Predictive Analysis of College Students' Creative Self-Efficacy Based on Decision Tree Modeling

Creativity is a focal point in current psychological research, domestically and internationally. An individual's self-awareness of their creativity is vital for determining their creative potential, helping them understand their strengths and weaknesses, and enhancing their self-creativity. There are few studies on college students' self-perception of creativity and even fewer on using the decision tree (DT) to predict such self-perception. This study aimed to assess the effectiveness of using a modified C5.0 decision tree model (DTM) in predicting creative self-efficacy (CSE) among college students and to identify which variables could serve as predictors of CSE. In response to these questions, 607 college students in Sichuan Province, China, filled out a Strengths and Difficulties Questionnaire (SDQ) containing five sections: psychological resilience scale, academic self-efficacy scale, psychological sense of school membership, innovative behavior scale, and CSE scale. The method described involves systematically providing the SDQ, scoring the results, and assigning codes to the answers of 60 students. Using the SDQ sub-scales, a DTM called C5.0 was built to predict the types of behavioral risk. The DTM was trained and cross-validated, showing a 10-fold improvement, and its performance was evaluated by accuracy and F1-score. The results of the revised C5.0 DTM revealed that the significant predictive factors of CSE, in decreasing order of importance, are psychological trust, psychological sense of school membership, academic self-efficacy, and innovative behavior. By proving the DTM's effectiveness in predicting college students' CSE, this research developed the link between basic learning and CSE, increased the interpretation of CSE, and provided research support for enhancing college students' creativity and CSE.

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Publication Details

Journal
Journal of Visualized Experiments
Published
2025-08-08
DOI
https://doi.org/10.3791/68730
Primary Topic
Educational Technology and Pedagogy
Type
article
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article

Predictive Analysis of College Students' Creative Self-Efficacy Based on Decision Tree Modeling

M HONG, X. Ai, Linghui Fu, Xueli Wan
Journal of Visualized Experiments
Educational Technology and Pedagogy
article

Predictive Analysis of College Students' Creative Self-Efficacy Based on Decision Tree Modeling

M HONG, X. Ai, Linghui Fu, Xueli Wan
article en

Abstract

Creativity is a focal point in current psychological research, domestically and internationally. An individual's self-awareness of their creativity is vital for determining their creative potential, helping them understand their strengths and weaknesses, and enhancing their self-creativity. There are few studies on college students' self-perception of creativity and even fewer on using the decision tree (DT) to predict such self-perception. This study aimed to assess the effectiveness of using a modified C5.0 decision tree model (DTM) in predicting creative self-efficacy (CSE) among college students and to identify which variables could serve as predictors of CSE. In response to these questions, 607 college students in Sichuan Province, China, filled out a Strengths and Difficulties Questionnaire (SDQ) containing five sections: psychological resilience scale, academic self-efficacy scale, psychological sense of school membership, innovative behavior scale, and CSE scale. The method described involves systematically providing the SDQ, scoring the results, and assigning codes to the answers of 60 students. Using the SDQ sub-scales, a DTM called C5.0 was built to predict the types of behavioral risk. The DTM was trained and cross-validated, showing a 10-fold improvement, and its performance was evaluated by accuracy and F1-score. The results of the revised C5.0 DTM revealed that the significant predictive factors of CSE, in decreasing order of importance, are psychological trust, psychological sense of school membership, academic self-efficacy, and innovative behavior. By proving the DTM's effectiveness in predicting college students' CSE, this research developed the link between basic learning and CSE, increased the interpretation of CSE, and provided research support for enhancing college students' creativity and CSE.

Journal of Visualized Experiments(222)
University of Malaya (MY), Chengdu Normal University (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 90%
Educational Technology and Pedagogy
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Predictive Analysis of College Students' Creative Self-Efficacy Based on Decision Tree Modeling — M HONG, X. Ai, et al. · Journal of Visualized Experiments (2025) | TGRS Research Map | TGRS