Graduation

This dataset focuses on 253 graduates from the class of 2020 majoring in Computer Science and Technology at Hebei Agricultural University. The data primarily comes from the graduates' academic performance and self-assessment questionnaires, specifically including scores from foundational professional courses, core professional courses, and general education courses to quantify their academic performance. Additionally, the Richter Scale (Likert scale) was used to collect and analyze qualitative data on students' innovation ability, autonomous learning ability, employability, personal qualities, and moral character.

According to the talent cultivation program, graduation requirements are divided into three primary indicators: Professional Knowledge and Skills, Professional Competence and Development, and Personal Qualities and Morality. Each primary indicator includes several secondary indicators. For example, "Professional Knowledge and Skills" covers engineering knowledge, research ability, and information technology; "Professional Competence and Development" includes innovation and entrepreneurship, lifelong learning, and professional norms; while "Personal Qualities and Morality" involves all-round development and moral character. Each secondary indicator is further subdivided into two or three tertiary indicators, and each evaluation criterion corresponds to supporting courses or evaluation scales.

To ensure the objectivity and reasonableness of the assessment results, the study first standardized the academic scores and Likert scale scores for each student. Then, based on the weights calculated using the Analytic Hierarchy Process (AHP), a comprehensive score for each student was calculated for each evaluation criterion. Subsequently, the TOPSIS model was applied to determine the positive ideal solution and negative ideal solution for each indicator. Combined with the weights obtained from AHP, the Euclidean distances between each student and the positive and negative ideal solutions were computed to derive the relative closeness of each student to the graduation requirements. This process ensured the objectivity and reasonableness of the evaluation results while considering the combined effect of all evaluation criteria. Finally, the achievements of these 253 graduates were assessed by integrating these two models.