An analysis of grades and placement in the transition from pre-calculus to calculus at the University of Manitoba
Bibliographic record
Abstract
Recently, first-year mathematics instructors at universities across North America and around the globe have been noticing a decline in the mathematics skills and preparation of the incoming student body, and these students have been failing out of first-year mathematics courses at alarming rates (Crowther, Thompson & Cullingford, 2007; Hourigan & O’Donaghue, 2007; Kajander & Lovric, 2005; Rylands & Coady, 2009). Though some universities have implemented placement or diagnostic tests to measure the preparedness of their incoming students, many still use high school grades as the only indicator of students’ readiness for university mathematics. However, researchers have found mixed results in terms of the effectiveness of high school grades at predicting success in university mathematics courses (Finnie et. al, 2010; Geiser, 2007; Salim & Al-Zarooni, 2009) due to factors such as the miss-alignment of teaching methods, students’ entering knowledge, and skills, the curricula in high school and university, and high school grade inflation. This dissertation includes two studies. The first study analyzed the relationship between grade 12 Pre-Calculus grades and first-year university Calculus grades at a large Canadian university over the period from 2001-2015. The results show that the disconnect between high school grades in mathematics and university grades in mathematics has been growing over time, and is significant for students who are not performing well in university Calculus. Recommendations include the implementation of placement examination, and increased communication and collaboration between K-12 and university mathematics educators and administrators. iii The second study looked at the effectiveness of the ALEKS placement test at predicting students’ readiness for first-year Calculus. Results show that including this mathematics placement test along with high school grades and some demographic variables can help predict the students’ success in Calculus more validly. Recommendations include the implementation of the ALEKS placement test for entry into university Calculus courses, as well as some additional measures that could aid in placing underrepresented minority students more appropriately. Furthermore, the sharing of information between university and high school mathematics teachers is recommended.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".