Consistency in Assessment of Pre-Engineering Skills
Bibliographic record
Abstract
Abstract Consistency in Assessment of Pre-Engineering SkillsAssessment tools are often used in a predictive way to gauge the overall skills of first-yearengineering students as they begin their engineering education. They are also useful in settinginterventions in terms of tutorials, as well as providing self- improvement motivation for thestudents who achieve scores that are not consistent with earlier high school performance.Previous research has demonstrated that the academic averages obtained in high school, may notnecessarily reflect the skill level (competency) of the students entering first-year, especially inmathematics. However, a longitudinal study over more than ten years has also indicated that theaverages from the math advisory and engineering assessment (Force Concept Inventory) examsdid not show a statistically significant decline during that time period. In this study, both themath and engineering assessment results were further analyzed on a per question basis todetermine whether or not there were any observable trends in the student responses.The results for math assessment exams, taken over thirteen years, indicated that the averageperformance on each question every year is statistically very consistent. The questions that themajority of the students got right each year, and those that the majority got wrong each yearshowed very little variation in the standard deviation (typically < 5%), which was used as themeasure in variability of the mean. The results were further analyzed by categorizing thequestions according to three classifications: algebra, trigonometry and geometry. Typically, thequestions with the best overall performance were simple algebra questions, and the questionswith the worst overall performance involved trigonometric concepts. Moreover, as thecomplexity of the algebra questions increased, the success rate on those questions diminished asexpected. Both assessment exams were time limited and students were not allowed to usecalculators. In the high school curriculum in our region, students use calculators regularly in theirhigh school math courses. As a result, their inherent competency in trigonometric functions islacking, as the average scores (typically less than 30%) on these questions would indicate.Engineering assessment (Force Concept Inventory) exam results collected over a slightly shorterduration (six years) were also analyzed. The same trends in student responses were observed, butin this case the results were somewhat less striking than the results obtained from the mathassessment. It is clear, however, that there is a consistency on the success rate for individualexam questions that test both math and engineering concepts. These results support the anecdotalcontention that students collectively have competency in certain areas (algebra) but lackcompetency in others (trigonometry). It further demonstrates that students often come into first-year engineering with common misconceptions and common math deficiencies.The results from this study are useful from several perspectives. They can provide a focus forinterventions that might address both competency and misconceptions. Secondly, the consistencyand repeatability of this data may provide an impetus to work with K-12 educators to addressthese issues before the students reach university. The consistency of this data also implies thatpre-engineering skills are somewhat predictable from year to year.
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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 | 0.027 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".