Developing a Model for Identifying Students at Risk of Failure in a First Year Accounting Unit
Bibliographic record
Abstract
This paper reports on the process involved in attempting to build a predictive model capable of indentifying students at risk of failure in a first year accounting unit in an Australian university. Identifying attributes that contribute to students being at risk can lead to the development of appropriate intervention strategies and support services. In this study, regression analysis was used to model the impact of individual factors on grade performance based on a review of the literature and using data extracted from a university’s student information database for all students who completed a first year accounting unit in one semester. The overall findings were that while the explanatory power of the model was poor, a number of variables were found to have a significant impact on performance. These variables included: younger students, males, those enrolled in non-business majors, and those with English as a second language. Further research in this area is warranted with the overall aim of reducing student failure and subsequent student attrition as well as developing appropriate intervention strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".