Journal of Applied Data Sciences
Vol 6, No 3: September 2025

Development of a Self-Identity Construction Model for Private Vocational College Students Using Data Science Techniques

Chen, Mei (Unknown)
Sangsawang, Thosporn (Unknown)



Article Info

Publish Date
25 Jun 2025

Abstract

This study aimed to synthesize theories of self-identity learning to develop a self-identity development model for private vocational college students in Yunnan Province, China, identify key influencing factors, and evaluate the model's effectiveness. Using purposive sampling, the study involved 17 experts and 1,004 first-year students. Data were collected through a semi-structured questionnaire via Delphi Technique, supported by consultations via email, WeChat video, and in-person interviews. The model’s validity was assessed based on satisfaction levels from students, teachers, and stakeholders. Statistical analyses included weight calculations, means, standard deviations, coefficients of variation, and path analysis. The results showed strong expert consensus, with an average score of M = 4.5008 and CV = 0.1181, forming a model of 27 first-level and 21 second-level indicators. The "career development expectation evaluation" held the highest weight at 26.86% in the initial assessment, while "dynamic feedback loop development" recorded the highest importance at 0.442 in the practical development phase. Practical testing demonstrated significant effectiveness, with satisfaction means ranging from M = 4.059 to 4.341. Regression analysis confirmed significant mutual influences among the model's five modules. Overall, the model effectively addresses the urgent need for personalized development strategies for private vocational college students in Yunnan Province.

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Journal Info

Abbrev

JADS

Publisher

Subject

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Description

One of the current hot topics in science is data: how can datasets be used in scientific and scholarly research in a more reliable, citable and accountable way? Data is of paramount importance to scientific progress, yet most research data remains private. Enhancing the transparency of the processes ...