The application of improved backpropagation neural network in college student achievement prediction
Bibliographic record
Abstract
Educational institutions generate a large amount of digital data in their daily operations, which is stored in servers, forming a substantial educational data set. Extracting valuable information through practical data analysis has become a critical problem that needs to be solved urgently. Students' examination results are an essential basis for evaluating their learning status, which reflects the effect of school education to some extent. Therefore, we propose a model based on the BP network and Pandas to construct a prediction model for Pandas' performance in the first year and their successful graduation to explore the potential relationship between Pandas' performance in the freshman year and graduation, thus realizing the principle of early guidance and improvement of teaching quality. Through the random prediction experiment of 9,424 scores data of 304 students in 2017 and 2018 majoring in network engineering at a university, the accuracy rate is 96.71% after the experimental data analysis and verification, which has proved that there is a potential correlation between the students' first-year course scores and graduation. Meanwhile, the improved BP network proposed in the present research exhibits reasonable practicability and extensibility in the college student achievement prediction model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".