Assessing Numerical Analysis Performance with the Practi Mobile App
Bibliographic record
Abstract
Numerical analysis is a unique combination of mathematical and computing skills. It facilitates a deeper understanding of data analytics and machine learning software libraries, which are exploding in use and importance. However, it is a topic that continues to challenge students because it requires a confluence of conceptual, procedural, and computational skills and associated pedagogies. Therefore, it is valuable to identify effective pedagogies and tools to enhance and assess student numerical analysis skills. Despite the proliferation of mobile technology in postsecondary education, its role in the context of numerical analysis is largely unknown. This quasi-experimental pilot study used Practi, an educational mobile app designed to assess numerical analysis performance and promote both retrieval practice and deliberate practice, which have been shown to help improve performance and develop expertise. Participants were 32 undergraduate students enrolled in a second-year introductory Numerical Analysis course at a large North American university. They were prompted to use Practi to solve quizzes on a regular basis throughout the course, before and after each lecture, to promote deliberate practice and spaced retrieval. Results of a paired t-test analysis showed that Practi was able to detect improvement in student quiz performance after the lectures compared to before the lectures. Additionally, performance on the Practi quizzes after the lectures was positively associated with the overall course performance. This suggests that mobile apps supporting deliberate and retrieval practice can complement more traditional means of instruction and assessment of numerical analysis in postsecondary mathematics education.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".