PREDICTIVE MODELLING OF PSYCHOSOCIAL STRESS IN HIGHER EDUCATION TEACHERS WITH MACHINE LEARNING

Abstract

Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning is the proposed invention. The proposed invention focuses on understanding the functions of Teacher's Psychosocial Stress in Higher Education. The invention focuses on analyzing the parameters of Predictive Modelling of Psychosocial Stress 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 subset of artificial intelligence (AI) that allows machines to learn from data and improve their performance without being explicitly programmed. Machine learning (ML) algorithms use data to make predictions or classifications. They adjust weights to reduce the difference between the model estimate and a known example. Machine learning (ML) is used in many applications, including banking, online shopping, and social media.
[0003] A number of different types of techniques to predict stress among Higher education teachers 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] Mental Health Prediction Models Using Machine Learning in Higher Education Institution: Today, mental health problem has become a grave concern in Malaysia. According to the National Health and Morbidity Survey (NHMS) 2017, one in five people in Malaysia suffers from depression, two in five from anxiety, and one in ten from stress. Higher education students are also at risk of being part of the affected community. The increased data size without proper management and analysis, and the lack of counsellors, are compounding the issue. Therefore, this paper presents on identifying factors in mental health problems among selected higher education students. This study aims to classify students into different categories of mental health problems, which are stress, depression, and anxiety, using machine learning algorithms. The data is collected from students in a higher education institute in Kuala Terengganu. The algorithms applied are Decision Tree, Neural Network, Support Vector Machine, Naïve Bayes, and logistic regression. The most accurate model for stress, depression, and anxiety is Decision Tree, Support Vector Machine, and Neural Network, respectively.
[0005] Psychosocial stress is a type of stress that occurs when an individual's ability to cope with adverse experiences in their daily life is outmatched. It can be caused by social or cultural situations that create physical, emotional, or psychological strain. Psychosocial stress can have a significant impact on an individual's mental and physical health, and can lead to a number of consequences, including Social isolation, Parenting stress, Heart disease, High blood pressure, Addictive behavior and etc. The proposed invention focuses on analyzing the modelling of Stress in 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 techniques to predict stress among Higher education teachers 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 Predictive Modelling for analysing Psychosocial Stress in Higher Education Teachers with Machine Learning 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 techniques to predict stress among Higher education teachers 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 Predictive Modelling for analysing Psychosocial Stress in Higher Education Teachers with Machine Learning 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 Predictive Modelling of Psychosocial Stress. Predictive Modelling of Psychosocial Stress 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 the functions of Teacher's Psychosocial Stress in Higher Education. Predictive Modelling of Psychosocial Stress 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 Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning, 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 is a formal stage of learning that takes place after secondary education. It is also known as post-secondary education, third-level education, or tertiary education. Higher education is offered at universities, colleges, and polytechnics, and it results in the award of an academic degree. In India, higher education options include undergraduate, postgraduate, and doctoral programs.
[0018] Teachers experience high levels of psychosocial stress, which can have negative consequences on their health, their students, and the economy. Stress management strategies can help reduce stress and its consequences. Cognitive-behavioural therapy (CBT) is one strategy that can help teachers build resilience to stress. Teachers experience stress from a variety of sources, including life events, social stressors, and everyday stress. However, the main source of stress in the classroom is problematic teacher-student interactions. The proposed invention focuses on implementing the algorithms of Machine Learning Approach for studying the functions of Teacher's Psychosocial Stress in Higher Education.
[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 Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning 100. The proposed invention 100 includes a higher education teacher 101 who are analysed for understanding the psychological stress they go through. The analysis unit 102 will alerted using the IOT unit 106. The machine learning unit 103 will run predictive unit 104 and the results of predictive unit is stored on resultant unit 105. The resultant unit 105 will send alert messages to IOT unit 106.
[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. Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning, comprises of:
Analysis unit;
Machine learning unit and
Resultant unit.
2. Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning, according to claim 1, includes an analysis unit, wherein the analysis unit will be alerted using the IOT unit.
3. Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning, according to claim 1, includes a machine learning unit, wherein the machine learning unit will run predictive unit.
4. Predictive Modelling of Psychosocial Stress in Higher Education Teachers with Machine Learning, according to claim 1, includes a resultant unit, wherein the resultant unit will send messages to IOT unit.

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