Strengthening Student Understanding Through Interactive Classroom Methods in Computer Science and Engineering
Bibliographic record
Abstract
Abstract This paper assesses the impact of various in-class instructional tools in post-secondary engineering curriculum. Various interactive methods were employed in university classrooms in Canada and the United States and analyzed to assess their effectiveness. These methods were evaluated to determine their efficacy in stimulating students, prompting critical thinking, and deepening overall understanding. The overall goal of each method is unique and the outcomes of implementing them in a classroom setting are presented in this paper. Student engagement and attendance was seen to increase as a result of iClicker use and the associated participation points. Additionally, Google forms were used to capture student responses of in-class practice of Boolean Algebra. Students found the forms to be helpful in comparing their responses with other students’ responses. The forms also helped the instructor gauge the class understanding by viewing the student response summary. This prompted the instructor to either explain the material in a different manner or move to another topic depending on the number of correct responses. The instructor could also identify the areas where students struggled the most. The third method, Immediate Feedback Assessment Technique, was used to solidify students’ understanding of test concepts, provide immediate feedback on whether they approached the concept correctly, and provide an opportunity to improve their grades. Overall, it was found that the interactive activities discussed in this paper increased engagement, information retention, critical thinking skills and overall learning experience of the engineering students.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".