Career Planning Method Based On Survey Simulation Model

Field of the Invention

The invention relates to mathematical modeling of career choice and a survey simulation based career planning method for optimization of the model.

State of the Art

Approximately three hundred thousand of the two and a half million candidates who applied to the Higher Education Institutions Examination (HEIE) organized by the Student Selection and Placement Center (SSPC) in Turkey are candidates who have already been placed in a program. Students who have already been placed in a program are required to reapply for the exam. It suggests that students are not satisfied with their programs. On the other hand, it is known that many young people enroll in departments they do not want to study or are not interested in just to become university graduates. It is thought that the pressure of "getting a university education no matter which department" has an effect on students' enrollment in the departments they do not want to study. It is seen that after the individuals enroll in the departments they do not want without taking into account their expectations of professional results under the influence of this pressure, their life satisfaction is affected and they apply to HEIE again in search. Therefore, career choices that cannot be made correctly cost both individuals and societies wasted time and money, and unsatisfied with their lives and have low confidence in their future profession individuals. When choosing a career, an individual may prefer the career he/she has wanted to be since childhood.

An individual can choose a career according to his/her HEIE score. Another individual may prefer the career of someone he/she takes as a model. Another individual may be influenced by his/her friends. Another individual may have chosen that career entirely due to the influence of the luck factor. As a result, different people make career choices for different reasons. However, since choosing a career suitable for individuals is first and foremost an opportunity for their happiness rather than a decision to be taken without thinking, the chosen career affects the way the individual perceives himself, his income level, social status, social acceptance, social relations, self-expression, private life, time use, health, psychological well-being, satisfaction from work and life, it is necessary to make the right use of this opportunity, for individuals to follow a systematic path in career selection, to choose which education or profession they will turn to. to get help with the issue.

As it is known, in scale development or adaptation studies, researchers need large samples to collect data suitable for factor analysis, which can be costly for them. In addition, participants may leave some items blank, some participants fail attention questions (e.g., "Leave this item blank.") leads to data loss. It also states that a factor analysis starts with a set of imperfect data, so the results of a single factor analysis are less reliable and should be conducted at least twice. Considering that data are collected again to repeat the factor analysis, this will be even more costly for researchers.

As a result, due to the above-mentioned problems and the inadequacy of the existing solutions, it was deemed necessary to make an improvement in the relevant technical field.

Aim of the Invention

Thanks to the inventive method, students will be able to make career choices based on mathematical foundations without the support of a career counselor.

There are several stages in the proposed algorithmic approach. In the first stage, the factors affecting the choice of career are determined. In the second stage, simulations, which are not used much in social sciences, are used to test the proposed scale before going directly to the field and to check its compatibility with the data collected from the field. In order to save time and cost, a simulation model is proposed in the proposed algorithmic approach.

In the third stage, the proposed algorithm first acts as an expert system. But then career development is described by the mathematical model given below. With this model, the entire career selection or career determination process is modeled with a linear analytical model. For example, if a student wants to pursue a career in computer engineering in the field of engineering, the appropriate CC (Career Coefficient) is determined and the appropriate career development process can be found with optimization algorithms.

With the method aimed to be developed in this proposed algorithm, individuals who are on the verge of making a career choice will be able to make the right career choice by logging into the system wherever they are in the world and answering the items, or they will be informed about what kind of changes they need to make in their characteristics in order to get one step closer to their target career.

In order to create the proposed algorithmic approach, it is necessary to recreate many scales and test them with accepted testing mechanisms found in the literature. The first difference or innovation in the proposed model is that the proposed scale has to be tested with simulations, which are not used much in social sciences, before going directly to the field, tests are planned to be carried out.

Based on the algorithm proposed in the study, analysis can be done with different optimization methods. In other words, the algorithm can be easily adapted to different methods. Because the proposed algorithmic structure is flexible enough to be applied to every student. Even in the literature The desired algorithm can also be used to solve the objective function of the career coefficient. This is also It provides the advantage that the proposed model can be applied objectively to the desired person.

The structural and characteristic features of the invention and all its advantages are more clearly illustrated by the following figures and the detailed description with references to these figures will be understood.

Figures Clarifying the Invention

Figure 1: The steps required to construct a valid questionnaire in the methodology shows.

Figure 2: Shows the process steps of the questionnaire application in the method in question.

Figure 3: The method according to the invention shows a flow diagram for applying an optimization model to the career coefficient model.

The drawings are not necessarily to scale and may omit details that are not necessary to understand the present invention.

Detailed Description of the Invention

In this detailed description, the preferred embodiments of the invention are described solely for the purpose of a better understanding of the subject matter and without limiting effect. First of all, the invention has a unique cost-benefit structure for the most basic methods applied in career planning since the mathematical model of career planning is derived. It has been shown that career planning can be done with the help of new and unique criteria by using expert knowledge. In this way, a personalized career plan can be made by simply answering the questions asked without consulting an expert shown.

Factors affecting career choice are individual factors (IF), social factors (SoF), systemic factors (SiF) and chance (Sa). With the proposed algorithmic approach, a mathematical model in the form of an career coefficient calculation was created. With this model, all The process of career selection or career determination is modeled with a linear analytical model.

For example, if a student wants to pursue a career in computer engineering, the appropriate CC (career coefficient) can be determined and the appropriate career development process can be found with optimization algorithms. In fact, one of the most unique aspects of this study is the methodology of defining the objective function.

Within the scope of the invention, appropriate and unique objective functions were determined. While determining the objective functions, 4 main components affecting the student's career choice and sub-components affecting these main components were determined (Table 1 ). Using this objective function structure, the objective function can be defined in any configuration. This is also suggested the algorithm to create a student-specific career model for each student's career plan provides. In fact, one of the most unique aspects of this study can be shown as the methodology of defining the objective function. Because for the first time in this field, a mathematical model of career choice or planning has been derived.

In determining the factors affecting career choice and developing the items, the literature was utilized and many items were redeveloped. In this way, flexibility has been provided to the models. All algorithms used during optimization are optimized based on the proposed career model. The proposed career coefficient model can work with any optimization algorithm. This means that all interests, i.e. the optimal career plan for students under all circumstances.

The algorithm structure given in Figure 1 is unique. It is a virtual survey simulator model used to test the proposed scales by taking into account all the components of the problem and this model can perform validity analysis of the surveys obtained from the field. The model will first create the data set in which the question in the scale and all the score probabilities will be found. Then an expert system will be activated in the code.

This expert system will filter all configurations in the data pool according to the item relationships previously determined according to expert knowledge, and will create a reasonable results data matrix by taking reasonable results. In this way, an expert system that checks the reasonableness of the survey results as well as a structure that derives simulation survey results will be obtained. But essentially different mathematical models and approaches will be used at this stage. For example, by selecting random data sets in the resulting plausible data matrix, results can be obtained as if a survey is being conducted in the field.

In the model, first of all, the questions in the question pool were grouped into groups of 10 in a certain systematic way in order to make it easier to create the all probability matrix. An example of the matrix for generating all probabilities is given below. for 1 = 1 : 4 end end

The EPM matrix, created according to the constraints specified in the sample survey form, is filtered to ensure the selection of results that closely approximate human responses from the EPM parameter vector space. The CSM matrix is formed based on the example structure given below. fori = 1 : 4 end end end

Subsequently, a simulation of the random person survey process is aimed to be conducted by making random selections in the obtained CSM matrix. However, in this part, a new approach that is not commonly used within algorithms but can have a significant impact when used is employed. In fact, the goal is to achieve a structure that optimizes processes with optimization algorithms simulating the career goals the student wants to pursue according to the specified criteria, and provides the student with a possible career plan. This way, obtaining preliminary results before entering the field will be facilitated. This is an extremely innovative approach for this field. For example, instead of conducting a survey with 1000 people, the consistency of the developed scale could be tested with a 100-person survey. As seen in the table, the proposed model consists of four different components: individual factors (IF), social factors (SF), systemic factors (SysF), and luck (L). The suggested algorithmic approach, as mentioned in previous sections, initially operates as an expert system. However, later on, career development is defined using the mathematical model provided below.

CC: 01IF+ o ₂ SF+ o ₃ SysF+ o ₄ L (1 )

The entire process of career selection or determination is modeled with a linear analytical model using this approach. For instance, if a relevant student wants to pursue a career in computer engineering, a suitable CC (career coefficient) is determined for them, and optimization algorithms can be employed to find appropriate career development paths. The coefficients o1 , o2 , o3 and o4 in the equation represent the factors influencing the career components in the individual's career plan. It is a requirement that o1 + o2+ o3+ o4=1 .

These coefficients are determined as follows. As known, initially, a current situation analysis is conducted in the system. According to the current situation analysis, if the individual factors of the surveyed person are high, a high value such as 0.4 or 0.5 is assigned to the o coefficient, while low coefficients are assigned to other variables. In fact, Equation 1 represents the objective function used during the optimization process. Figure 3 illustrates the optimization flow of the obtained objective function. The objective function, based on the original structure, produces results tailored to the individual, i.e. , according to individual constraints.

In this study, a more innovative approach is employed by using heuristic optimization algorithms during the optimization process. After conducting the current situation analysis in the algorithm, the relevant student is asked to choose the CC (career coefficient) value that is determined by us and shown on the system for the desired profession. In essence, even though the profession is chosen in the foreground, the program actually determines the CC value for Equation 1. Of course, with the determined values o1 , o2 , o3 and o4 trials are conducted using optimization algorithms to reach the relevant CC value for all questions in the system components.

In the background, the algorithm prevents the sticking problem to a local point for the system by running the mentioned optimization algorithms and prints the best result among the obtained results on the screen. Since the obtained result is presented in comparison with the current situation, a comprehensive future plan is made. Although analytical optimization methods are generally used in such problems, this project demonstrates that heuristic optimization algorithms can be used for all kinds of problems.

As shown in Figure 1 and Figure 2, the main flow of the inventive method is as follows:

- determining the pool of questions and the type of distribution to be used when making random selections,

- deriving all possible probabilities according to the items in the question pool and creating an overall probability matrix,

- filtering the entire probability matrix according to the specified constraints and generating the candidate solution matrix,

- creation of the survey sample result matrix by making random selections from the candidate solution matrix,

- factor analysis of the survey sample result matrix,

- testing the validity of the analysis result and accepting the survey as valid if the survey sample result matrix is consistent,

- conducting a current situation analysis of the student by applying a validated questionnaire to the student,

- student's choice of career and optimization of the career coefficient by starting the optimization according to the relevant career, - is to compare the current situation analysis with the optimized occupational coefficient and report the result.

In a preferred embodiment of the invention, if the survey sample result matrix is not consistent, the constraints will be updated until a consistent result is obtained.