Predictors of Student Success In Supplemental Instruction Courses at A Medium Sized Women’s University
Bibliographic record
Abstract
Supplemental Instruction (SI) is a program that seeks to improve studentsuccess by targeting classes with high failure rates, as defined with a failurepercentage of 30% or more. It isorganized by an administrative SI supervisor who supervises SI leaders, whichare students that have successfully completed the courses that they have beenassigned. The SI supervisor alsocollaborates with the course instructors who aid in screening the competency ofthe SI leaders. Improvedself-confidence, teamwork, independence and course performance have beenreported as benefits of SI. This projectsought to explore the effect of SI on success and failure, along with gender,age and race. The type of course wasalso used as a factor in order to control for it as a confounding variable. In order to ascertain the effect of thesevariables on success, a technique called logistic regression was used. Caucasian female students who tookbacteriology and did not attend SI were used as the reference group. Students were about twice as likely tosucceed if they completed the required number of SI sessions and one fifth aslikely to succeed if they were in a SI class and did not meet the minimumnumber of sessions. Hispanic studentswere 40% as likely to succeed, and African American students were about onethird as likely to succeed when compared to Caucasian students. Students between20 and 29 years old were half as likely to succeed, and those 30 or older wereone quarter as likely to succeed when compared to teen students. Those in algebra were about three times morelikely to succeed than those in bacteriology, chemistry and statistics. When the students that withdrew were removed,the chances of success were about the same, except for African Americanstudents which were one quarter as likely to succeed, and those that did notmeet minimum sessions were one quarter as likely to succeed. The model explained more variation when thestudents that withdrew were included. AsSI had a strong influence on success, it should be considered as a tool toenable retention of students in high risk courses.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".