done or in other words, centers do not move anymore. Finally, this technique aims at minimizing an objective function known as squared error function given by:

Further, in data analytics or decision sciences, most of the time, there may be a necessity to classify data based on a certain dependent variable. To support the solution for this need there are multiple techniques which can be applied such as Logistic Regression, Random Forest Algorithm, Bayesian Algorithm, and the like. The SVM-based technique is an ML technique to separate data which tries to maximize a gap between categories. Furthermore, a performance of the SVM-based technique and the k-means clustering technique may improve as a dataset size grows.

Upon predicting the future performance, the system (10) may try to make sure that the actual future performance of the one or more learners matches with the future performance predicted via the prediction module (70). Therefore, the processing subsystem (20) also includes a recommendation module (80) operatively coupled to the prediction module (70). The recommendation module (80) is configured to generate one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module (70), thereby monitoring and controlling the educational growth of the one or more learners. In one embodiment, the preference learning path may correspond to one or more courses, one or more training, or the like that the one or more learners may have to take to match with the future performance predicted by the prediction module (70).

In one exemplary embodiment, the processing subsystem (20) may also include a tracking module (as shown in FIG. 2) operatively coupled to the input module (40). The tracking module may be configured to track a path of the one or more learners when the one or more drivers pick up the one or more learners between a source location and a destination location using a predefined tracking technique. In one embodiment, the source location and the destination location may be a home location, a school location, a college location, or the like. Also, in an embodiment, the predefined tracking technique may include a Global Positioning System (GPS) tracker for tracking the path of the one or more learners. The tracking module may also be configured to update a pick-and-drop status of the one or more learners to the one or more guardians based on the tracking of the path of the one or more learners.

Additionally, in an embodiment, the processing subsystem (20) may also include a report generation module (as shown in FIG. 2) operatively coupled to the prediction module (70). The report generation module may be configured to generate one or more progress reports corresponding to the current performance and the future performance of the one or more learners, upon receiving a report generation request from the one or more entities. At any stage during the process of monitoring and controlling the educational growth of the one or more learners, the one or more entities may be willing to have a look at the one or more progress reports corresponding to the one or more learners. Therefore, during that time, the report generation module may receive the report generation request from the corresponding one or more entities and may generate the one or more progress reports to be shared with the corresponding one or more entities.

FIG. 2 is a block diagram representation of an exemplary embodiment of the system (10) for monitoring and controlling the educational growth of FIG. 1 in accordance with an embodiment of the present disclosure. The system (10) includes the processing subsystem (20) hosted on the server (30). Suppose school authorities (85) of a school ‘X’ (90) are willing to use the system (10) for monitoring and controlling the educational growth of students (100) at the school ‘X’ (90). Therefore, the school ‘X’ (90) registers with the system (10) via the registration module (110) upon receiving a plurality of school details via a school laptop (120). The plurality of school details is stored in the database (130) of the system (10). Further, the school ‘X’ (90) allows schoolteachers (140), the students (100), school bus drivers (150), and parents (160) of the corresponding students (100) to register with the system (10) via the registration module (110) using a schoolteacher mobile phone (170), a student device (180), a school bus driver mobile phone (190), and a parents mobile phone (200) respectively. Upon registration, the authenticity of each is also verified via the verification module (210). Upon verification, each may choose an appropriate subscription plan by making the corresponding payment and receive the subscription confirmation notification via the subscription module (220).

Later, the one or more preferences of each corresponding to the educational growth are received via the input module (40). Then, the background data of the students (100) is also collected via the data collection module (50), which will help the system (10) to make future predictions. Further, the students (100) may be attending certain class tests, participating in certain indoor and outdoor games, and the like. So, the plurality of results of such class tests, such indoor and outdoor games, and the like, are also collected and the current performance of the students (100) is monitored via the performance monitoring module (60). By monitoring the current performance, a subject, a game, a skill, or the like which the corresponding students (100) are good at is identified. Then, based on the current performance monitored, the future performance of the students (100) is predicted via the prediction module (70). For example, suppose some of the students (100) are found to be good at math subject, then the prediction could be that such students (100) could excel in being an engineer, a mathematician, a math teacher, or the like. Then, the one or more recommendations corresponding to the preferred learning path are generated for the students (100) via the recommendation module (80). For example, the students (100) who could excel in being the engineer, the mathematician, the math teacher, or the like, could be recommended to take an engineering course, do a Ph.D. in Mathematics, take a teaching course in math, or the like respectively.

Further, as the students (100) travel from home (225) to the school ‘X’ (90) via a school bus and back, the corresponding parents (160) may be worried about the safety of the students (100). Therefore, the path of the students (100) is also tracked via the tracking module (230). Moreover, the one or more progress reports are also generated via the report generation module (235) when the parents request for the same.

FIG. 3 is a block diagram of a monitoring and controlling computer or a monitoring and controlling server (240) in accordance with an embodiment of the present disclosure. The monitoring and controlling server (240) includes processor(s) (250), and memory (260) operatively coupled to a bus (270). The processor(s) (250), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (250).

The memory (260) includes a plurality of subsystems stored in the form of executable program which instructs the processor(s) (250) to perform method steps illustrated in FIG. 4. The memory (260) includes a processing subsystem (20) of FIG 1. The processing subsystem (20) further has following modules: an input module (40), a data collection module (50), a performance monitoring module (60), a prediction module (70), and a recommendation module (80).

The input module (40) is configured to receive one or more preferences corresponding to the educational growth, from one or more entities upon registration, wherein the one or more entities comprises one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, and one or more learners.

The data collection module (50) is configured to collect background data corresponding to the one or more learners based on the one or more preferences, wherein the background data corresponds to one or more fields including at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners.

The performance monitoring module (60) is configured to collect a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data. The performance monitoring module (60) is also configured to monitor a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results.

The prediction module (70) is configured to train a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning techniques, upon monitoring the current performance, wherein the plurality of datasets includes at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria. The prediction module (70) is also configured to predict a future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners.

The recommendation module (80) is configured to generate one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module (70), thereby monitoring and controlling the educational growth of the one or more learners.

The bus (270) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (270) includes a serial bus or a parallel bus, wherein the serial bus transmits data in a bit-serial format and the parallel bus transmits data across multiple wires. The bus (270) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.

FIG. 4 is a flow chart representing steps involved in a method (280) for monitoring and controlling educational growth in accordance with an embodiment of the present disclosure. The method (280) includes receiving one or more preferences corresponding to the educational growth, from one or more entities upon registration, wherein the one or more entities includes one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, and one or more learners in step 290. In one embodiment, receiving one or more preferences may include receiving one or more preferences by an input module (40).

The method (280) also includes collecting background data corresponding to the one or more learners based on the one or more preferences, wherein the background data corresponds to one or more fields including at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners in step 300. In one embodiment, collecting the background data may include collecting the background data by a data collection module (50). Furthermore, the method (280) includes collecting a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data in step 310. In one embodiment, collecting the plurality of results may include collecting the plurality of results by a performance monitoring module (60).

Furthermore, the method (280) also includes monitoring a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results in step 320. In one embodiment, monitoring the current performance may include monitoring the current performance by the performance monitoring module (60).

Furthermore, the method (280) also includes training a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning techniques, upon monitoring the current performance, wherein the plurality of datasets includes at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria in step 330. In one embodiment, training the prediction model may include training the prediction model by a prediction module (70).

Furthermore, the method (280) also includes predicting a future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners in step 340. In one embodiment, predicting the future performance may include predicting the future performance by the prediction module (70).

Furthermore, the method (280) also includes generating one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module, thereby monitoring and controlling the educational growth of the one or more learners in step 350. In one embodiment, generating the one or more recommendations may include generating the one or more recommendations by a recommendation module (80).

In one exemplary embodiment, the method (280) may further include tracking a path of the one or more learners when the one or more drivers pick up the one or more learners between a source location and a destination location using a predefined tracking technique. In such embodiment tracking the path of the one or more learners may include tracking the path of the one or more learners by a tracking module.

Further, in one exemplary embodiment, the method (280) may also include updating a pick-and-drop status of the one or more learners to the one or more guardians based on the tracking of the path of the one or more learners. In such embodiment, updating the pick-and-drop status of the one or more learners may include updating the pick- and-drop status of the one or more learners by the tracking module.

Further, from a technical effect point of view, the implementation time required to perform the method steps included in the present disclosure by the one or more processors of the system is very minimal, thereby the system maintains very minimal operational latency and requires very minimal processing requirements.

Various embodiments of the present disclosure enable monitoring and controlling the educational growth of the one or more learners more accurately it is done by predicting the future performance based on the background data and the current performance of the corresponding one or more learners. Also, generating recommendations corresponding to the preferred learning path that the students should choose to have a better future performance makes the system more advantageous, as the system assists the parents and the students to take the right decisions in carrier selection.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

WE CLAIM:

1. A system (10) for monitoring and controlling educational growth comprising: a processing subsystem (20) hosted on a server (30), and configured to execute on a network to control bidirectional communications among a plurality of modules comprising: an input module (40) configured to receive one or more preferences corresponding to the educational growth, from one or more entities upon registration, wherein the one or more entities comprises one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, and one or more learners; a data collection module (50) operatively coupled to the input module (40), wherein the data collection module (50) is configured to collect background data corresponding to the one or more learners based on the one or more preferences, wherein the background data corresponds to one or more fields comprising at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners; and a performance monitoring module (60) operatively coupled to the data collection module (50), wherein the performance monitoring module (60) is configured to: collect a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data; and monitor a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results; a prediction module (70) operatively coupled to the performance monitoring module (60), wherein the prediction module (70) is configured to: train a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning techniques, upon monitoring the current performance, wherein the plurality of datasets comprises at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria; and predict a future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners; and a recommendation module (80) operatively coupled to the prediction module (70), wherein the recommendation module (80) is configured to generate one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module (70), thereby monitoring and controlling the educational growth of the one or more learners.

2. The system (10) as claimed in claim 1, wherein the one or more preferences comprises at least one of one or more educational institution-related preferences, one or more knowledge distributer-related preferences, one or more guardian- related preferences, one or more driver-related preferences, and one or more learner-related preferences.

3. The system (10) as claimed in claim 1, wherein the background data comprises at least one of past academic performance, financial status, family income, family expenditure, past extracurricular performance, past physical health conditions, and past mental health conditions.

4. The system (10) as claimed in claim 1, wherein the plurality of activities comprises at least one of one or more tests, one or more extracurricular activities, one or more assignments, a finance-related activity, and one or more regular academic submissions.

5. The system (10) as claimed in claim 1, wherein the one or more machine learning techniques comprises at least one of a k-means clustering technique and a support vector machine-based technique.

6. The system (10) as claimed in claim 1, wherein the processing subsystem (20) comprises a tracking module (230) operatively coupled to the input module (40), wherein the tracking module (230) is configured to: track a path of the one or more learners when the one or more drivers pick up the one or more learners between a source location and a destination location using a predefined tracking technique; and update a pick-and-drop status of the one or more learners to the one or more guardians based on the tracking of the path of the one or more learners.

7. The system (10) as claimed in claim 1, wherein the processing subsystem (20) comprises a report generation module (235) operatively coupled to the prediction module (70), wherein the report generation module (235) is configured to generate one or more progress reports corresponding to the current performance and the future performance of the one or more learners, upon receiving a report generation request from the one or more entities.

8. A method (280) for analysis and management of educational growth comprising: receiving, by an input module (40), one or more preferences corresponding to the educational growth, from one or more entities upon registration, wherein the one or more entities comprises one or more educational institutions, one or more knowledge distributers, one or more guardians, one or more drivers, and one or more learners; (290) collecting, by a data collection module (50), background data corresponding to the one or more learners based on the one or more preferences, wherein the background data corresponds to one or more fields comprising at least one of education, finance, family, extracurricular, and physical-mental health of the one or more learners; (300) collecting, by a performance monitoring module (60), a plurality of results corresponding to a plurality of activities associated with the one or more learners in real-time upon collecting the background data; (310) monitoring, by the performance monitoring module (60), a current performance of the one or more learners by analyzing and comparing a background trend of the background data and a current trend of the plurality of results; (320) training, by a prediction module (70), a prediction model in real-time with a plurality of datasets corresponding to the one or more learners using one or more machine learning techniques, upon monitoring the current performance, wherein the plurality of datasets comprises at least one of the one or more preferences, the background data, the plurality of results, the current performance, and predefined prediction criteria; (330) predicting, by the prediction module (70), a future performance of the one or more learners using the prediction model, based on analysis of a performance trend of the current performance of the one or more learners; and (340) generating, by a recommendation module (80), one or more recommendations corresponding to a preferred learning path to be followed by the one or more learners to match the future performance predicted by the prediction module, thereby monitoring and controlling the educational growth of the one or more learners (350).

9. The method (280) as claimed in claim 8, comprises tracking, by a tracking module (230), a path of the one or more learners when the one or more drivers pick up the one or more learners between a source location and a destination location using a predefined tracking technique.

10. The method (280) as claimed in claim 9, comprises updating, by the tracking module (230), a pick-and-drop status of the one or more learners to the one or more guardians based on the tracking of the path of the one or more learners.