Utilization of Artificial Intelligence Technology in Higher Education Management
Bibliographic record
Abstract
Traditional university education management has issues such as low efficiency and lack of personalization. As artificial intelligence (AI) technology develops rapidly, its application in educational management in universities is increasingly becoming a focus of attention for academics and educational institutions. To explore the application of AI technology in higher education management, this paper focused on personalized course recommendations for students. The data from the 2010 KDD Cup Education Data Mining Challenge dataset was collected and cleaned using Talend and Apache Spark tools; information features were extracted using information gain, and finally the data was trained using the C4.5 decision tree algorithm to obtain a recommendation model. After experiments, the precision of this model for students' preferences in course selection reached 94%, and the F1 value of the model reached 0.93, indicating that the model had good precision and comprehensiveness. At the same time, the highest recommended course click through rate reached 0.39, indicating that the personalized recommendation ability of the model was excellent. This model improved the efficiency of students' course selection and the utilization of educational resources, exploring new ways for university education management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".