Enhancing high-school dropout identification: a collaborative approach integrating human and machine insights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Despite its proven success in various fields such as engineering, business, and healthcare, human–machine collaboration in education remains relatively unexplored. This study aims to highlight the advantages of human–machine collaboration for improving the efficiency and accuracy of decision-making processes in educational settings. High school dropout prediction serves as a case study for examining human–machine collaboration’s efficacy. Unlike previous research prioritizing high accuracy with immutable predictors, this study seeks to bridge gaps by identifying actionable factors for dropout prediction through a framework of human–machine collaboration. Utilizing a large dataset from the High School Longitudinal Study of 2009 (HSLS:09), two machine learning models were developed to predict 9th-grade students’ high school dropout history. Results indicated that the Random Forest algorithm outperformed the deep learning algorithm. Model explainability revealed the significance of actionable variables such as students’ GPA in the 9th grade, sense of school belonging, self-efficacy in mathematics and science, and immutable variables like socioeconomic status in predicting high school dropout history. The study concludes with discussions on the practical implications of human–machine partnerships for enhancing student success.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it