Building lectures and building bridges with socio-economically disadvantaged students
Bibliographic record
Abstract
This paper is an empirical analysis of the first stage of an ongoing effort to introduce technology to enhance student learning in introductory corporate finance within a multi-campus and multi-mode regional Australian University. The engagement and performance of low socio-economic status (SES) students is of particular interest because approximately one-quarter of the university's enrolled students are classified low SES. A Tablet PC is used to facilitate a cooperative 'building' of each week's lecture in class and the recording of this process for delivery online. The analysis of the academic achievement of two cohorts of students in two different semesters-with the technology and without-forms the basis of the formal evaluation of the efficacy of the approach to date. The results indicate that there is a significant difference in retention (drop-out statistics) and academic achievement (examination performance, final grade and course progression statistics) between the 'Tablet PC' and 'control' semesters. The largest improvement was exhibited by the low SES students.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".