ENHANCING E-LEARNING WITH ADVANCED MACHINE LEARNING TECHNIQUES TO OPTIMIZE STUDENT PERFORMANCE IN ENGLISH AS A FOREIGN LANGUAGE (EFL)

Abstract

Enhancing E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL) is the proposed invention. The proposed invention focuses on understanding the functions of Enhanced E-Learning. The invention focuses on analyzing the parameters of Student Performance as a Foreign Language in English using algorithms of Machine Learning Approach.

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 type of artificial intelligence (AI) that allows computers to learn and improve from experience without being explicitly programmed. Machine learning (ML) uses algorithms to analyze large amounts of data, identify patterns, and make predictions. The more data a machine learning system is exposed to, the better it gets at making predictions.
[0003] A number of different types of systems to analyse the efficiency of students learning English as foreign language 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] US20140065596A1: A computer-implemented method of generating learning exercises is provided. The method comprises receiving text, processing the text using linguistic parsers to generate linguistic characteristics of the text, storing the linguistic characteristics in a data file, retrieving user information comprising a user knowledge level and user goals, using the stored linguistic characteristics and the user information to generate the learning exercises based on a parametrical model, receiving responses to the learning exercises from the user, and updating the user information based on the responses to the learning exercises. The linguistic characteristics comprise words of the text and relationships between the words.
[0005] Advanced machine learning (ML) is a data-based approach to improving the performance of systems, using techniques and tools to solve a variety of ML problems. Advanced ML uses data to improve performance, whereas traditional ML uses experience. Advanced ML courses cover the theoretical foundations of modern ML, as well as advanced methods and frameworks. Advanced ML courses can help students develop skills for performing research to advance the state of knowledge in machine learning. The proposed invention focuses on analyzing the Student Performance in English as a Foreign Language (EFL) 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 systems to analyse the efficiency of students learning English as foreign language 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 English learning techniques integrated with machine learning algorithms for optimizing the efficiency of students studying English as foreign language 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 systems to analyse the efficiency of students learning English as foreign language 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 English learning techniques integrated with machine learning algorithms for optimizing the efficiency of students studying English as foreign language 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 Student Performance in English as a Foreign Language (EFL). Enhancing E-Learning with Advanced Machine Learning Techniques 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 Enhanced E-Learning. The Student Performance in English as a Foreign Language 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 E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL), 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] E-learning, or online learning, is the use of digital technologies to deliver learning and training. It can include computer-based learning, web-based learning, and mobile learning. Learners can access educational content at any time and from any location. E-learning can include interactive elements like videos, texts, audios, and images. E-learning can be used to train large groups of people in different locations.
[0018] To optimize student performance in English as a Foreign Language (EFL), focus on creating an immersive learning environment, integrating relevant content, encouraging active participation through varied speaking activities, building vocabulary strategically, providing regular feedback, and tailoring instruction to individual needs while fostering a positive and supportive classroom atmosphere. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying the functions of Enhanced E-Learning.
[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 E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language 100. The proposed invention 100 includes a student 101 who are studying English as a foreign language. The analysis unit 102 will stead the data of students 101 and sends it to the machine learning unit 103. The predictive unit 104 will predict the factors that can be improved to make the language easy for students 101. The results of predictive unit 104 will be displayed on display unit 105. The optimization unit 106 will optimize the outcomes.
[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 E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL), comprises of:
Analysis unit;
Display unit;
Predictive unit and
Optimization unit.
2. Enhancing E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL), according to claim 1, includes an analysis unit, wherein the analysis unit will stead the data of students and sends it to the machine learning unit.
3. Enhancing E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL), according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.
4. Enhancing E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL), according to claim 1, includes a predictive unit, wherein the predictive unit will predict the factors that can be improved to make the language easy for students.
5. Enhancing E-Learning with Advanced Machine Learning Techniques to Optimize Student Performance in English as a Foreign Language (EFL), according to claim 1, includes an optimization unit, wherein the optimization unit will optimize the outcomes.

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