Strengthening Student Understanding Through Interactive Classroom Methods in Computer Science and Engineering
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it