Exploring Correlated Features in Deep Learning Models for Academic Advising
Bibliographic record
Abstract
In an attempt to facilitate the way students, succeed academically, academic advising is an integral part of the educational system. However, it has frequently been dependent on time-consuming manual processes and personalized interaction, which are not scalable. Machine learning and deep learning have the potential to completely transform advising by automating parts of process and giving students distinctive guidance. This paper explores the relationship between student academic performance and correlated features using deep learning methodology in academic advising systems. The model was trained and tested with the Kaggle dataset, and the results show high accuracy, specificity, and true positive rate values compared to naive bayes and random forest methodologies. The paper also addresses potential challenges and limitations of deep learning in academic advising, such as data privacy concerns, algorithmic bias, and the need for human intervention. It concludes by emphasizing the importance of incorporating ethical considerations and human expertise in the development and deployment of deep learning models in academic advising. Overall, the paper highlights the potential of deep learning algorithms to transform academic advising and improve student success.
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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 | 0.000 | 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".