THE IMPORTANCE OF INCLUDING RECOGNITION OF PATTERNS ACTIVITIES IN LEARNING PROBLEM-SOLVING IN ENGINEERING CLASSROOMS
Bibliographic record
Abstract
"Why do we study limits?" "How would I calculate 7π/4 without a calculator?" "There are several trigonometric formulas, how can I memorize them?" These are a few of the frequent questions asked by engineering students who seek the help of the Learning Strategist, a professional who advises students on academic skills. Attempting to memorize formulas and problem solutions without understanding their origin is common among engineering students. Consequently, students often disregard or are unaware of how formulas are derived, and they do not allocate time to find patterns that connect these formulas to the concepts they are learning in class. Investing time to study the origin and assumptions underlying formulas can be rewarding yet this process has a steep learning curve. Once mastered, understanding the derivation of commonly used formulas and mathematical patterns saves students’ energy and time by giving them tools to quickly solve difficult engineering problems. In this paper, we demonstrate the process of problem-solving and pattern finding through a fun activity that can be utilized in lectures or tutorials to create in students an appreciation of the basics. The activity shows the importance of finding and understanding patterns and how to extend these findings into solutions. Through recognition of patterns, students can develop higher order thinking skills and the ability to derive formulas from their skeletal form. The goal of this project is to investigate the impact of instructors including pattern finding activities within their classrooms.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".