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ENHANCING EDUCATION WITH MACHINE LEARNING AND IT PROFICIENCY STRATEGIES FOR BUILDING SMART AND ADAPTIVE LEARNING ENVIRONMENTS

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ENHANCING EDUCATION WITH MACHINE LEARNING AND IT PROFICIENCY STRATEGIES FOR BUILDING SMART AND ADAPTIVE LEARNING ENVIRONMENTS

ORDINARY APPLICATION

Published

date

Filed on 9 November 2024

Abstract

Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments is the proposed invention. The proposed invention focuses on understanding the functions of Enhancing Education with IT Proficiency Strategies. The invention focuses on analyzing the parameters of Smart and Adaptive Learning Environments using algorithms of Machine Learning.

Patent Information

Application ID202441086563
Invention FieldCOMPUTER SCIENCE
Date of Application09/11/2024
Publication Number46/2024

Inventors

NameAddressCountryNationality
Dr J PrakashAssociate Professor, Department of Information Technology, Hindusthan Institute of Technology, Coimbatore- 641 032IndiaIndia
Dr Sureshkumar Markanday SubramanianAssociate Professor, Department of Chemistry, ACE Engineering College, Ankushapur, Hyderabad- 501301IndiaIndia
Dr A. Ajmal KhaanAssociate Professor of English, Deputy Controller of Examinations, Jamal Mohamed College, Trichy- 620020IndiaIndia
Arnika KabraAssistant Professor, Department of Management, Shri Vaishnav Institute of Management and Science, Indore- 452009IndiaIndia
Sadashiv S. TavashiAnantrao Pawar College of Engineering and Research Parvati, PuneIndiaIndia
Dr. Manasi Vyankatesh GhamandeAssistant Professor, DESH, Vishwakarma Institute of Technology, PuneIndiaIndia
Dr Anjum Ayyaj PatelHead of Department Computer Science Vishwakarna College of ACS, Pune- 411048IndiaIndia
Dr Jyoti Prasad PatraPrincipal Nigam Institute of Engineering and Technology Niet UG PG Diploma Engineering, Odisha, India- 754006IndiaIndia
Dr G. PrabhakaranProfessor & Dean for Student Welfare, Department of Civil Engineering, Siddharth Institute of Engineering & Technology, Puttur- 517583IndiaIndia
Dr Sweta PriyaAssociate Professor, Amity School of Communication, Amity University, Patna- 801503IndiaIndia
Dr Nidhi TripathiAssistant Professor, Department of Zoology, Swami Shukdevanand College, Shahjahanpur, Uttar PradeshIndiaIndia
Deepa EAssistant Professor, Department of Economics,The Zamorins Guruvayurappan College, Calicut- 673014IndiaIndia

Applicants

NameAddressCountryNationality
Dr J PrakashAssociate Professor, Department of Information Technology, Hindusthan Institute of Technology, Coimbatore- 641 032IndiaIndia
Dr Sureshkumar Markanday SubramanianAssociate Professor, Department of Chemistry, ACE Engineering College, Ankushapur, Hyderabad- 501301IndiaIndia
Dr A. Ajmal KhaanAssociate Professor of English, Deputy Controller of Examinations, Jamal Mohamed College, Trichy- 620020IndiaIndia
Arnika KabraAssistant Professor, Department of Management, Shri Vaishnav Institute of Management and Science, Indore- 452009IndiaIndia
Sadashiv S. TavashiAnantrao Pawar College of Engineering and Research Parvati, PuneIndiaIndia
Dr. Manasi Vyankatesh GhamandeAssistant Professor, DESH, Vishwakarma Institute of Technology, PuneIndiaIndia
Dr Anjum Ayyaj PatelHead of Department Computer Science Vishwakarna College of ACS, Pune- 411048IndiaIndia
Dr Jyoti Prasad PatraPrincipal Nigam Institute of Engineering and Technology Niet UG PG Diploma Engineering, Odisha, India- 754006IndiaIndia
Dr G. PrabhakaranProfessor & Dean for Student Welfare, Department of Civil Engineering, Siddharth Institute of Engineering & Technology, Puttur- 517583IndiaIndia
Dr Sweta PriyaAssociate Professor, Amity School of Communication, Amity University, Patna- 801503IndiaIndia
Dr Nidhi TripathiAssistant Professor, Department of Zoology, Swami Shukdevanand College, Shahjahanpur, Uttar PradeshIndiaIndia
Deepa EAssistant Professor, Department of Economics,The Zamorins Guruvayurappan College, Calicut- 673014IndiaIndia

Specification

Description:[0001] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0002] Machine learning (ML) is a branch of artificial intelligence (AI) that allows systems to learn and improve from data without being explicitly programmed. Machine learning (ML) uses algorithms to analyze data, identify patterns, and make decisions. As systems are exposed to more data, their performance improves over time. Machine learning (ML) systems use neural networks and deep learning to learn from data. The more data a system is exposed to, the better it gets at performing tasks.
[0003] A number of different types of adaptive learning environments that are known in the prior art. For example, the following patents are provided for their supportive teachings and are all incorporated by reference.
[0004] US20210027647A1: Apparatuses, systems, methods, and computer program products are disclosed for adaptive machine learning. An apparatus includes a monitoring module that continuously monitors one or more interactions of a user while the user performs one or more simulated tasks digitally presented to the user that are associated with a learning path. The apparatus includes a metadata module that tracks data describing the user's interactions during the user's performance of one or more simulated tasks. The apparatus includes a machine learning module that, dynamically and in real-time, optimizes the user's learning path by simulating multiple different learning paths using one or more machine learning processes and tracked data. The apparatus includes a recommendation module that presents one or more recommendations to the user for optimizing the user's learning path. One or more recommendations may be generated as a function of the optimized learning path.
[0005] Proficiency-based learning is a teaching method that focuses on students demonstrating their mastery of content, rather than grades, age, or attendance. It's also known as competency-based learning. The goal of proficiency-based learning is to ensure that students are acquiring the skills and knowledge they need to succeed in school, college, careers, and adult life. The proposed invention focuses on analyzing the Smart and Adaptive Learning Environments through algorithms of Machine Learning.
[0006] Above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, no assertion is made, and as to whether any of the above might be applicable as prior art with regard to the present invention.
[0007] In the view of the foregoing disadvantages inherent in the known types of adaptive learning environments now present in the prior art, the present invention provides an improved system. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine learning based approach for building smart and adaptive learning environments that has all the advantages of the prior art and none of the disadvantages.
SUMMARY OF INVENTION
[0008] In the view of the foregoing disadvantages inherent in the known types of adaptive learning environments now present in the prior art, the present invention provides an improved one. As such, the general purpose of the present invention, which will be described subsequently in greater detail, is to provide a new and improved Machine learning based approach for building smart and adaptive learning environments which has all the advantages of the prior art and none of the disadvantages.
[0009] The Main objective of the proposed invention is to design & implement a framework of Machine Learning techniques for analyzing the parameters of Smart and Adaptive Learning Environments. Enhancing Education with Machine Learning and IT Proficiency Strategies is analyzed.
[0010] Yet another important aspect of the proposed invention is to design & implement a framework of Machine Learning techniques that will consider on understanding the functions of Enhancing Education with IT Proficiency Strategies. Building Smart and Adaptive Learning Environments is analyzed by predictive unit. The results of prediction are displayed on the display unit.
[0011] In this respect, before explaining at least one embodiment of the invention in detail, it is to be understood that the invention is not limited in its application to the details of construction and to the arrangements of the components set forth in the following description or illustrated in the various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0012] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be had to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.
BRIEF DESCRIPTION OF DRAWINGS
[0013] The invention will be better understood and objects other than those set forth above will become apparent when consideration is given to the following detailed description thereof. Such description makes reference to the annexed drawings wherein:
Figure 1 illustrates the schematic view of Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments, according to the embodiment herein.
DETAILED DESCRIPTION OF INVENTION
[0014] In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
[0015] While the present invention is described herein by way of example using several embodiments and illustrative drawings, those skilled in the art will recognize that the invention is neither intended to be limited to the embodiments of drawing or drawings described, nor intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention covers all modification/s, equivalents and alternatives falling within the spirit and scope of the present invention as defined by the appended claims. The headings are used for organizational purposes only and are not meant to limit the scope of the description or the claims. As used throughout this description, the word "may" be used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Further, the words "a" or "a" mean "at least one" and the word "plurality" means one or more, unless otherwise mentioned. Furthermore, the terminology and phraseology used herein is solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and any additional subject matter not recited, and is not intended to exclude any other additives, components, integers or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles and the like are included in the specification solely for the purpose of providing a context for the present invention.
[0016] In this disclosure, whenever an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same element or group of elements with transitional phrases "consisting essentially of, "consisting", "selected from the group consisting of", "including", or "is" preceding the recitation of the element or group of elements and vice versa.
[0017] Education is a process of acquiring or sharing knowledge, skills, and values, and developing character traits. It can take many forms, including formal, non-formal, and informal education. Education can help people develop skills for daily life, learn social norms, and develop judgment and reasoning. The goal of education is to help people navigate life and contribute to society.
[0018] Smart learning environments (SLEs) and adaptive learning are educational frameworks that use technology to personalize the learning experience for individual students. Use technologies like artificial intelligence, data analytics, and cloud computing to provide personalized learning paths, real-time feedback, and interactive content. Smart learning environments (SLEs) are context-aware, meaning they can sense the learner's situation and provide support based on their online and real-world status. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying the functions of Enhancing Education with IT Proficiency Strategies.
[0019] Reference will now be made in detail to the exemplary embodiment of the present disclosure. Before describing the detailed embodiments that are in accordance with the present disclosure, it should be observed that the embodiment resides primarily in combinations arrangement of the system according to an embodiment herein and as exemplified in FIG. 1
[0020] Figure 1 illustrates the schematic view of Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments 100. The proposed invention 100 includes a classroom 101 which is continuously monitored using the monitoring device 102. The monitoring device 102 includes a camera 103. The machine learning unit 106 will run predictive algorithm 107 which will predict the real time needs of the classroom 101. The cloud server 105 is connected to the display unit 108 and to the e-application 104, which provides enhanced adaptive learning environments.
[0021] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of the arrangement of the system according to an embodiment herein. It will be apparent, however, to one skilled in the art that the present embodiment can be practiced without these specific details. In other instances, structures are shown in block diagram form only in order to avoid obscuring the present invention.
, Claims:1. Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments, comprises of:
Machine learning unit;
Display unit and
Predictive unit.
2. Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments, according to claim 1, includes a machine learning unit, wherein the machine learning unit will run predictive unit.
3. Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments, according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.
4. Enhancing Education with Machine Learning and IT Proficiency Strategies for building Smart and Adaptive Learning Environments, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the real time needs of the classroom.

Documents

NameDate
202441086563-COMPLETE SPECIFICATION [09-11-2024(online)].pdf09/11/2024
202441086563-DRAWINGS [09-11-2024(online)].pdf09/11/2024
202441086563-FORM 1 [09-11-2024(online)].pdf09/11/2024
202441086563-FORM-9 [09-11-2024(online)].pdf09/11/2024

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