Intelligent Platform for Educational Resources of Computer Basic Courses in the Digital Education Environment
Bibliographic record
Abstract
Computer science is a highly practical professional foundation course, and its teaching purpose is to equip students with basic computer application skills and prepare them for future work. Due to the lack of clear goals in current computer foundation courses, teachers mainly adopt an "indoctrination" teaching method that only emphasizes theory and not practice, resulting in students becoming accustomed to "contact based" learning without applying theory to practice. Therefore, it is necessary to choose a scientific and reasonable teaching strategy based on the specific purpose of computer teaching and students' actual mastery, so that students can proficiently master and apply this knowledge in practice. On this basis, this article first described the main problems in the teaching of computer basic courses and the impact of digital learning environment on contemporary education models, thereby highlighting the necessity of building a computer basic course resource platform. After that, this article discussed a cloud service platform for computer basic course education resources. Finally, through experimental analysis and survey questionnaires, it was proven that the response time of the designed system was shorter than that of traditional systems; the accuracy of the system was superior to traditional systems, and 66.23% of respondents were satisfied with the system.
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 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".