MACHINE LEARNING APPROACHES TO PREDICT EMPLOYABILITY OF STUDENTS IN HIGHER EDUCATION

Abstract

Machine Learning Approaches to Predict Employability of Students in Higher Education is the proposed invention. The proposed invention focuses on understanding how the students in higher education institutions can predict the employability. The invention focuses on analyzing the parameters of Employability of Students in Higher Education using algorithms of Machine Learning.

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. ML uses algorithms to analyze large amounts of data, identify patterns, and make predictions. Machine learning (ML) is well-suited for situations where data is always changing, or where coding a solution would be difficult.
[0003] A number of different types of higher education students employability analysis systems 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] US20090035733A1: Device, system, and method of adaptive teaching and learning. For example, a teaching/learning system includes a real-time class management module to selectively allocate first and second digital learning objects for performance, substantially in parallel, on first and second student stations, respectively.
[0005] Education is the transmission of knowledge, skills, and character traits and manifests in various forms. Formal education occurs within a structured institutional framework, such as public schools, following a curriculum. Non-formal education also follows a structured approach but occurs outside the formal schooling system, while informal education entails unstructured learning through daily experiences. The proposed invention focuses on analyzing the Employability of Students in Higher Education through algorithms of Machine Learning Approach.
[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 higher education student's employability analysis systems 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 to predict the employability skills of higher education students 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 higher education student's employability analysis systems 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 to predict the employability skills of higher education students 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 Employability of Students in Higher Education. Machine Learning Approaches to Predict Employability of Students in Higher Education 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 how the students in higher education institutions can predict the employability. Machine Learning Approaches to Predict Employability of Students in Higher Education 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 Machine Learning Approaches to Predict Employability of Students in Higher Education, 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] Higher education refers to the level of education that follows secondary education (high school) and provides advanced academic and professional knowledge and skills. It typically includes undergraduate programs, such as bachelor's degrees, and postgraduate programs, such as master's degrees and doctoral degrees. Higher education institutions include universities, colleges, and professional schools.
[0018] To predict a student's employability in higher education, consider factors like their academic performance (GPA), relevant skills acquired through coursework and internships, extracurricular activities, leadership roles, communication abilities, critical thinking skills, technical proficiency (depending on the field), and demonstrated initiative; analyzing this data through machine learning models like decision trees, random forests, or logistic regression can provide a comprehensive prediction of their potential employability upon graduation. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying how the students in higher education institutions can predict the employability.

[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 Machine Learning Approaches to Predict Employability of Students in Higher Education 100. The proposed invention 100 includes a student 101 who are studying higher education. The skills of students 101 is analysed and stored on analysis unit 103. The employers 102 and their exceptions are stored on database 104 along with skill set of students 101. The machine learning unit 105 will run predictive unit 106 and displays the results on display unit 107 which is predicting employability.

[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. Machine Learning Approaches to Predict Employability of Students in Higher Education, comprises of:
Machine learning unit;
Display unit and
Predictive unit.
2. Machine Learning Approaches to Predict Employability of Students in Higher Education, according to claim 1, includes a machine learning unit, wherein the machine learning unit will run predictive unit.
3. Machine Learning Approaches to Predict Employability of Students in Higher Education, according to claim 1, includes a display unit, wherein the display unit will display the results of predictive unit.
4. Machine Learning Approaches to Predict Employability of Students in Higher Education, according to claim 1, includes a predictive unit, wherein the predictive unit will predict the employability of students in higher education.

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