Keuntungan, Batasan, dan Tantangan Penggunaan Building Information Modeling dalam Proses Pembelajaran
Bibliographic record
Abstract
This study investigates the use of Building Information Modeling (BIM) as a construction education technique on a campus setting. The planning, design, and project management processes are more accurate and efficient because to the growing use of BIM in the construction sector. However, there could be a number of problems and challenges when using BIM among students and teachers on a college campus. A literature analysis and questionnaires were employed in this study to gather information from students at one of Yogyakarta universities. The information covered students' understanding of BIM, their practical experience with it, and their perspectives on its advantages and disadvantages in the educational process. According to the study's findings, nearly all of college students are familiar with BIM. The survey also highlighted a number of obstacles that prevent the use of BIM on campuses, including a lack of infrastructure and resources, a curriculum that isn't properly integrated with BIM, and access issues for BIM software. The research also looked at the potential for creating OpenBIM as a way to improve teamwork and flexibility when using BIM on campuses. The adoption of BIM on campuses can have a substantial positive impact on students' readiness for a work market that is becoming more and more digitized and technologically focused.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".