Enterprise AI Research Analysis
How to quantitatively evaluate the craftsmanship spirit in higher education courses? --Multi dimensional learning performance prediction model based on SVM-PDP
Authors: Liyun Luo, Qiang Wan, Jincheng Ma
This study takes the cloud computing course teaching of the College of Big Data and Artificial Intelligence in higher education institutions as the background, introduces the CraftsmanshipScale (which includes five dimensions: Personal growth, Accountability, Refine on, Cherish reputation, and Firm and persistent), and combines support vector machine (SVM) and partial Partial Dependence Plot (PDP) technology to construct an interpretable predictive model. The results showed that the recognition accuracy of the prediction model was high (F1 value of 0.93), and the dimension of "Firm and persistent" ranked first in the relative importance of the prediction results for the overall course evaluation, at 0.24. This study provides interpretable decision-making basis for the optimization of cloud computing courses in higher education institutions, the cultivation of students' mental health, and the improvement of learning values. At the same time, it expands the application boundary of the CraftsmanshipScale in the field of computer related professional education.
Keywords: Craftsmanship Scale, SVM model, Partial Dependence Plot, SHapley Additive exPlanations
Executive Impact & Key Findings
Leverage cutting-edge AI methodologies to enhance educational outcomes and foster critical 'Craftsmanship Spirit' in technical curricula. Our analysis highlights the robust performance of predictive models and the critical dimensions influencing student success.
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SVM Prediction Accuracy
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Model Performance Score
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Discriminative Ability
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Top Feature Importance (Firm & Persistent)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Core of Craftsmanship Spirit
The spirit of craftsmanship is a professional attitude and spiritual concept, balancing technical depth with professional ethics. It involves key dimensions like Personal growth, Accountability, Refine on, Cherish reputation, and Firm and persistent. Cultivating this spirit is crucial for students to adapt to industry changes and realize their full potential, especially in technical fields like cloud computing.
Integrating Craftsmanship in Cloud Computing Courses
Cloud computing, as a cornerstone of the national digital economy, requires talents who not only master technical skills but also embody the craftsmanship spirit. Traditional evaluations often miss this synergistic blend. This study highlights the need to integrate principles of technical precision, continuous optimization, and integrity throughout course design, project development, and teaching evaluation to cultivate well-rounded professionals.
Leveraging SVM, SHAP, and PDP for Interpretation
The study employs a robust methodology by combining Support Vector Machine (SVM) for predictive modeling with SHAP (Shapley Additive exPlanations) and PDP (Partial Dependence Plots) for model interpretability. SVM excels in classification tasks by finding optimal hyperplanes, while SHAP values quantify each feature's contribution to the prediction. PDP visualizes non-linear relationships and feature interactions, providing deep insights into complex models.
Unveiling Key Drivers of Learning Performance
Empirical analysis demonstrated that the SVM model achieved high accuracy in predicting student learning outcomes. Crucially, "Firm and persistent" emerged as the most significant dimension of craftsmanship spirit, followed by "Accountability". The interactive effects shown by PDP reveal that higher levels of these dimensions strongly correlate with positive course evaluations, offering a clear guide for educators to prioritize specific traits.
93.16%
SVM Prediction Accuracy for Craftsmanship Spirit Evaluation
The Support Vector Machine model achieved an impressive 93.16% accuracy (F1 score of 0.93 and ROC-AUC of 0.987) in predicting student learning performance based on the Craftsmanship Scale, demonstrating its effectiveness in quantitative evaluation.
| Source | Key Dimensions |
|---|---|
| German Dual System |
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| Chinese Scholars |
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| Craftsmanship Scale (This Study) |
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Enterprise Process Flow: Craftsmanship Spirit Evaluation
Define Craftsmanship Spirit Dimensions
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Develop & Validate Craftsmanship Scale
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Collect Student Performance & Scale Data
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Train SVM Predictive Model
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Interpret Model with SHAP & PDP
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Derive Actionable Educational Insights
Key Craftsmanship Dimensions Driving Learning Performance
The SHAP analysis revealed that the 'Firm and persistent' dimension (CS5) is the most critical factor, contributing 24.26% to the overall course evaluation, followed by 'Accountability' (CS2) at 11.52%. Partial Dependence Plots further illustrate that high levels of both 'responsibility and accountability' and 'conviction and persistence' significantly promote positive learning outcomes, highlighting the importance of fostering these traits in cloud computing education. This insight provides actionable intelligence for educators to refine curriculum and pedagogical strategies.
Calculate Your Potential AI ROI
Estimate the significant efficiency gains and cost savings your organization could achieve by integrating AI-driven insights, similar to those presented in this analysis.
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Your AI Implementation Roadmap
Transform educational assessment and student development within your institution through a structured AI integration process, fostering craftsmanship in technical fields.
Phase 1: Discovery & Strategy Alignment
Collaborate to define specific educational goals, identify key craftsmanship dimensions relevant to your curriculum, and assess current data infrastructure for AI integration. This foundational phase ensures alignment with your institutional vision.
Phase 2: Data Preparation & Model Training
Collect and preprocess student performance and behavioral data, including existing assessment scores and custom craftsmanship surveys. Train and validate tailored SVM and interpretable AI models, ensuring high accuracy and robust performance for your specific context.
Phase 3: Insights Generation & Pedagogical Integration
Utilize SHAP and PDP to generate transparent insights into student learning drivers and areas for improvement. Integrate these AI-driven recommendations directly into curriculum design, teaching methodologies, and personalized student feedback loops.
Phase 4: Monitoring, Refinement & Scaling
Continuously monitor model performance and educational outcomes. Implement feedback mechanisms to refine AI models and pedagogical strategies. Scale successful interventions across various courses and departments to maximize impact and foster a widespread culture of craftsmanship.
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