期刊論文
| 學年 | 114 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2026-06-01 |
| 作品名稱 | AI-Driven Sustainable Transformation of the Educational Supply Chain: Comparative Evaluation of Machine Learning Models for an Early Warning System and Design-Level Frameworks for Institutionalization and Impact Assessment |
| 作品名稱（其他語言） | |
| 著者 | Chen-Chung Chi |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | Sustainability 2026 18(11), p. 5523 |
| 摘要 | Higher education institutions face the persistent challenge of student attrition, a critical risk node within the educational supply chain (ESC). This study adopts a supply chain management (SCM) perspective to apply artificial intelligence (AI) for sustainable transformation of the ESC and evaluates an early warning system (EWS) for student performance prediction on a single programming course at Tamkang University. Learning trajectory data from 188 students across four semesters (90 for training, 98 for temporal validation; 30 fail cases in total) were collected from the iClass learning management system. To match the operational goal of the EWS—maximizing detection of at-risk students—the minority Failclass was treated as the positive class, so that recall directly measures sensitivity to at-risk cases. Three models were compared under a 5-seed protocol with time-masking to prevent future-week leakage: Random Forest (RF) with SMOTE, GRU, and LSTM. Averaged across weeks 6–16 and both validation semesters, RF achieved an accuracy 85.59%, a Fail-recall 91.19%, a precision 58.89%, and an F1 70.36%, already providing reliable warning at Week 6 (Fail-recall 87.86%). Under the same protocol LSTM and GRU collapsed to the majority class during weeks 6–10 (Fail-recall 0–42%), yielding higher headline accuracy but substantially lower sensitivity; they became usable only from Week 14 onwards (LSTM Fail-recall 80.00% at Week 14, 82.86% at Week 16). A Wilcoxon test on Cohen’s d over 90 (week×feature) pairs showed that cumulative features exhibit larger, not smaller, between-class separation than original features (|𝑑| 0.717 vs. 0.192; 𝑝<0.001), indicating that the original-vs-cumulative trade-off is one of sensitivity versus precision rather than information dilution. As design-level companions to these empirical results, the study also proposes a three-tier institutionalization framework and a four-dimensional impact assessment framework; these are offered as implementation blueprints rather than empirically validated outcomes. The contributions of this paper are operational rather than methodologically novel: (i) a reproducible EWS benchmark on a small, imbalanced ESC dataset, including a diagnosis of LSTM/GRU’s early-week majority-class collapse under naive augmentation, and (ii) design-level institutionalisation and impact-assessment scaffolding offered as a template for subsequent institutional pilots, not as empirically validated outcomes of the present study. |
| 關鍵字 | artificial intelligence;educational supply chain;sustainable transformation;early warning system;learning analytics;institutionalization;impact assessment;organizational learning;talent development |
| 語言 | en |
| ISSN | |
| 期刊性質 | 國外 |
| 收錄於 | SSCI |
| 產學合作 | |
| 通訊作者 | Chen-Chung Chi |
| 審稿制度 | 否 |
| 國別 | CHE |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/129315 ) |