Retention Of First Year Students In Canadian Institutes Of Engineering And Technology: Affecting Factors And Solutions
Bibliographic record
Abstract
The freshman year is critical for both academic success and retention of students in engineering and technology programs.There has been argument of considering student completion rates as a fundamental measure of success of the student or the institution.But the drop out of a student after first year of education is considered overwhelmly by the education community as a terrible waste of human and financial resources.Also because of phenomenon growth, sweeping changes of technologies and the economic globalization it is rewarding to focus our whole hearted effort to retention.That is why, the author has identified the related most important factors such as student orientation and motivation, curriculum innovation and integration, underrepresented groups, human interface issues and employment opportunities.There must be a well-established coordination between the institution's responds for adjusting their programs and services and the today's students' expectation.First year seminar course that provides the basis for cohesive learning is useful.The author will demonstrate the effect of changing the sequence of courses on retention in electrical engineering technology program in a Canadian institution.The underrepresented groups specially the women whom represent nearly fifty percent of the population will be motivated to enroll and finish the program by understanding that the carriers in these fields are exciting, rewarding and accessible.Human interface issues such as active learning and teaming will be presented.The overall job prospects along with ever lasting demands in some special categories will be pointed out to the employment concern students.The goal of this study is to retain even one student out of the dropouts by individual institution.This modest achievement will not only make a difference in his/her life but Page 7.985.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".