Predicting a Student's Success at a Post-Secondary Institution
Bibliographic record
Abstract
The primary objective of this study is to investigate the correlation between college math entry placement scores, required college math courses and the final program GPA of the students who have completed a diploma in a business or a technical program at an Ontario college (in Toronto, Canada) from 2000 to 2008. The correlation analysis did demonstrate a significant positive correlation between college math entry placement scores and final grades from the math courses of the students that have completed the program. A significant positive correlation was also demonstrated between the college math placement scores and the final program GPA of the same sample of students. All of these findings support the use of math placement tests to predict the success of students in a post-secondary environment. Introduction It is common practice to use standardize tests to set admission requirements for acceptance into a postsecondary program. Standardized tests have also been used within the post-secondary environment as knowledge assessments and predictors of college achievement. As knowledge assessments, these types of tests have found considerable success (Armstrong, 2000, McFate & Olmsted, 1999, Bunce & Hutchinson, 1993, Tai et. al, 2006, Russell, 1994) and unlike admission tests, the goals of these tests are to measure a student's academic ability and then properly stream students to courses to ensure academic success and improve institutional outcomes. Post-secondary institutions use standard placement tests that tend to focus on math and English assessment and are used to place the incoming student in an appropriate learning stream to promote student success at that institution. Susan Callahan (1993) presented a paper that concluded that math placement tests provide accurate course recommendations for incoming students and also showed that these placement tests assist with curriculum improvements within Cottey College in Nevada, Missouri. Roth, Crans, Carter, Ariet and Resnick (2000) did a similar study with a sample of almost 20,000 high school graduates in Florida and found that placement tests were beneficial to students who might have difficulties in college math courses and thereby providing the proper support to meet students' needs. This study agreed with the Callahan work (1993) and supported the ability to identify areas of an academic weakness or gaps in student knowledge; begin to stream the students early in their post-secondary career into the appropriate learning stream; and will ultimately improve student success at a post-secondary institution. In yet another study (Leopold and Edgar (2008)) a math test taken during the first week of the students' secondsemester introductory college chemistry class was used to predict success within the chemistry class and showed a high degree of direct correlation between the math tests scores and final chemistry grade. This work demonstrated mat math ability can be a predictor to other post-secondary non-math course grades. Routinely, post-secondary institutions administer entry-level placement tests that are critical to a student's academic options, high stakes and are used to give the students an appropriate context for the level of learning that is expected in the class. Armstrong's work (2000) supports the use of placement tests to support student learning outcomes, but he cautions the use of streaming of students based only on one measure. This message is echoed in McFate «Sc Olmsted's paper (1999) which suggests that institutions find a middle ground by using placement-test results only as an advising tool or including additional placement factors. Both studies demonstrate that placement tests support student outcomes, but that their results must be balanced with other factors that are part of the students' background that influences their success in a course. Previous work (Armstrong, 2000, Callahan, 1993, Bunce & Hutchinson, 1993, Tai et al, 2006, Russell, 1994, McFate & Olmsted, 1999) has demonstrated that placement testing can be beneficial to the students' learning, college outcomes and institutional retention numbers. …
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".