# Tool for Predicting College Student Career Decisions: An Enhanced Support Vector Machine Framework
## 1 Introduction
This potential leads people to engage in interest-oriented behaviors that are conducive to the development of their abilities, and this innate motivation for self-determination constitutes an intrinsic motivation for human behavior. After several decades of research and development, self-determination theory has gradually formed a relatively complete theoretical system on human motivation and personality, which has been widely applied in the fields of organizational management, sports, psychological medicine, and educational counseling [3]. \"Autonomy needs\" are about having the psychological freedom to do things of one's own choice, \"Competence needs\" are about having control over one's environment and growing as a person, and \"Belonging needs\", also called relationship needs, are about having a sense of connection with other people. These three needs are important for people to grow, internalize, and be happy [4]. The main assumption of this theory is that when the three basic psychological needs of autonomy, competence, and relationship are met, people will be more willing and able to participate in activities, which will lead to more sustained and high-quality behaviors and better behavioral outcomes as well as better physical and mental health for people. At the same time, we find that the three basic psychological needs are very individualized and, interestingly, can exist widely across cultures and situations. Through a literature review, the core assumptions of self-determination theory are consistent with the characteristics of college students; therefore, it is feasible to discuss the construction of a career development model for college students based on self-determination theory. Until now, many studies have been conducted to better investigate and discuss the career development of college students. Using interview data from product development interns at a single engineering business, [PERSON] et al. [5] contributed insights into the particular abilities that interns describe as gaining in their internship and identified linkages between school-and-work learning. [PERSON] et al. [6] used a sample of 420 South Korean college students to analyze the cultural validity of the family impact scale in order to determine the degree to which family played a role in college students' career development within collectivistic societies. [PERSON] et al. [7] addressed the social constructivism foundations of machine learning approaches in career advising, as well as the relevance of social networks in psychological research. [PERSON] et al. [8] employed random forests in machine learning to predict students at risk of dropping out in order to identify and assist students who are in danger of dropping out. [PERSON] et al. [9] looked at how stereotyped attitudes about STEM occupations influenced STEM self-efficacy and STEM career-related result expectancies as well as how these constructs predicted STEM career desire in upper primary pupils. [PERSON] et al. [10] looked at the effects of interpersonal interactions on gay, lesbian, bisexual, and heterosexual college students' job decisions. [PERSON] et al. [11] investigated the impacts of a future time perspective on job selections, which comprised three sessions that were opportunity, value, and connectivity. By collecting 558 completed questionnaires, [PERSON] et al. [12] investigated the influence of several significant professional decision-making elements (i.e., advisers, industry mentors, parents, faculty members, and social media) on students. Therefore, in order to discuss the construction of a career development model for college students based on self-determination theory, this paper proposes a support vector machine (SVM) combined with improved butterfly optimization algorithm (BOA) named CBBOA that adds two mechanisms to BOA. First, a communication mechanism (CM) was added, which can enhance the exploitation ability and improve the convergence accuracy of the original BOA. We also introduced Gaussian bare-bones mechanism into the original BOA.