Utilization of Artificial Intelligence Technology in Higher Education Management: Teaching Theory and Practical Skills of Landscape Architecture Construction Technology
Bibliographic record
Abstract
In traditional university education management, landscape construction technology helps students comprehensively master landscape construction technology by teaching theoretical knowledge such as the basic principles of landscape construction and setting up practical bases for landscape construction. However, this approach has some limitations, such as delayed information transmission, which limits the flexibility and effectiveness of learning. Therefore, artificial intelligence technology can be applied to the teaching theory and practice of landscape construction technology in university education management. The word bag model and SVM (Support Vector Machine) algorithm were used as a case analysis tool for landscape construction technology to analyze construction problems and solutions in real cases, and then virtual reality (VR) and augmented reality (AR) technologies were used to enable students to practice landscape construction in a virtual environment. Finally, a Convolutional Neural Network (CNN) model was used to provide specific learning resources and operational recommendations. This article applied artificial intelligence technology to the theory and practice of landscape construction technology in university education management. The average score of students in the test has increased by 8 points, and over 90% of students can independently complete the experiment. With the help of artificial intelligence technology, university education management can break the limitations of time and space, improve the flexibility and convenience of students' learning, and provide more timely feedback for education managers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".